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11th IFAC International Symposium on
Advanced Control of Chemical Processes June 13-16, 2021
11th IFAC International Symposium on Advanced Control of Chemical Processes
June 13-16, 2021, Venice, Italy
Program at a Glance Sunday Monday Tuesday Wednesday Author Index Keyword Index
Last updated on June 20, 2021. This conference program is tentative and subject to change
Technical Program for Sunday June 13, 2021
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SuSAWS
Workshop: Machine Learning and Model Predictive Control Workshop
14:00-18:00, Paper SuSAWS.1
Workshop: Machine Learning and Model Predictive Control
Mesbah, Ali University of California, Berkeley
Paulson, Joel The Ohio State University
Keywords: Model-based Control
SuSAOS01
Batch Processes Regular Session
08:00-18:00, Paper SuSAOS01.1
Data-Driven Process Modeling and Optimization Aided by Material and Energy Balances: The Case of a Batch Polymerization Process
Bardooli, Ahmed 1993
Dong, Yachao Dalian University of Technology
Georgakis, Christos Tufts Univ
Keywords: Industrial Applications, Batch Processes, Modeling and Identification
Abstract: Our group recently defined two novel data-driven modeling methodologies: The Design of Dynamic Experiments (DoDE) and the Dynamic Response Surface Methodology (DRSM). These two methods enable the quick and efficient data-driven modeling of processes with a partial understanding of their inner workings. They generalize the Design of Experiments (DoE) and the Response Surfaces Methodology (RSM). DoDE allows time-varying inputs, and DRSM models time-varying process outputs. In this paper, we combine the above data-driven tools and partial knowledge of a batch polymerization process to develop an integrated data and knowledge-driven model. The optimization objective is to minimize the process's batch time while producing the same product quality, increasing productivity. The process knowledge incorporated into the model consists of material and energy balances in which we lack a quantitative description of the rate phenomena, such as reaction or mass/heat transfer rates. The optimization is evolutionary; initially, targeting small improvements through constrained extrapolations around the normal operating conditions. Then, we build the first models and use such models to design the next set of experiments that meet our specifications. This cycle of running experiments and updating the models is repeated until an optimum is reached. After three cycles, we succeeded in reducing the batch time by 26%, while producing an acceptable product.
Simple Model and Predictive Control of a Pharmaceutical Batch Fluidized Bed Dryer
Gagnon, Francis Université Laval
Desbiens, Andre Universite Laval
Poulin, Eric Laval University
Bouchard, Jocelyn Université Laval
Lapointe-Garant, Pierre-Philippe Pfizer
Keywords: Modeling and Identification, Model-based Control, Batch Processes
Abstract: The fluidized bed theory can be complex, especially for heterogeneous flow descriptions with bypasses, generally resulting in large differential and algebraic systems of equations. Consequently, their applicability to model-based process control is limited. In this work, a simplified homogeneous model for pharmaceutical batch drying is derived from the two-phase fluidization theory using physical insights and simplifying assumptions, reducing more than a hundred equations to five. A nonlinear model predictive controller with an internal model structure is designed from these simple equations, showing the simplicity of tuning and implementation. Parameters of the model are calibrated through nonlinear grey-box identification using pilot scale experimental data. The validation demonstrates that the proposed simplifications do not impair the ability to replicate the process dynamics adequately with experimental conditions similar to the ones used for calibration. Closed-loop results in simulation attest the robustness of this control strategy.
Dynamic Modeling of a Batch Sludge Settling Column by Partial Differential Non Linear Equations with a Moving Interface
Valentin, Claire UCB Lyon 1 Et CPE Lyon
Couenne, Francoise Univ Lyon, Université Claude Bernard Lyon 1
Jallut, Christian Université Claude Bernard Lyon 1
CHOUBERT, jean-Marc INRAE REVERSAAL
Tayakout-Fayolle, Mélaz Université Lyon 1 / LAGEPP
Keywords: Modeling and Identification, Batch Processes
Abstract: The further goal of the model presented in this paper is to propose a digital tool to predict the behaviour of a main equipment of the water resource recovery facilities, the secondary settling tank. Most of the time, models of this liquid/solid two-phase system are based on a dynamic solid phase mass balance and a settling velocity constitutive expression stemming from the Kynch'assumption. The model presented here is based on both dynamic mass and dynamic momentum balance equations and on an explicit representation of the sludge blanket depth. Therefore, it is able to be used in a wider range of operating conditions than existing models in the literature. It includes effective solid stress appearing when the solid particles concentration is above a given threshold and a moving interface above which there is no solid particles: the sludge blanket. A study of the main physical phenomena into a batch sludge sedimentation column (hydrodynamics of two-phase suspensions) is usually carried out before a faithful representation of continuous sedimentation within a continuous settling tank is deduced. The batch settler is divided into three zones (clarification, thickening and compression). A one dimensional implicit hybrid partial differential non linear equations (Hybrid NL-PDE's) state space representation is proposed based on the different dynamics in each of the three zones as well as the constitutive equations and the boundary conditions. Some simulations are given and compared with sludge blanket height measurements in a batch settling column.
Moment-Based Kalman Filter Design for Cell Population Balance Models in Batch Fermentation Processes
Jerono, Pascal Chair of Automatic Control, Kiel University
Schaum, Alexander Kiel University
Meurer, Thomas Christian-Albrechts-University Kiel
Keywords: Batch Processes, Big Data Analytics and Monitoring
Abstract: The observer design problem for the cell population estimation in a yeast batch process is addressed. The cell population balance model is described by a partial integro-differential equation coupled with a set of ordinary differential equations. Based on the observability property of the first moment of the cell distribution and the structural observability of the discretized cell population balance model, an extended Kalman Filter operating on on-line biomass measurements is designed for the model equations. The observer is validated using experimental data.
PIO Based Data-Driven Iterative Learning Control for Nonlinear Batch Processes with Nonrepetitive Disturbances Subject to Input Constraints
Hao, Shoulin Dalian University of Technology
Liu, Tao Dalian University of Technology (DLUT)
Paszke, Wojciech University of Zielona Gora
Keywords: Batch Processes
Abstract: This paper proposes a novel proportional-integral observer (PIO) based high-order data-driven iterative learning control (HODDILC) for nonlinear batch processes with nonrepetitive disturbances subject to input constraints. First, an equivalent dynamic linearization data model (DLDM) with an uncertainty term arising from the nonrepetitive disturbances is constructed in the batch direction. Based on the established DLDM along with a gradient estimation algorithm, a data-driven PIO is then designed to estimate the uncertainty term along the batch direction, followed by designing a HODDILC law in terms of the tracking errors over more than one previous batches and control inputs in the previous time instants of the current batch. Using the contraction mapping principle and matrix theory, rigorous convergence analysis is conducted in the iteration domain. A notable advantage of the proposed design is that only input and output measurement data are used rather than a process model. Finally, an illustrative example from the literature is given to demonstrate the effectiveness and merit of the proposed method.
Dual Mode Finite-Time Seeking Control for a Class of Unknown Dynamical System
Guay, Martin Queen's Univ
Keywords: Model-based Control, Scheduling and Optimization, Batch Processes
Abstract: In this study, we propose a dual-mode finite-time extremum seeking control system for the solution of real-time optimization problems for a class of unknown nonlinear dynamical systems. The technique provides fast convergence of the closed-loop to the optimum of a measured objective function in the absence of exact knowledge of the process dynamics. The finite-time approach provides a control system suitable for a large class of process control problems where exact models are not available. A simulation study is presented to demonstrate the effectiveness of the technique.
Data Analytics and Machine Learning I Regular Session
Adaptive Learning of Hybrid Models for Nonlinear Model Predictive Control of Distillation Columns
Lüthje, Jannik Torben RWTH Aachen University
Schulze, Jan Christoph RWTH Aachen University
Caspari, Adrian RWTH Aachen University
Mhamdi, Adel RWTH Aachen
Mitsos, Alexander RWTH Aachen University
Schäfer, Pascal Aachener Verfahrenstechnik - Process Systems Engineering, RWTH A
Keywords: Model-based Control, Modeling and Identification, Scheduling and Optimization
Abstract: Our previous work has shown that replacing parts of the classical compartmentalization model reduction approach for distillation columns by offline-trained artificial neural networks (ANNs)improves computational performance. In real-life applications, the absence of a high-fidelity model for data generation can, however, prevent the deployment of this approach. Therefore, we propose a method that utilizes solely plant measurement data, starting from a small initial data set and then continuously adapting to newly measured data. We demonstrate the approach in closed-loop simulations and compare to benchmarks using either the high-fidelity model or an offline trained reduced model for control.
Adsorption Material Composition and Process Optimization, a Systematical Approach Based on Deep Learning
Regufe, Maria João Laboratory of Separation and Reaction Engineering Associate Labo
Santana, Vinicius Department of Chemical Engineering School Polytechnic, Federal U
Martins, Márcio A. F. Universidade Federal Da Bahia
Ferreira, Alexandre Laboratory of Separation and Reaction Engineering Associate Labo
Loureiro, José M. Laboratory of Separation and Reaction Engineering Associate Labo
Alírio, Rodrigues Laboratory of Separation and Reaction Engineering Associate Labo
Ribeiro, Ana Mafalda Laboratory of Separation and Reaction Engineering Associate Labo
B. R. Nogueira, Idelfonso Laboratory of Separation and Reaction Engineering Associate Labo
Keywords: Scheduling and Optimization, Modeling and Identification
Abstract: The material screening is a preliminary step while designing an adsorption process. This step is carried out with a limited view of what concerns the material used. It usually focuses only on the materials' properties and not on their behavior while employed in the separation process. Furthermore, there is a lack of a systematic approach that uses an available materials database to identify the best material in a given process application. This leaves an open issue in the literature, which is getting attention with the advance of computer sciences. Hence, this work addresses this topic by proposing a systematic approach based on Deep Learning and a meta-heuristic optimization for simultaneous adsorbent screening and process optimization. As a case study, it is presented the carbon dioxide capture by Electric Swing Adsorption. The results show that the proposed methodology can identify the optimal material composition while providing the optimal process operating conditions.
Deep Transfer Learning Based Multisource Adaptation Fault Diagnosis Network for Industrial Processes
Chai, Zheng Zhejiang University
Zhao, Chunhui Zhejiang University
Keywords: Big Data Analytics and Monitoring, Industrial Applications
Abstract: In industrial processes, there are generally multiple data sources generated from different working conditions, which can provide different fault diagnosis knowledge to the target application. In this paper, a multisource adaptation diagnosis network (MADN) method is proposed to transfer the diagnostic knowledge existed in multiple sources to the target. First, a stacked-autoencoder based feature generator is pretrained to extract feature representations from the process data acquired from diverse working conditions. Then, domain discriminators are developed to reduce the distribution discrepancy between the target domain and each of the sources in an adversarial way. The domain discrimination ability, on the other hand, also reveals the different importance of the source domains. Thus, the fault classifiers can be assembled to identify the fault types of the unlabeled target data. The superiority of the proposed method is verified using a real-world process.
Decision Tree Analysis on the Performance of Zeolite-Based SCR Catalysts
Bae, Shin Young Seoul National University
Lee, Jong Min Seoul National University
Shin, Junseop Seoul National University
Kim, Yeonsoo Kwangwoon University
Keywords: Big Data Analytics and Monitoring, Modeling and Identification, Industrial Applications
Abstract: Urea-based selective catalytic reduction (SCR) is a promising method for removing NOx emissions. In the urea-based SCR method, zeolite-based catalysts are popularly used owing to their applicability over a wide range of temperatures compared to other catalysts. However, they still have several drawbacks such as inferior performance at low temperatures and thermal instability. This has subsequently led to numerous studies and experiments on the development of superior catalysts. While substantial experimental data exist on zeolite-based catalysts, extracting useful information from the data with a simple literature search is difficult owing to not only the amount of data but also complex correlations between features, including preparation variables such as doped-metal loading and operational variables such as reaction temperature. Recently, extracting insights from a large database has become possible by utilizing machine learning tools. Among them, the decision tree can extract insights on the synthesis of the catalysts as the results derived from the models are intuitive and easy to interpret, unlike those from conventional discriminant machine learning models. In this study, classification models are obtained by training decision tree models with literature data on zeolite-based urea SCR catalysts, particularly the Beta and ZSM-5 types, and several experimental heuristics are extracted from the derived models.
Developing Soft-Sensor Models Using Latent Dynamic Variational Autoencoders
Lee, Yi Shan Chung Yuan Christian University
Ooi, Sai Kit Chung Yuan Christian University
Tanny, Dave Chung Yuan Christian University
Chen, Junghui Chung-Yuan Christian Univ
Abstract: Quality variables, which are usually measured offline, play important roles in describing process behaviors. However, online data obtained from soft sensors are significant as they provide accurate and immediate information. The reliability of online soft sensors is questionable due to changes in sensors, equipment, raw material availability, and operation conditions. In addition, chemical plants have dynamic properties and complex correlations amidst a large number of process variables. This causes most of the predictions obtained from steady-state soft sensors to be inaccurate in representing the particular chemical process. In this paper, the latent dynamic variational autoencoder is proposed to provide an estimation model and supervise soft-sensors. The input data are encoded in the latent space to remove underlying noises and disturbances in the data. Afterward, the dynamical properties are learned in the latent space through the bi-directional recurrent neural network, whose output (latent variable) is used to reconstruct back the input data. A simulation case study is conducted to show the effectiveness of the proposed method.
Multisource Heterogeneous Data Fusion for Toxin Level Quantification
Strelet, Eugeniu University of Coimbra
Wang, Zhenyu Dow Inc
PENG, YOU Dow
Castillo, Ivan The Dow Chemical Company
Rendall, Ricardo Dow
Braun, Birgit Dow
Joswiak, Mark Dow
Chiang, Leo The Dow Chemical Company
Seabra dos Reis, Marco P. University of Coimbra
Abstract: The operational management of wastewater treatment plants (WWTP) is a complex activity due to the biological phenomena' intricate nature. This complexity hinders the adoption of first principles approaches, which lack the necessary accuracy to be adopted in practice. Data-driven methodologies also face significant challenges in processing the different information sources available. In this work, we present a data-driven and model-agnostic data-fusion framework to estimate the concentration level of a toxin in the effluent, using heterogeneous data (sensor data, images, laboratory measurements) collected at different locations in the process. Single- and multi-source modeling approaches are applied and compared. Among the methodologies tested, Bayesian fusion stands out as presenting a good balance in terms of accuracy, stability, and flexibility.
Data Analytics and Machine Learning II Regular Session
Improving Autoencoder Training with Novel Goal Functions Based on Multivariable Control Concepts
Martello, Rafael Henrique Federal University of Rio Grande Do Sul - UFRGS
Ranzan, Lucas UFRGS
Farenzena, Marcelo Federal University of Rio Grande Do Sul
Trierweiler, Jorge Otávio Federal University of Rio Grande Do Sul
Keywords: Modeling and Identification, Big Data Analytics and Monitoring, Industrial Applications
Abstract: Autoencoders are becoming more representative in all fields of knowledge, due to their ability to classify, compress, and identify data patterns. This study objective was to propose entirely new objective functions using multivariable process control concepts as the gain matrix and Relative Gain Array to improve the quality of prediction and classification of an autoencoder. The advantages of the proposed approach are illustrated through a pulp-and-paper industry. The new function results show an improvement in the detection, leading to savings of up to 22 to 38 thousand dollars per month compared to a model using only MSE.
A Design Centering Methodology for Probabilistic Design Space
Kusumo, Kennedy Putra Imperial College London
Morrissey, Richard Imperial College London
Mitchell, Hamish Imperial College London
Shah, Nilay Imperial College London
Chachuat, Benoit Imperial College London
Keywords: Modeling and Identification, Industrial Applications, Batch Processes
Abstract: The use of mathematical models for design space characterization has become commonplace in pharmaceutical quality-by-design, providing a systematic risk-based approach to assurance of quality. This paper presents a methodology to complement sampling algorithms by computing the largest box inscribed within a given probabilistic design space at a desired reliability level. Such an encoding of the samples yields an operational envelope that can be conveniently communicated to process operators as independent ranges in process parameters. The first step involves training a feed-forward multi-layer perceptron as a surrogate of the sampled probability map. This surrogate is then embedded into a design centering problem, formulated as a semi-infinite program and solved using a cutting-plane algorithm. Effectiveness and computational tractability are demonstrated on the case study of a batch reactor with two critical process parameters.
Anticipated Cell Lines Selection in Bioprocess Scale-Up through Machine Learning on Metabolomics Dynamics
Barberi, Gianmarco University of Padova
Benedetti, Antonio Process Engineering & Analytics, GlaxoSmithKline R&D
Diaz-Fernandez, Paloma Biopharm Process Research, GlaxoSmithKline R&D
Finka, Gary Biopharm Process Research, GlaxoSmithKline R&D
Bezzo, Fabrizio Università of Padova
Barolo, Massimiliano University of Padova
Facco, Pierantonio University of Padova
Keywords: Batch Processes, Industrial Applications, Big Data Analytics and Monitoring
Abstract: The development of biopharmaceutical therapeutics, such as monoclonal antibodies, requires the testing of several cell lines at different development scales and the selection of the high performing cell lines which allow meeting the desired quality attributes of the product. In this context, data analytics, which is extremely useful for a better process understanding and a faster scale-up, can be used to understand the relation between biological information, such as cell metabolism, and process productivity. This study shows that monoclonal antibodies end-point titer can be estimated in the early stages of the industrial product development for cell line selection using information on cell metabolism dynamics. This allows the anticipated identification of the high-performing cell lines, and a better understanding of the relationships between the time evolution of both the metabolic information and the product titer.
Estimation-Based Predictive Control of Nonlinear Processes Using Recurrent Neural Networks
S. Alhajeri, Mohammed University of California, Los Angeles
Wu, Zhe University of California, Los Angeles
Rincon, David University of California, Los Angeles
Albalawi, Fahad KAUST
Christofides, Panagiotis D. University of California at Los Angeles
Keywords: Model-based Control, Modeling and Identification, Big Data Analytics and Monitoring
Abstract: Machine learning techniques have demonstrated their capability in capturing dynamic behavior of complex, nonlinear chemical processes from operational data. As full state measurements may be unavailable in chemical plants, this work integrates recurrent neural networks (RNN) within extended Luenberger observers to develop data-based state estimators. Then, an output feedback model predictive controller is designed based on state estimates provided by the RNN- based estimator to stabilize the closed-loop system at the steady-state. A chemical process example is utilized to illustrate the effectiveness of the proposed state estimation approach.
Learning Physics Based Models of Li-Ion Batteries
Best Mckay, Maricela University of British Columbia
Wetton, Brian University of British Columbia
Gopaluni, Bhushan University of British Columbia
Keywords: Big Data Analytics and Monitoring, Energy Processes, Modeling and Identification
Abstract: Lithium-ion (Li-ion) batteries are increasingly pervasive and important in daily life. We present a surrogate modeling approach that uses synthetic data generated by an electrochemical model to approximate Li-ion battery dynamics using a Deep Neural Network. Elechtrochemical models are needed to describe high current operation but are computationally costly. As an initial study, we prototype our approach for the Single Particle Model. We use a battery use-cycle and observations of concentrations and voltages to predict future battery behavior. Given only the use cycle and knowledge that the battery is fully charged, the surrogate model can accurately forecast observations of Li-ion concentrations and voltages for the entire use cycle, as well as give a window of time for battery depletion.
Energy Processes Control and Optimization Regular Session
A Reduced-Order Model for Real-Time NMPC of Ethanol Steam Reformers
Reyero-Santiago, Pedro Universitat Politècnica De Catalunya
Yu, Xinwei University of Alberta
Ocampo-Martinez, Carlos Universitat Politecnica De Catalunya (UPC)
Braatz, Richard D. Massachusetts Institute of Technology
Keywords: Energy Processes, Modeling and Identification
Abstract: One of the approaches to green energy of interest in recent years is through the use of hydrogen technologies in which the waste product of combustion is water rather than carbon dioxide, nitrous oxides, and other pollutants generated by the burning of hydrocarbon fuels. Ethanol steam reformers is one of the primary technologies with the potential to generate hydrogen efficiently and reliably. This article investigates the design of nonlinear optimal feedback control systems for ethanol steam reformers based on mechanistic models described by a system of nonlinear partial differential-algebraic equations, aka singular/descriptor systems. The mechanistic models are too complicated to simulate in real-time using the standard numerical discretization methods, much less to directly incorporate such models as nonlinear constraints into mechanistic model-based nonlinear model predictive control formulations. This article employs a rather sophisticated multivariate form of the method of characteristics to reformulate the nonlinear constraints into a much simpler form amenable to real-time control implementations.
H2 Purity Control for High-Pressure Alkaline Electrolyzers
David, Martín Instituto Tecnológico De Buenos Aires
Bianchi, Fernando Universitat Politecnica De Catalunya
Sánchez-Peña, Ricardo Instituto Tecnologico De Buenos Aires (ITBA)
Keywords: Model-based Control, Energy Processes
Abstract: This paper proposes a control strategy that mitigates the cross contamination of H2 and O2 in a high-pressure alkaline electrolyzer, which consequently increases the supplied gases purity. In order to reduce the diffusion of gases through the membrane, the controller establishes the opening of two outlet valves based on the pressure of the system and the difference in liquid level between both separation chambers. Therefore, a multiple input - multiple output optimal controller is designed here. For this purpose, a high-fidelity model previously developed was simplified in order to obtain a control-oriented model. The proposed controller was evaluated in simulation using the high-fidelity nonlinear model in a wide operating range and was compared with a pair of decoupled PI controllers. The impurity of gases was below 1% in all cases.
Analysis of Optimal Control Strategies for Efficient Operation of a Produced Water Reinjection Facility for Mature Fields
Fonseca Ivo, Otávio Norwegian University of Science and Technology
Imsland, Lars Norwegian University of Science and Technology
Keywords: Scheduling and Optimization, Energy Processes, Industrial Applications
Abstract: Produced water management policies in oil and gas industry have been transitioning towards zero emission, no ocean discharge and waste-to-value conversion. Produced water reinjection plays a key role in achieving these goals for mature fields as it derives value from produced water and is able to maintain environmental integrity. A central element in this, is optimal operation of pumping. Hydraulic efficiency and specific energy are generally the chosen indices used to evaluate operational energy efficiency in pumping systems. In this work, we investigate optimization strategies based on natural objective function candidates for a produced water reinjection system: (i) operation at best efficiency point; (ii) minimization of total shaft input power; (iii) maximization of overall pump hydraulic efficiency; (iv) minimization of total specific energy and (v) minimization of total delivered energy. In addition, we discuss the implication of the different solutions over control structure selection.
Non-Intrusive Time Galerkin POD for Optimal Control of a Fixed-Bed Reactor for CO2 Methanation
Bremer, Jens Max-Planck-Institute for Dynamics of Complex Technical Systems
Heiland, Jan Max Planck Institute for Dynamics of Complex Technical Systems
Benner, Peter Max Planck Institute for Dynamics of Complex Technical Systems
Sundmacher, Kai Max Planck Institute for Dynamics of Complex Technical Systems
Keywords: Scheduling and Optimization, Energy Processes, Model-based Control
Abstract: The optimization of a controlled process in a simulation without access to the model itself is a common scenario and very relevant to many chemical engineering applications. A general approach is to apply a black-box optimization algorithm to a parameterized control scheme. The success then depends on the quality of the parametrization that should be low-dimensional though rich enough to express the salient features. This work proposes using solution snapshots to extract dominant modes of the temporal dynamics of a process and use them for low-dimensional parametrizations of control functions. The approach is called proper orthogonal decomposition in time (time-POD). We provide theoretical reasoning and illustrate the performance for the optimal control of a methanation reactor.
Efficient Utilization of Active Carbon in a Blast Furnace through a Black-Box Model-Based Optimizing Control Scheme
Azadi, Pourya Process Dynamics and Operations Group, Department of Biochemical
Klock, Rainer Thyssenkrupp Steel Europe AG
Engell, Sebastian TU Dortmund
Keywords: Model-based Control, Industrial Applications
Abstract: The daily operation of blast furnaces in the steel industry is only partly automated. The thermal control of the process is yet carried out manually by the operators. Their decisions may lead to an oversupply of carbon-based fuels, causing surplus production of carbon monoxide. The unexploited excess of carbon monoxide in the iron oxide reduction reactions increases the total carbon supply, hence the cost and the CO2 emissions. To maximize the carbon monoxide efficiency in the reduction reactions, the authors propose a dynamic optimizing control scheme and evaluate its performance by simulation studies using real operational data. The optimizer adjusts the fast dynamics of the blast furnace to prevent the inefficiency of the utilization of carbon monoxide that is influenced by the slow dynamics, subject to process productivity and safety constraints. Simulation results demonstrate that the control scheme can lead to the full conversion of the reduction reactions as well as a reduction of the total carbon supply.
Industrial Applications I Regular Session
Continuous Integrated Filtration, Washing and Drying of Aspirin: Digital Design of a Novel Intensified Unit
Destro, Francesco University of Padova
Coleman, Simon Alconbury Weston Ltd
Firth, Paul Alconbury Weston Ltd
Barton, Alastair Alconbury Weston Ltd
Nagy, Zoltan K. Loughborough Univ
Keywords: Modeling and Identification
Abstract: Within the recent modernization of pharmaceutical manufacturing, an important milestone consists in developing enabling technologies for end-to-end continuous production of drug products. Continuous filtration, washing and drying of active pharmaceutical ingredients from mother liquors in upstream manufacturing are critical steps for achieving end-to-end continuous production. In this work, we develop a mathematical model for a novel intensified carousel system capable of continuously filtering, washing and drying a slurry stream into a dry crystals cake. The mathematical model consists of detailed dynamic differential mass, energy and momentum balances, capable of tracking the solvents and impurities content in the cake (critical quality attributes) across the entire carousel. We successfully demonstrate the model features by identifying the probabilistic design space in a simulation study for the isolation of aspirin crystals in the carousel, accounting for model uncertainty through Monte Carlo simulations.
Identification and Online Updating of Dynamic Models for Demand Response of an Industrial Air Separation Unit
Tsay, Calvin The University of Texas at Austin
Cao, Yanan Smart Operations, Center of Excellence (COE), Linde
Wang, Yajun Carnegie Mellon University
Flores-Cerrillo, Jesus Linde
Baldea, Michael The University of Texas at Austin
Keywords: Industrial Applications, Scheduling and Optimization, Modeling and Identification
Abstract: Demand-response operation of air separation units often involves frequent changes in process setpoints (e.g., production rate), and therefore process dynamics should be considered in scheduling calculations to ensure feasibility. To this end, scale-bridging models (SBMs) approximate the scheduling-relevant dynamics of a closed-loop process in a low-order representation. In contrast to previous works that have employed nonlinear SBMs, this paper proposes linear SBMs, developed using time-series analysis, to facilitate online scheduling computations. Using a year-long industrial dataset, we find that compact linear SBMs are suitable approximations over typical scheduling horizons, but that their accuracies are unpredictable over time. We introduce a strategy for online updating of the SBMs, based on Kalman filtering schemes for online parameter estimation. The approach greatly improves the accuracy of SBM predictions and will enable the use of linear SBM-based demand-response scheduling in the future.
Application of the IFSH Methodology for Plantwide Control for an Evaporator Benchmark Process
Pedersen, Morten Lysdahlgaard Technical University of Denmark
Bähner, Franz David Technical University of Denmark
Huusom, Jakob Kjøbsted Technical University of Denmark
Keywords: Scheduling and Optimization, Model-based Control, Industrial Applications
Abstract: Plantwide control (PWC) is an inherently complex task, which is why systematic guidelines and step-wise procedures are necessary to identify proper control pairings. Several different methodologies for the development of PWC structures have been proposed over the years, trying to balance the field between formal methods, practicality, and heuristics. The Integrated Framework of Simulation and Heuristics (IFSH) by Konda et al. (2005) utilizes simulators to support the heuristic decisions, thereby reducing the dependence of experience and prior understanding of the process behavior. In this article, the framework is successfully applied to the Forced Circulation Evaporator process by Newell and Lee (1989). A step-wise application of the methodology provides the basis for a discussion of how the methodology benefits from the utilization of simulations and how it differs from previously developed control structures for this evaporator process.
Multi-Channel Graph Convolutional Network Based End-Point Element Composition Prediction of Converter Steelmaking
Chang, Shuchao Zhejiang University
Li, Yuanlong Alibaba Group
Zhou, Min Alibaba Group
Fu, Chuan Alibaba Group
Qiao, Honglin Alibaba Group
Keywords: Industrial Applications
Abstract: End-point composition is an important quality standard for the converter steelmaking process, which consists of multiple elements, including C, Si, Mn, etc. However, it is hard to measure the element composition online. Real-time and precise prediction for element composition is essential for the optimization of alloy addition so as to bring economic profits. Nevertheless, most conventional models neglect the correlations among element compositions and predict each element composition without the information from other elements. In this paper, a new multi-channel graph convolutional network is proposed to integrate these correlations with the process variables together for a more accurate prediction model. The proposed model uses graph structure to describe the correlations among element compositions. Specifically, through the multi-channel design, each element composition can be learned based on process variables in an independent channel. Element compositions and correlations among them are respectively described by nodes and edges in graph. With the constructed graph, the graph convolution across channels can fuse the features of correlated elements to explicitly exploit the correlation information for performance improvement. Besides, compared with conventional methods which learn relations among nodes based on distances, we take sparse representation learned by sparse coding as edges to describe the correlations among nodes. As strong correlations exist among element compositions, the consideration of correlation information can integrate the learning of correlated elements and bring performance improvement. Experiments based on the real converter steelmaking process demonstrate the superiority and effectiveness of the proposed model.
A Time-Dependent Model of Pressure Drop in Reverse Osmosis Spiral Wound Membrane Modules
Ruiz-García, Alejandro University of Las Palmas De Gran Canaria
de la Nuez, I. Campus Universitario Tafira, ULPGC
Keywords: Modeling and Identification, Industrial Applications
Abstract: Reverse osmosis (RO) is the leading technology in desalination for both, brackish and seawater. Modeling RO process is a complex task as there are a high dependence on the operating conditions and feedwater characteristics. One of the main drawbacks of this technology is the performance decay due to fouling impact. Usually, fouling make the pressure drop to increase. This increase has not been reflected in the proposed models in RO process. The aim of this work was to proposed a time-dependent model for the pressure drop in RO process. For this purposes long-term experimental data of two full-scale brackish water RO desalination plants with two stages each were used. The first desalination plant was operating along 11 years in continuous regime while the second desalination plant was in operation during 14 years in intermittent regime. In the first desalination plant, the pressure drop ranges were 75-250 kPa and 25-150 kPa in the first and second stage respectively while in the second desalination plant were 50-200 kPa and 20-125 kPa. The behavior of the experimental data in both cases was close to an exponential function. The mentioned function was fitted in time domain using the standard deviation as error function.
An Automatic System for Modeling and Controlling Color Quality of Dyed Leathers in Tanneries
Bacci di Capaci, Riccardo University of Pisa
Pannocchia, Gabriele University of Pisa
Kenny, Colm Kentec S.r.l
Pacchi, Giacomo BCN Concerie S.p.A
Lupi, Roberto BCN Concerie S.p.A
SCALI, Claudio University of Pisa
Keywords: Industrial Applications, Model-based Control, Modeling and Identification
Abstract: This paper presents an automatic system for modeling and controlling color quality of dyed leathers of an Italian tannery. The proposed software is implemented within the IT company system, and is fully integrated with the machineries of the finishing line, that is, a spraying cabin with a robotic carousel, and an electronic tintometer system. Suitable experimental tests according to the Design of Experiments (DoE) are firstly defined, executed and analyzed for a series of color tones of interest. In order to derive and validate a set of colorimetric models able to evaluate and predict the color rendering of painted leathers, a set of recipes of basic dye pigments and data of light reflection measured by a multispectral camera are used. Principal Component Analysis is applied for dimensionality reduction, and linear least squares regression is employed to identify these data-driven models, which are then used for color control purpose. A color correction feedback strategy is indeed developed in order to converge towards the various target formulations. The control algorithm aims at reaching the multispectral reading values of the reference, that is, the first sample of unknown color recipe starting from the most similar archive base and appropriately updating the recipe of pigments, by using the measurement of leather samples prepared from time to time by the finishing line machineries. A set of company data are used to successfully validate the identified colorimetric models and the proposed color correction strategy.
Industrial Applications II Regular Session
Sensor Fusion and Computer Vision Integrated System for Primary Separation Vessel Interface Level Estimation
Wang, Jingyi University of Alberta
Alipouri, Yousef University of Alberta
Huang, Biao Univ. of Alberta
Keywords: Industrial Applications, Modeling and Identification
Abstract: In oil sands industry, primary separation vessel (PSV) is a critical component to recover bitumen from oil sands slurry. Accurate interface level estimation between froth and middlings layers ensures economical and environmental effectiveness of bitumen recovery. Nuclear density profiler, differential pressure (DP) cell, and image processing based computer vision system are usually used to estimate this interface level. The computer vision system, which uses a camera to capture sight glass vision frames, is considered to be the most accurate. Although the accuracy of computer vision system is high, its qualities are influenced by abnormalities, such as sight glass vision blocking, stains, and level transition between sight glasses. A sensor fusion approach, which recursively updates fusion parameters using high-accuracy computer vision results and provides reliable alternative interface level estimation under abnormal scenarios, is proposed and integrated with computer vision system to improve froth-middlings interface level estimation accuracy and robustness. Industrial environment simulations and factory accepted test (FAT) demonstrate the advantages and effectiveness of the sensor fusion and computer vision integrated system, which has been applied in the industry.
An Adaptive Stabilizing Model Predictive Control Strategy Applied to an ESP-Lifted Oil Well System
Santana, Bruno Federal University of Bahia
Maia Fontes, Raony Universidade Federal Da Bahia
Schnitman, Leizer Federal University of Bahia
Abstract: This work presents the benefits of using an adaptive model predictive control approach for controlling an ESP-lifted oil well system. The idea behind the proposed scheme is the successive linearization of the plant nonlinear model and incorporate it into a stabilizing infinite-horizon MPC formulation that handles feasibility issues by considering slacked terminal constraints. Suitable use of a zone control strategy to deal with time-varying ESP operating envelope constraints (downthrust and upthrust) becomes the approach implementable in practice. Nonlinear plant-model mismatch scenarios, including simulation of unmeasured disturbances and the tracking of ESP power consumption minimization, show the effectiveness of the controller proposed here concerning different operating conditions of the oil production equipped with ESP.
Control of a Natural Gas Liquids Recovery Plants in a GSP Unit under Feed and Composition Disturbances
Mandis, Marta Università Degli Studi Di Cagliari
Chebeir, Jorge Department of Chemical Engineering, Louisiana State University
Tronci, Stefania Università Degli Studi Di Cagliari
Baratti, Roberto Universita' Degli Studi Di Cagliari
Romagnoli, Jose Louisianna State University
Abstract: Recent technological improvements have driven the rapid increase in natural gas production from unconventional reservoirs. The heaviest hydrocarbon fraction of this fossil fuel, the so-called natural gas liquids (NGL), have greater economic interest justifying the attention on its separation process from the raw gas. Various process schemes have been developed and studied for the NGL recovery, including the conventional, cold residue recycle (CRR), and the gas subcooled process (GSP). This study aims to assess different control strategies for a GSP unit and determine the most appropriate and effective process control scheme. For this, the dynamic responses for each control scheme are evaluated by changing feed flow rate and composition. The main targets are the achievement of 84% ethane recovery and low levels of methane impurity at the bottom of the demethanizer column. Due to the high cost of composition analyzers and the high delays introduced by composition controllers under the presence of flow disturbances, the control goals are reached by the knowledge of on-line temperature measurements. This is done by considering different temperature control structures such as the direct temperature control and cascade control, plus a pressure compensator. The results are compared, in presence of composition disturbances, with the action of a hybrid cascade control that uses in-line delayed concentration measurements to update the controller reference at each sampling period. Here, the hybrid and the simple cascade controls show the best control performance.
Online Wear Detection for Joints in Progressing Cavity Pumps
Müller, Jens Ruhr-Universität Bochum
Leonow, Sebastian Ruhr University Bochum
Schulz, Johannes SEEPEX GmbH
Hansen, Christian SEEPEX
Monnigmann, Martin Ruhr-Universität Bochum
Keywords: Industrial Applications, Modeling and Identification, Big Data Analytics and Monitoring
Abstract: The wear and failure of pin joints integrated in coupling rods of process equipment can lead to unpredictable breakdown with severe consequences. We present a simple algorithm for a non-invasive online detection of wear in pin joints where a progressing cavity pump serves as an example. The algorithm only requires a pressure sensor and a binary speed signal of the motor. We verify the algorithm with a laboratory test setup and an embedded system. The results show that the proposed algorithm can reliably monitor joint wear during regular pump operation.
Anti-Windup Predictive Control for an Engine Air Path System
Mizumoto, Ikuro Kumamoto Univ
Makimoto, Yusuke Kumamoto University
Abstract: This paper deals with the control problem of an engine air path system with the air mass flow and EGR rate as the outputs and throttle valve and EGR valve as inputs. In the considered system, since the EGR response is slow compared with the response of the intake air mass, the overshoot and undershoot phenomena occur in fresh air mass flow during transient response. Moreover, the mechanical restriction of the valves opening range results in instability of the control system. In this paper, we propose an anti-windup predictive control taking into inputs restriction for air path system to improve transient performance and to maintain the stable air path system. The effectiveness of the proposed method will be confirmed through numerical simulations.
Predictive Control of Activated Sludge Plants to Supply Nitrogen for Optimal Crop Growth
Bezerra Leite Neto, Otacílio Federal University of Ceará
Haddon, Antoine INRAE
AICHOUCHE, Farouk INRA
Harmand, Jérome INRA
Mulas, Michela Federal University of Ceara
Corona, Francesco Aalto University
Abstract: We report the preliminary results of a feasibility study in which we investigate the possibility to operate a common class of activated sludge plants to produce effluent wastewater of varying quality for crop irrigation. Firstly, a nitrogen reference trajectory is computed as solution to a higher-level optimal control problem that aims at maximizing plant biomass in a crop growth system. We then study how to control the treatment plant with a zero-offset predictive control strategy designed to operate the treatment process to supply nitrogen according to this optimal planning for crop growth. We show how an ad hoc tuning of the predictive controller allows to define alternative policies that favour the manipulation of different forms of nitrogen in the treatment plant. We show that the designed controllers are only partially capable to operate the treatment plant to meet the nitrogen demand, when subjected to typical municipal wastewater influent conditions. However, zero-offset can be achieved under constant influent conditions. We analyse the performance of the controller in terms of tracking accuracy and operational energy.
Model Predictive Control I Regular Session
Dual Adaptive Model Predictive Control with Disturbances
Cai, Zicheng Carnegie Mellon University
Ydstie, B. Erik Carnegie Mellon
Keywords: Model-based Control, Modeling and Identification
Abstract: Model-based control requires good accuracy of model parameters to achieve high performance. Controller design under parametric uncertainties is therefore a challenging topic in control system engineering. One well known control design for uncertain systems is adaptive control. An adaptive controller has two tasks: process regulation and parameter learning. Dual control explores the trade-off between the two seemingly conflicting tasks. The control structure in an adaptive system consists of a model-based controller and a recursive update rule for parameter estimation. In this paper, the adaptive control framework consists of model predictive control for system regulation and recursive least squares for parameter estimation. The requirement for persistent excitation is shown to be necessary for systems with high-dimensional parameter space. Then, a dual formulation is proposed in an attempt to generate excitation signals while maintaining control performance in the adaptive control scheme. The algorithm is implemented and tested on a simulated SISO system.
Adaptive Tube-Enhanced Multi-Stage Nonlinear Model Predictive Control
Abdelsalam, Yehia Technical University of Dortmund (TU Dortmund)
Subramanian, Sankaranarayanan TU Dortmund
Aboelnour, Mohamed Technische Universität Dortmund
Abstract: A robust adaptive controller for nonlinear plants with parametric uncertainties, additive disturbances, and state estimation errors based on the tube-enhanced multi-stage (TEMS) nonlinear model predictive (NMPC) framework is proposed. In TEMS NMPC, primary multi-stage NMPC is used to achieve robustness against the uncertainties which have a large effect on the evolution of the state of the plant, and ancillary multi-stage NMPC is used to track the predictions of the primary controller to counteract the effect of the small uncertainties. We propose updating, at each time step, the uncertainty set considered by the scenario trees of the primary and ancillary controllers with a tighter non-falsified uncertainty set, which results from solving a guaranteed parameter estimation (GPE) optimization problem. This produces significant performance improvements over the non-adaptive implementation as will be shown on the Williams-Otto continuous stirred tank reactor (CSTR) case study.
A Robust Model Predictive Controller Applied to a Pressure Swing Adsorption Process: An Analysis Based on a Linear Model Mismatch
Marrocos, Paulo Laboratory of Separation and Reaction Engineering Associate Labo
Abstract: The present work studies the control problem of a pressure swing adsorption (PSA) unit using a robustly stabilizing model predictive control (RIHMPC) strategy with guarantee of feasibility for realistic mismatch scenarios. A procedure to identify multi-plant linear models is also proposed. This procedure is based on an optimal point given by an optimization layer, concomitantly with the uncertainty associated with that point. As a case study, it was evaluated the control of a PSA unit for the purification of syngas by porous amino-functionalized titanium terephthalate MIL-125 in the PSA. The results demonstrated that RIHMPC might be an efficient strategy to address the control of cyclic adsorption processes accommodating the intrinsic nonlinearities and uncertainties of these processes.
Application of Model Predictive Control to the Reactive Extrusion of ɛ-Caprolactone in a Twin-Screw Extruder
Cegla, Maximilian TU Dortmund
Abstract: In this work the control of the reactive extrusion of ɛ-Caprolactone in a twin-screw extruder using nonlinear model predictive control with a tracking objective is investigated. For this, the modeling of the extrusion process using a one-dimensional mechanistic model is presented and extended to reactive extrusion systems. A novel modeling approach describing the pressure as a differential state is proposed to be able to use efficient optimization methods. Results for two scenarios, a change of the product quality and a change of the throughput are shown. The tuning of the controller is discussed and the benefits over traditional control methods are elaborated.
Control of Particle Formation Processes
Otto, Eric Otto Von Guericke University Magdeburg
Behrens, Jessica Otto Von Guericke University Magdeburg
Dürr, Robert Max Planck Institute for Dynamics of Complex Technical Systems
Palis, Stefan University Magdeburg
Kienle, Achim University Magdeburg
Abstract: Discrepancy-based control (DBC) represents an elegant direct control design conceptfor infinite-dimensional systems, which is frequently used to control particle formation processes,an important class of operations in chemical engineering. So far, DBC has been applied tocontinuous layering growth and agglomeration, which have been accounted for by partialdifferential equations. In this contribution we develop control laws for infinite systems of ordinarydifferential equations, inspired by DBC. The results are visualized by the application to acontinuous agglomeration process involving additional particle breakage. We show how a two-dimensional controller is able to control the industrially relevant Sauter mean diameter of theproduct particles, resulting in improved performance of the closed-loop system compared to asingle control loop.
Robust Economic Model Predictive Control with Zone Control
Decardi-Nelson, Benjamin University of Alberta
Liu, Jinfeng University of Alberta
Abstract: This paper presents a robust economic Model Predictive Control (EMPC) formulation for discrete-time uncertain nonlinear systems. The proposed controller not only ensures that the closed-loop system is robust to disturbances, but also ensures that the economic performance does not deteriorate in the presence of the disturbances. The key idea is to have the controller track a robust control invariant subset of the state space with specified economic properties at all times, and within the zone optimize the process economics. To this end, we introduce the notion of risk factor in the controller design and provide an algorithm to determine the economic zone to be tracked. The risk factor determines the conservativeness of the controller. Our proposed controller is computationally less demanding as it only makes use of the system model without disturbances. A nonlinear CSTR example is presented to demonstrate the performance of the proposed formulation.
Model Predictive Control II Regular Session
A Data-Driven Automatic Tuning Method for MPC under Uncertainty Using Constrained Bayesian Optimization
Sorourifar, Farshud Ohio State University
Makrygiorgos, Georgios University of California, Berkeley
Keywords: Model-based Control, Modeling and Identification, Industrial Applications
Abstract: The closed-loop performance of model predictive controllers (MPCs) is sensitive to the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints. In this work, we demonstrate a general approach for automating the tuning of MPC under uncertainty. In particular, we formulate the automated tuning problem as a constrained black-box optimization problem that can be tackled with derivative-free optimization. We rely on a constrained variant of Bayesian optimization (BO) to solve the MPC tuning problem that can directly handle noisy and expensive-to-evaluate functions. The benefits of the proposed automated tuning approach are demonstrated on a benchmark continuously stirred tank reactor example.
Observer-Based Distributed Sensor Fault-Tolerant Model Predictive Control for Intermittent Faults
Xiao, Guannan Jiangnan University
Luan, Xiaoli Jiangnan University
Liu, Fei Jiangnan University
Keywords: Model-based Control, Scheduling and Optimization
Abstract: For distributed control systems with intermittent sensor faults, a distributed fault and state combined observer is designed, and an observer switching mode strategy is proposed to improve the observer convergence performance, then a distributed fault-tolerant model predictive is designed based on the observer. In the proposed observer, the distributed system observer matrices, and state and fault estimation are computed in a totally distributed way with some transition matrices, which can consider the coupled states. The switching mode observer can be more targeted. The two modes: state only estimation, both state and fault estimation are switched due to the absence or presence of the sensor faults. The approach is shown effective through a simulated example.
Development of Non-Cooperative Distributed Adaptive MPC Schemes Based on ARX Models Parameterized Using Orthonormal Basis Filters
Sailo, Sairengpuia Indian Institute of Technology Bombay
Kumar, Kunal IIT Bombay
Patwardhan, Sachin C. Indian Institute of Technology Bombay
Nataraj, P.S.V. Indian Inst. of Tech
Abstract: Adaptive model predictive control (AMPC) schemes can prove beneficial in the control of non-linear time-varying systems due to their ability to maintain closed-loop performance in the face of large transitions in operating conditions. In this work, two non-cooperative distributed adaptive MPC (dAMPC) schemes are proposed that are based on ARX models parameterized using generalized orthonormal basis filters (GOBF). Poles of the GOBF models estimated through offline estimation are kept fixed and only the Fourier coefficients are updated recursively online thereby avoiding drift in the pole locations. The system under consideration is decomposed into a number of subsystems coupled through the inputs. A separate local AMPC controller is implemented for each subsystem. Each local controller optimizes its input trajectories assuming the input trajectories of its neighbors fixed to the last computed values, and then shares the computed inputs with the neighbors. We consider both sequential and iterative distributed control strategies for the dAMPC development. The efficacy of the proposed dAMPC schemes is demonstrated using simulation studies on an octuple tank process. When the system is subjected to large transitions in operating points, the proposed dAMPC schemes generate closed-loop performances comparable to a centralized adaptive MPC scheme while significantly reducing the average computation time.
A Distributed Active Set Method for Model Predictive Control
Stomberg, Gösta TU Dortmund University
Engelmann, Alexander TU Dortmund University
Faulwasser, Timm TU Dortmund University
Abstract: This paper presents a novel distributed active set method for model predictive control of linear systems. The method combines a primal active set strategy with a decentralized conjugate gradient method to solve convex quadratic programs. An advantage of the proposed method compared to existing distributed model predictive algorithms is the primal feasibility of the iterates. Numerical results show that the proposed method can compete with the alternating direction method of multipliers in terms of communication requirements for a chain of masses example.
Advanced-Multi-Step Moving Horizon Estimation
Lin, Kuan-Han Carnegie Mellon University
Thierry, David Carnegie Mellon University
Biegler, Lorenz T. Carnegie Mellon Univ
Keywords: Modeling and Identification, Model-based Control, Scheduling and Optimization
Abstract: Moving Horizon Estimation (MHE) is an important optimization-based approach for state estimation and parameter updates, because of its capabilities in dealing with nonlinearity and state constraints. In addition, one of the applications is to provide the full state information for Model Predictive Controller (MPC) to control the process in either setpoint tracking or economic control purposes. However, the computational burden of MHE could deteriorate the control performance if the feedback delay caused by computation is too long, leading to potential safety issues or process damage. In this paper, we propose a fast moving horizon estimation algorithm to overcome the long computational time of MHE for real-time control applications, especially for fast dynamics or large-scale systems. We exploit the nonlinear programming (NLP) sensitivity and make use of efficient NLP solvers, IPOPT and k_aug, to reduce the on-line computational costs. This new approach is demonstrated on a CSTR process, where results are compared to ideal MHE and advanced-step MHE (asMHE).
Application of Data-Driven Economic NMPC on a Gas Lifted Well Network
Andersen, Joakim Rostrup Norwegian University of Science and Technology
Abstract: The Daily Production Optimization (DPO) problem is the task of maximizing production of hydrocarbons subject to operational constraints. Handling of uncertainty in model structure and parameters is of high importance to the usefulness of the solution. Ignoring these challenges will, most likely, render the solution either infeasible or the solution will not be an optimum of the plant. We suggest to apply a data-driven methodology to use state- and output-measurements from the plant to iteratively update the Optimal Control Problem (OCP) which are used to control the plant. The goal of the method is to tune the OCP such that the solution will go towards an optimum of the plant as the parameters are being updated. A Reinforcement Learning updating technique is used to update the optimization formulation.
Modeling and Control of Biosystems I Regular Session
Mixing Strategies Combined with Shape Design to Enhance Productivity of a Raceway Pond
Bernard, Olivier INRIA
Lu, Liu-Di Sorbonne Universite & Inria
Salomon, Julien Laboratoire Jacques-Louis Lions
Keywords: Scheduling and Optimization, Industrial Applications, Model-based Control
Abstract: This paper focuses on mixing strategies and designing shape of the bottom topographies to enhance the growth of the microalgae in raceway ponds. A physical-biological coupled model is used to describe the growth of the algae. A simple model of a mixing device such as a paddle wheel is also considered. The complete process model was then included in an optimization problem associated with the maximization of the biomass production. The results show that non-trivial topographies can be coupled with some specific mixing strategies to improve the microalgal productivity.
Compartmental Glycosylation Flux Analysis
Aggarwal, Shilpi University at Buffalo
Qin, Xi University at Buffalo
Neelamegham, Sriram State University of New York, Buffalo
Gunawan, Rudiyanto University at Buffalo
Abstract: N-linked glycosylation is a critical quality attribute of therapeutic monoclonal antibodies (mAbs) – an important class of biologics. In this work, we developed compartmental Glycosylation Flux Analysis (cGFA) to enable a more accurate representation of the cell's Golgi apparatus (GA), where protein glycosylation takes place, in the analysis of intracellular fluxes of glycosylation reactions. The application of one-compartment and two-compartment cGFA to a Chinese hamster ovary cell culture production of immunoglobulin G demonstrated the insufficiency of modeling the GA as one well-mixed compartment. The two-compartment cGFA was able to, not only fit the data significantly better than the one-compartment cGFA, but also accurately predict the localization of enzyme in the GA cisternae. Information on glycosylation alterations during cell cultivation, as provided by the cGFA, may lead to a better understanding of how different cell culture parameters control the N-linked glycosylation which would enable a more precise glycoengineering of therapeutic mAbs.
Latent Variables Model Based MPC for People with Type 1 Diabetes
Sun, Xiaoyu Illinois Institute of Technology
Rashid, Mudassir Illinois Institute of Technology
Askari, Mohammad Reza 1990
Cinar, Ali Illinois Inst. of Tech
Abstract: A model predictive control (MPC) system based on latent variables (LV) model generated by using partial least squares (PLS) method is developed. The difference in the performance of MPCs that use recursively updated LV models based on autoregressive time series modeling (with exogenous inputs - ARX) and PLS is studied. The effect of signal noise on MPC performance is also investigated for both types of models. MPC performance is evaluated by regulating the blood glucose concentration (BGC) of people with Type 1 diabetes mellitus (T1DM) in simulation studies. Signal noise in glucose concentration sensor data, delays caused by insulin absorption and action, and disturbances caused by consumption of meals make the regulation of BGC difficult. The proposed controller is evaluated with 10 in-silico adult subjects of the UVa/Padova simulator with different levels of signal noise. The results illustrate the effectiveness of the MPC based on LV model. The average time for BGC in the safe range (70-180 mg/dL) for the LV-based MPC is 83.23% compared to 79.68% for the MPC based on ARX model when intravenous BGC values are used. The average time in safe range decreases to 76.04% and 71.92%, respectively, when using the generic CGM sensor of the simulator. It is reduced further to 71.93% and 67.20% when additional noise is added to CGM readings.
DISCOPOLIS 2.0: A New Recursive Version of the Algorithm for Uniform Sampling of Metabolic Flux Distributions with Linear Programming
Bogaerts, Philippe Université Libre De Bruxelles
Rooman, Marianne Université Libre De Bruxelles
Abstract: Metabolic flux values are subject to equality (e.g., mass balances, measured fluxes) and inequality (e.g., upper and lower flux bounds) constraints. The system is generally underdetermined, i.e. with more unknown fluxes than equations, and all the admissible solutions belong to a convex polytope. Sampling that polytope allows subsequently computing marginal distributions for each metabolic flux. We propose a new version of the DISCOPOLIS algorithm (DIscrete Sampling of COnvex POlytopes via Linear program Iterative Sequences) that provides the same weight to all the samples and that approximates a uniform distribution thanks to a recursive loop that computes variable numbers (called grid points) of samples depending on the fluxes that have already been fixed in former iterations. The method is illustrated on three different case studies (with 3, 95 and 1054 fluxes) and shows interesting results in terms of flux distribution convergence and large ranges of the marginal flux distributions. Three consistent criteria are proposed to choose the optimal maximum number of grid points.
Different Control Strategies for a Yeast Fermentation Bioreactor
Lisci, Silvia Università Degli Studi Di Cagliari
Grosso, Massimiliano Università Degli Studi Di Cagliari
Abstract: Biological systems are usually highly sensitive to process conditions variations, such as temperature, pH, substrate concentration. For this reason, it is important to adequately control and monitor the process in order to guaranteeing product quality while maintaining adequate performance and productivity. The production of ethanol by fermentation is certainly one of the most important industrial bioprocesses, being ethanol an alternative source of energy. For this reason, valuable models of this process based on different kinetic considerations are available in literature, and they can be considered a valid benchmark to investigate control system and estimation techniques for biological reactors. Three different control strategies have been analysed: direct reactor temperature control, cascade control where the primary loop uses delayed ethanol measurements, and 2x2 control system with inferential control for the product concentration. The proposed configurations have been compared at different operating conditions and results show that the use of the inferential control is the most effective in case of severe disturbances.
Modelling for Control of Biodiesel Microreactors
Ajeni, Michael The University of Manchester
Heath, William Paul University of Manchester
Poupard, Eduardo Festo SE & Co.KG
Keywords: Modeling and Identification, Industrial Applications, Model-based Control
Abstract: The modelling of biodiesel production through transesterification in chemical reactors has largely been studied, but the control of this process in microreactors is not straightforward without a suitable process model. In this paper, a useful mathematical model for control is proposed. The model suitably represents the dynamics of biodiesel properties in a microreactor. By analysing the physical influences of convection, diffusion and chemical kinetics on biodiesel yield, an explicit relationship between feed flows and mixing ratios with biodiesel output concentration is captured for control. The model responses are analysed and compared against a variety of experimental results in existing literature at similar operating conditions. The proposed model shows appropriate responses and opens the possibilities of more accurate model-based control applications such as Model Predictive Control and Internal Model Control.
Modeling and Control of Biosystems II Regular Session
Optimal Feed Trajectories for Fedbatch Fermentation with Substrate Inhibition Kinetics
Ryde, Thomas Emil Technical University of Denmark
Wahlgreen, Morten Ryberg Technical University of Denmark
Nielsen, Marcus Krogh Technical University of Denmark
Hørsholt, Steen Technical University of Denmark
Jørgensen, Sten Bay Technical University of Denmark
Jorgensen, John Bagterp Technical University of Denmark
Keywords: Scheduling and Optimization, Model-based Control, Batch Processes
Abstract: We provide optimal feed trajectories for fedbatch fermentation of microorganisms with substrate inhibition kinetics. We demonstrate that the optimal trajectories are non-unique and provide analytical procedures for solving the optimal control problem. Since, the optimal trajectories are non-unique this is essential for practical operations as a numerical solution could be any of the feasible non-unique optimal solutions. In addition, we investigate the sensitivity of the optimal trajectories and demonstrate that a bang-bang solution is optimal from a sensitivity point of view. This insight is relevant for model predictive control of fedbatch fermentation.
A Combined Computational-Fluid-Dynamics Model and Control Strategies for Perfusion Bioreactor Systems in Tissue Engineering
Nascu, Ioana East China University of Science and Technology
Sebastia-Saez, Daniel University of Surrey
Chen, Tao University of Surrey
Du, Wenli East China Univ of Science and Technology
Abstract: This work sets the foundations for the design of control algorithms to facilitate manufacturing of a cell growth process using a continuous perfusion bioreactor. The algorithms are designed to work with different types of cell cultures and deal with major disturbances that might appear in the process. Different types of control strategies are designed, implemented and tested. First, a comprehensive mathematical model of convection and diffusion in a perfusion bioreactor, combined with cell growth kinetics, is developed and implemented using Computational Fluid Dynamics. The model describes the spatio-temporal evolution of glucose concentration and cell density within a 3D polymeric scaffold. Since such a model is too complex to be used directly for control studies, a simplified version is used for the design of the controllers. Finally, the performances of the control strategies are validated against the original high-fidelity CFD model, thus closing the loop. The simulations show good performances and satisfactory behavior.
Critical Quality Predictive Control of Fed-Batch Mammalian Cell Bioreactors
Jackson, Robert Illinois Institute of Technology
Parulekar, Satish Illinois Institute of Technology, Chicago
Keywords: Model-based Control, Batch Processes, Modeling and Identification
Abstract: A model predictive control (MPC) formulation for a mammalian cell fed-batch bioreactor processes is developed. A nonlinear fundamental model for the bioreactor is used to generate a database of historical runs comprising of the measurement variables and the manipulated input feed flow rate to the bioreactor. The database is used with subspace identification methods to develop a state-space model of the process. The identified model is used to design various MPC formulations with different objective criteria, including the conventional trajectory-tracking objective function and a novel terminal objective for maximizing the product yield at completion of a run. Case studies involving the simulated bioreactor process demonstrate the efficacy of the MPC algorithms subject to unknown disturbances, random variations in the inlet feed glucose and glutamine concentrations, and measurement noise. Compared to the traditional proportional-integral control algorithm, the trajectory-tracking predictive control algorithm is able to better track the reference glucose concentration set-point with an improvement of 5.1% in the tracking error. The critical quality attribute predictive control algorithm designed to maximize the product yield results in a 3.9% increase in the product concentration at the completion of the run.
Maximum-Likelihood Extremum Seeking Control of Microalgae Cultures
Dewasme, Laurent Université De Mons
Vande Wouwer, Alain Université De Mons
Feudjio Letchindjio, Christian Gabin University of Mons
Ahmad, Afaq TU Dortmund
Keywords: Batch Processes, Scheduling and Optimization, Industrial Applications
Abstract: This paper proposes a model-free extremum seeking control (ESC) approach to optimize the productivity of continuous cultures of microalgae, considering the dilution rate and the light intensity as manipulated variables, and the biomass concentration as single measurement. The resulting two-input single-output optimization problem is first solved using a recursive least-squares strategy based on the representation of the process by a Hammerstein block-oriented model. In order to face the presence of noise on the regressor variables (input and output signals), the problem is then reformulated as a maximum-likelihood estimation problem, which is solved on a moving horizon. Simulation results demonstrate the method performance.
On the Dynamics and Robustness of the Chemostat with Multiplicative Noise
Alvarez, Jesus Universidad Autonoma Metropolitana
Abstract: The stochastic dynamics of a two-state bioreactor model with random feed flow fluctuations and non-monotonic specific growth rate is analyzed. Using the Fokker-Planck equation approach for describing the probability density function (PDF) evolution the lack of stochastic robustness due to deterministic bifurcation phenomena for the open-loop reactor operating under optimal (maximum production) operation condition is established, and the associated stochastic stabilization problem is addressed. Inherent differences between the presence of multiplicative noise, due to the feed flow fluctuations, and additive background noise are analytically established. Numerical simulation results illustrate these inherent differences, the stochastic fragility of the open-loop operation yielding a stochastic extinction phenomenon, as well as the stochastic PDF stabilization with a proportional feedback control.
Experimental Application of Individual Column State and Parameter Estimation in SMB Processes to an Amino Acid Separation
Gerlich, Stefanie TU Dortmund
Arab, Hind TU Dortmund
Buchholz, Malte TU Dortmund
Keywords: Modeling and Identification, Big Data Analytics and Monitoring
Abstract: The simulated moving bed (SMB) process is a highly efficient continuous chromatographic separation process. Due to its hybrid process dynamics that lead to discontinuities and sharp fronts on the state trajectories, optimal SMB process operation is challenging. Process performance can be improved by applying model-based optimizing control methods. For this, online information about states and individual column parameters are required. The strategy for simultaneous state and parameter estimation used here exploits the switching nature of the SMB process. The successful experimental application of the strategy is demonstrated for the continuous separation of two amino acids on an SMB pilot plant where extra-column equipment effects need to be considered.
Modeling and Identification I Regular Session
An Adaptive Filter for Parameter Estimation of Damped Sinusoidal Signals
Jahvani, Mohammad Queen's University
Keywords: Modeling and Identification, Model-based Control, Big Data Analytics and Monitoring
Abstract: In this paper, we introduce a continuous-time adaptive algorithm for online estimation of the parameters of exponentially damped sinusoidal signals. The proposed algorithm can also reconstruct the original signal from its corrupted measurements in real-time and without extra computational cost. For sufficiently slow adaptation gains, we establish the local stability of the filter and local exponential convergence of the estimated parameters to their true values. We also obtain a linear approximation of the adaptation laws which can be utilized for tuning the adaptation gains. Simulation studies are also provided to corroborate our claims.
Analytical Derivation and Comparison of Alarm Similarity Measures
Kargaran, Amir Hossein Isfahan University of Technology
Neshastegaran, Amir Isfahan University of Technology
Izadi, Iman Isfahan University of Technology
Yazdian, Ehsan Isfahan University of Technology
Abstract: An industrial process includes many devices, variables, and sub-processes that are physically or electronically interconnected. These interconnections imply some level of correlation between different process variables. Since most of the alarms in a process plant are defined on process variables, alarms are also correlated. However, this can be a nuisance to operators, for one fault might trigger a, sometimes large, number of alarms. So, it is essential to find and correct correlated alarms. In this paper, we study different methods and techniques proposed to measure correlation or similarity between alarms. The similarity indices are first analytically calculated and then studied and compared. The results are also validated using Monte-Carlo simulation.
Generalized System Identication for Nonlinear MPC of Highly Nonlinear MIMO Systems
Chee, Ewan National University of Singapore
Wang, Xiaonan National University of Singapore
Keywords: Modeling and Identification, Model-based Control, Industrial Applications
Abstract: Advanced model-based control strategies like Model Predictive Control can offer superior control of key process variables for multiple-input multiple-output systems. The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization. Mechanistic models might be difficult to construct or might evaluate too slowly for fast-sampling purposes, while System Identification methods that yield linear models might only offer effective control within a limited range. This work thus articulates an integrated System Identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for Nonlinear Model Predictive Control. In this piece, seven candidate models were trained on data sets generated from two different methods and integrated into a Nonlinear Model Predictive Controller for a highly nonlinear Continuous Stirred Tank Reactor system. This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges, given that the data set is sufficiently uniform across the operating range. These results were subsequently built upon in a follow-up work. This demonstration of how such system models could be identified for Nonlinear Model Predictive Control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which, in the face of process uncertainties or modeling limitations, allow rapid and stable control over wider operating ranges.
Koopman-Based Data-Driven Control for Continuous Fluidized Bed Spray Granulation
Maksakov, Anton Otto-Von-Guericke University Magdeburg
Keywords: Modeling and Identification, Model-based Control
Abstract: Continuous fluidized bed spray granulation processes are known to be prone to non-linear oscillations, which requires a stabilizing controller design. In this article, a purely data-driven Koopman-based system linearization and control is proposed, which does not need any prior model knowledge and uses only systems input-output measurements. The linearization achieved by the Koopman embedding allows for linear-quadratic control design, which does not depend on the operating point. The proposed method was validated in a simulation study of a fluidized bed granulation process and was able to stabilize the particle size distribution in different operating points.
Mathematical Modelling of Rheological Properties of Low-Density Polyethylene Produced in High-Pressure Tubular Reactors
Dietrich, Maira Planta Piloto De Ingeniería Química – PLAPIQUI (Universidad Naci
Brandolin, Adriana Planta Piloto De Ingeniería Química – PLAPIQUI (Universidad Naci
Sarmoria, Claudia Planta Piloto De Ingeniería Química – PLAPIQUI (Universidad Naci
Asteasuain, Mariano Planta Piloto De Ingeniería Química – PLAPIQUI (Universidad Naci
Keywords: Industrial Applications, Modeling and Identification, Model-based Control
Abstract: Low-density polyethylene (LDPE) is a commodity polymer widely used in a variety of applications. Due to the complexity of its molecular structure, which includes molecular weight distribution and randomly distributed long-chain branching, its rheological behavior is varied. In the present work, a mathematical model is used to predict molecular and rheological properties of LDPE produced in a high-pressure tubular under different operating conditions. The agreement between flow curve predictions and experimental data is very good, demonstrating the potential of this model as a predictive tool for industrial producers.
Fast Dynamic Simulation of Distillation Columns with Local Thermodynamic Models and Adomian Decomposition
Fernandes, Pedro UFRGS, Porto Alegre
Abstract: Dynamic simulation of first-principles distillation models is a very time-consuming task, yet it is necessary for monitoring, control and optimization of such processes. In this sense, this work proposes a discretized Adomian decomposition method coupled with the Local Thermodynamic Model technique in order to speed up the integration of dynamic responses for distillation columns with acceptable accuracy loss. Results show that the combination of both approximation methods can lead to at least 150x faster simulations than a reference solution with equivalent model characteristics.
Modeling and Identification II Regular Session
Identifiability and Physical Interpretability of Hybrid, Gray-Box Models - a Case Study
Hotvedt, Mathilde Norwegian University of Science and Technology
Grimstad, Bjarne ITK, NTNU
Keywords: Modeling and Identification, Industrial Applications, Energy Processes
Abstract: Model identifiability concerns the uniqueness of uncertain model parameters to be estimated from available process data and is often thought of as a prerequisite for the physical interpretability of a model. Nevertheless, model identifiability may be challenging to obtain in practice due to both stochastic and deterministic uncertainties, e.g. low data variability, noisy measurements, erroneous model structure, and stochasticity and locality of the optimization algorithm. For gray-box, hybrid models, model identifiability is rarely obtainable due to a high number of parameters. We illustrate through an industrial case study - modeling of a production choke valve in a petroleum well - that physical interpretability may be preserved even for non-identifiable models with adequate parameter regularization in the estimation problem. To this end, in a real industrial scenario, it may be beneficial for the model's predictive performance to develop hybrid over mechanistic models, as the model flexibility is higher. Modeling of six petroleum wells on the asset Edvard Grieg using historical production data show a 35% reduction in the median prediction error across the wells comparing a hybrid to a mechanistic model. On the other hand, both the predictive performance and physical interpretability of the developed models are influenced by the available data. The findings encourage research into online learning and other hybrid model variants to improve the results.
Application of SOS-Constrained Regression to Model Unknown Reaction Kinetics
Pitarch, Jose Luis Universidad De Valladolid
Montes, Daniel Alberto Universidad De Valladolid
de Prada, Cesar Univ. of Valladolid
Sala, Antonio Universitat Politecnica De Valencia
Abstract: The key idea of the fourth industrial revolution is to use the huge amount of data from the increased process digitalisation in order to make better decisions at all levels: from the design and control, to operation and management. However, advanced decision support systems usually rely on good plant models. Despite the increased popularity of machine learning, in the process industry many of these approaches may fail in building reliable prediction models: that is, models whose output can be trusted even out of the region where actual data was collected. This paper illustrates how to get a reliable grey-box model of a chemical plant for optimisation purposes via sum-of-squares (SOS) constrained regression, a method that guarantees full enforcement of physical features on the identified model, no matter the quality and quantity of the collected data. The approach is used here to identify a reliable model for the reaction kinetics in a hybrid CSTR, a pilot plant where the chemical reactions are emulated over a harmless fluid.
Time-Delay Identification and Validation of a Liquid Film Transport Model Based on Pilot Plant Experiments
Hofmann, Julian Karlsruhe Institute of Technology
Ponomarev, Anton Saint Petersburg State University
Hagenmeyer, Veit Karlsruhe Institute of Technology
Gröll, Lutz KIT
Abstract: The paper presents an identification strategy to obtain relations between time-delays of a transport model for an evaporating liquid film and corresponding flow/liquid properties. To this end, step-response based pilot plant experiments referring to the falling film evaporator process are conducted. In this context, the time-delay relations are identified offline by minimizing the output error between the model and experiment. In subsequent validation experiments, we observe that modeled and measured outputs are in good accordance. Within this framework, we conclude that the influence of evaporation on the liquid's time-delay behavior can be neglected.
Modeling of Milk Acidification
Robles Rodriguez, Carlos Eduardo TU Eindhoven
Szymanska, Ewa FrieslandCampina
Ozkan, Leyla Technical University of Eindhoven
Abstract: This paper deals with the development of an empirical model to describe the dynamics of milk acidification process for the production of acid casein. The model is based on the dynamics of the pH profiles during the acidification process since pH is a good indicator of the status of the precipitation. Data from laboratory experiments has been used to identify the parameters in the proposed model. Calibration and validation results, with an independent data set, show that the model is able to predict accurately the pH at different temperatures and acid addition rates. Furthermore, the model has been used in a simulation study as an advisory tool to suggest acid addition, proving the parsimony of the model.
Local Parameter Identifiability of Large-Scale Nonlinear Models Based on the Output Sensitivity Covariance Matrix
Mendez-Blanco, Carlos Samuel Eindhoven University of Technology
Keywords: Modeling and Identification, Batch Processes, Industrial Applications
Abstract: The use of first-principle models is motivated by the large amount of detailed information available as well as their versatility. Therefore, it is important to keep these models up to date so the models represent accurately enough the processes at hand. However, most of these models are nonlinear with a large number of states and parameters but with a relatively low number of measured outputs. This lack of measurements hinders the possibility to estimate all of the parameters present in the model. In this work, parameter identifiability of large-scale nonlinear models is explored using the empirical output controllability covariance matrix approach. This empirical covariance matrix is used to extract the output sensitivity matrix of the model to assess parameter identifiability. The advantages of the proposed methods are discussed while different sensitivity indexes are evaluated to draw sound conclusions on the identifiability results. A large-scale reactive batch distillation process simulation is used as a demonstrator.
Process Monitoring Regular Session
Long-Term Dependency Slow Feature Analysis for Dynamic Process Monitoring
Gao, Xinrui Technical University of Ilmenau
Shardt, Yuri A.W. Technical University of Ilmenau
Abstract: Industrial processes are large scale, highly complex systems. The complex flow of mass and energy, as well as the compensation effects of closed-loop control systems, cause significance cross-correlation and autocorrelation between process variables. To operate the process systems stably and efficiently, it is crucial to uncover the inherent characteristics of both variance structure and dynamic relationship. Long-term dependency slow feature analysis (LTSFA) is proposed in this paper to overcome the Markov assumption of the original slow feature analysis to understand the long-term dynamics of processes, based on which a monitoring procedure is designed. A simulation example and the Tennessee Eastman process benchmark are studied to show the performance of LTSFA. The proposed method can better extract the system dynamics and monitor the process variations using fewer slow features.
Filtering and Smoothing of Hidden Monotonic Trends and Application to Fouling Detection
Chiplunkar, Ranjith University of Alberta
Abstract: In this paper, we present a filtering and smoothing scheme for process variables characterized by a hidden monotonic trend. The proposed method models the transition probability distribution of the hidden monotonic trend as a closed skew normal distribution and the observed data is assumed to have a Gaussian noise added onto this monotonic trend. The objective is to extract the monotonic trend given the noisy observations. The proposed method has advantages in process monitoring applications involving processes driven by a monotonic trend where vanilla Kalman filter may not be the apt option. The proposed method has been verified on an industrial dataset of a hot lime softener process to detect the fouling buildup.
Moment-Based Dissipative Observer Design for Cell Population Balance Models
Moreno, Jaime A. Universidad Nacional Autonoma De Mexico-UNAM
Keywords: Model-based Control, Big Data Analytics and Monitoring, Modeling and Identification
Abstract: A dissipativity-based observer design for a class of cell population balance models is presented and evaluated using experimental data. The population balance model is described by a partial integro-differential equation coupled with an ordinary differential equation for a batch bioreactor with biomass measurement via an optical density sensor. The dissipative observer is proven to exponentially converge in the first moment of the cell size distribution and the substrate concentration in the absence of modeling and measurement errors. The theoretical results are evaluated using experimental data from a 2 liter lab-scale reactor for anaerobic yeast fermentation on glucose using nitrogen gas supply, showing a good performance of the dissipativity-based estimation scheme.
Sparse Adjacency Forecasting and Its Application to Efficient Root Cause Diagnosis of Process Faults
Song, Pengyu Zhejiang University
Abstract: Efficient fault root cause diagnosis is essential to ensure the production safety of industrial processes. The existing root cause diagnosis models can be summarized as linear methods and nonlinear methods. Linear methods cannot handle nonlinear processes well, while nonlinear methods usually require pairwise calculations between variables, which are complex and difficult to apply in real time. To address the above issues, a method for root cause diagnosis of nonlinear processes, termed sparse adjacency forecasting (SAF), is proposed in this paper. SAF is a causal inference method based on the idea of Granger causality. While forecasting time series, it constructs an adjacency matrix to synthesize the process information and the interaction of different variables. By adding sparse constraints to the adjacency matrix, the predictive effects between variables are reflected, and the causality is captured. This method only needs to model once to obtain the causal relationship between all variables, which avoids multiple modeling and improves diagnosis efficiency. Besides, in order to solve the nonlinear problem, multiple nonlinear random feature nodes are introduced for time series prediction. Two cases are adopted to verify the causal inference and root cause diagnosis performance of the proposed method, including a numerical case and the Tennessee Eastman benchmark process.
Condition-Driven Soft Transition Modeling and Monitoring Strategy for Complex Nonstationary Process
Chen, Xu Zhejiang University
Keywords: Big Data Analytics and Monitoring
Abstract: Due to the frequent switching of operation condition, the real industrial processes show typical nonstationary characteristics. In some cases, the switching is frequent and may not be instantaneous, revealing typical transition characteristics different from steady operation. In this work, a condition-driven soft transition modeling and monitoring method is proposed to deal with this problem. The condition modes are obtained by an automatic sequential condition-mode division algorithm, then a fine-grained mode recognition strategy is developed to further separate the condition mode into steady and transition submodes. The steady submode model is established by designing a conditional autoencoder network which can more closely describe each steady submode and facilitate evaluation of the relationship between transition submode and each steady submode. Finally, an online monitoring strategy is designed which can capture the nonstationary process changes. A real industrial case illustrates the effectiveness and superiority of the proposed method, which establishes a more accurate model for nonstationary processes by revealing the transition feature.
A Nonstationary Process Monitoring Based on Mutual Information among Process Variables
Cheng Ji, Cheng Beijing University of Chemical Technology
WANG, JINGDE Beijing University of Chemical Engineering
Sun, Wei Beijing University of Chemical Technology
Abstract: In practical chemical industrial processes, the feed valve is automatically adjusted in response to the control systems and the production load will be adjusted with market situation and administrative regulations. Therefore, process data display nonstationary statistics in practical operation condition and cannot satisfy the ideal assumptions of traditional multivariate statistical methods that process is assumed operating around one preset steady state. Under normal operating conditions, fluctuations or adjustments will only affect the mean and standard deviation of process variables, but the correlation among process variables should follow its inherent mechanism model, whose feature can be statistically captured within certain range. In this paper, a nonstationary process monitoring based on mutual information among process variables is proposed. The Euclidean distance (ED) of eigenvalues of the mutual information matrix under normal operation conditions is calculated to obtain a statistic. Once a fault occurs, the changes in correlation among process variables will be reflected in the mutual information matrix and corresponding ED will exceed the threshold, by which process monitoring can be implemented. A numerical simulation example and a practical cracking process are applied as case studies. The results show a better performance on monitoring nonstationary process than traditional principle component analysis method.
Real-Time Optimization Regular Session
MTX-LAB Controlled by Multi-SISO PID Controllers
de Souza, Fernanda Bernardi Federal University of Rio Grande Do Sul
Barreiros, Brício Ferreira Federal University of Rio Grande Do Sul
Aldrighi Silveira, Lucas UFRGS
Muniz, Bruna Souza Universidade Federal Do Rio Grande Do Sul
Abstract: The MTX-LAB plant is a didactic laboratory setup based on humidity and temperature sensors for testing control algorithms. The objective is to reproduce an industrial problem with a classroom lab plant. The plant shows multivariable characteristics consisting of two-input two-output system, where air outlet temperature and humidity are controlled variables, and lamp and cooler fan intensity are the manipulated variables. A multi-SISO-PID controller shows one of the possibilities that can be applied to control the system.
A Closed-Loop Frequency Domain PI Retuning Technique for Multivariable Systems
Aguiar, Anna Paula Virgolino de Andrade Univ. Federal De Campina Grande
Acioli Junior, George Universidade Federal De Campina Grande
Barros, Péricles R. Universidade Federal De Campina Grande
Perkusich, Angelo Federal University of Campina Grande
Abstract: A Proportional-Integral (PI) controller frequency domain retuning technique for square multivariable process is presented in this paper. Assuming knowledge of the current PI controller, the controller gains increments are computed in such that the new closed-loop matches with a desired reference model. This is performed using only the closed-loop response at certain frequencies, without knowledge of the process model. Effectiveness of the proposed method is shown in the simulation results. The maximum singular value is used to show the robustness. Finally, an experimental result is presented.
Time and Frequency Data-Driven PID Retuning Applied in MIMO Process
da Silva Moreira, Lucas José Université Grenoble Alpes
Abstract: This paper presents the application of the time and frequency domain data-driven methods to the iterative decentralized PID tuning of MIMO process. Each single loop is sequentially excited to generate data required for retuning. The initial PID tuning gains are adjusted in order to match the defined decentralized MIMO reference model. The proposed strategy is applied to process simulations and to an experimental coupled tanks pilot plant.
Real-Time Optimization with Persistent Parameter Adaptation Applied to Experimental Rig
Matias, José Norwegian University of Science and Technology
Paez de Castro Oliveira, Julio University of Sao Paulo
Le Roux, Galo A. C. University of São Paulo
Jäschke, Johannes Norwegian University of Science & Technology
Keywords: Scheduling and Optimization, Modeling and Identification, Industrial Applications
Abstract: Real-time Optimization (RTO) is a steady-state model-based method used for optimizing process operation in chemical plants. The most common implementation, two-step RTO (TS-RTO), updates the model parameters in the first step, and optimizes the steady-state model in the second step. It has a major drawback, which is the need to wait for steady-state. If data from transient periods is directly used for updating the steady-state model parameters, the production optimization results will most likely be sub-optimal, decreasing the benefits. This becomes even more acute if the system is constantly affected by disturbances and has long settling times. Matias and Le Roux (2018) proposed a TS-RTO variant that uses a dynamic estimator to update the steady-state model parameters, which was named real-time optimization with persistent parameter adaptation (ROPA). By using dynamic estimation, it ensures that the model is always updated to the plant and the steady-state optimization can be scheduled at any desired rate without needing to wait for steady-state. This hybrid approach has been successfully tested in simulations. In this paper, we show its first implementation in a lab-scale rig, which emulates a subsea oil well network. The results show that the hybrid approach enables an increase in the optimization frequency and a decrease in the optimization results variability, improving the overall economic performance when compared to the TS-RTO implementation.
Robust Multi-Scenario Dynamic Real-Time Optimization with Embedded Closed-Loop Model Predictive Control
MacKinnon, Lloyd McMaster University
Swartz, Christopher L.E. McMaster University
Abstract: Economic optimization is a key tool in ensuring competitive chemical plant operation. Traditional steady-state real-time optimization (RTO) is suboptimal in many applications where the plant exhibits frequent transitions or slow dynamics, thus requiring the use of dynamic RTO (DRTO). Additionally, DRTO algorithms exhibit faster response when able to account for the behavior of the underlying model predictive control (MPC) systems. This work seeks to combine closed-loop (CL) prediction of the plant response under the action of MPC with a scenario based robust modeling approach to account for plant uncertainty. The CL prediction is handled by directly modeling the MPC calculations and reformulating the resulting multilevel optimization problem as a single-level mathematical program with complementarity constraints (MPCC). The proposed robust CL DRTO formulation is compared against a single-scenario nominal CL DRTO in terms of maximizing economic performance in a case study involving a nonlinear CSTR. The robust DRTO is shown to outperform the nominal DRTO in this metric on average across the scenarios.
Reinforcement Learning Regular Session
Dynamic Penalty Function Approach for Constraints Handling in Reinforcement Learning
Yoo, Haeun KAIST
Zavala, Victor M. University of Wisconsin-Madison
Lee, Jay H. KAIST
Keywords: Big Data Analytics and Monitoring, Scheduling and Optimization
Abstract: Reinforcement learning (RL) is attracting attention as an effective way to solve sequential optimization problems that involve high dimensional state/action space and stochastic uncertainties. Many such problems involve constraints expressed by inequality constraints. This study focuses on using RL to solve constrained optimal control problems. Most RL application studies have dealt with inequality constraints by adding soft penalty terms for violating the constraints to the reward function. However, while training neural networks to learn the value (or Q) function, one can run into computational issues caused by the sharp change in the function value at the constraint boundary due to the large penalty imposed. This difficulty during training can lead to convergence problems and ultimately lead to poor closed-loop performance. To address this issue, this study proposes a dynamic penalty (DP) approach where the penalty factor is gradually and systematically increased during training as the iteration episodes proceed. We first examine the ability of a neural network to represent a value function when uniform, linear, or DP functions are added to prevent constraint violation. The agent trained by a Deep Q Network (DQN) algorithm with the DP function approach was compared with agents with other constant penalty functions in a simple vehicle control problem. Results show that the proposed approach can improve the neural network approximation accuracy and provide faster convergence when close to a solution.
Constrained Q-Learning for Batch Process Optimization
Pan, Elton Imperial College London
Petsagkourakis, Panagiotis University College London
Mowbray, Max University of Manchester
Zhang, Dongda University of Manchester
del Rio-Chanona, Ehecatl Antonio Imperial College London
Keywords: Batch Processes, Scheduling and Optimization, Model-based Control
Abstract: Chemical process optimization and control are often mired by the need to satisfy constraints for safe operation. Reinforcement learning (RL) has been shown to be a powerful control approach that can handle nonlinear stochastic optimal control problems. However, despite the promise exhibited, RL has yet to see marked translation to industrial practice primarily due to its inability to satisfy state constraints. In this work we aim to address this challenge. We propose an "oracle"-assisted constrained Q-learning algorithm that guarantees the satisfaction of joint chance constraints with a high probability, which is crucial for safety critical tasks. To achieve this, constraint tightening (backoffs) are introduced, which can be adjusted using Broyden's method, hence making them self-tuned. This results in a general methodology that can be imbued into approximate dynamic programming-based algorithms to ensure constraint satisfaction with high probability. Finally, we present case studies that analyze the performance of the proposed approach and compare this algorithm with model predictive control (MPC). The superior performance of this algorithm, in terms of constraint handling, signifies a step toward the incorporation of RL into real world optimization and control of systems, where constraints are essential in ensuring safety.
Control of a Polyol Process Using Reinforcement Learning
Zhu, Wenbo Louisiana State University
Hayot, Philippe Dow Inc
Abstract: Reinforcement learning is a branch of machine learning, where an agent gradually learns a control policy via a combination of exploration and interactions with a system. Recent successes of model-free reinforcement learning (RL) has attracted tremendous attention from the process control community. For instance, RL has been successfully applied in very complex control tasks (e.g., games such as chess or Go that contain large state spaces) and is shown to be robust to uncertainties. These findings indicate that there is a significant potential to leverage RL methods to improve the control of chemical processes. In this work, RL was applied to a detailed and accurate simulation of an industrial polyol process. To manufacture the desired product, the RL controller is required to achieve the target ending conditions determined by four key parameters; meanwhile, economic factors are also considered in this process, including batch reaction time and total feed amounts. The obtained results show a high consistency between RL and the current optimal operating conditions. Additionally, an improvement opportunity was identified by extending current control bounds of the manipulated variables. This work illustrates that RL is capable of handling complicated industrial systems, even under realistic operating constraints.
Model-Free Safe Reinforcement Learning for Chemical Processes Using Gaussian Processes
Savage, Thomas University of Manchester
Keywords: Batch Processes, Modeling and Identification, Scheduling and Optimization
Abstract: Model-free reinforcement learning has been recently investigated for use in chemical process control. Through the iterative creation of an approximate process model, control actions are able to be explored and optimal policies generated. Typically, this approximate process model has taken the form of a neural network that is continuously updated. However when small quantities of historical data are available, for example in novel processes, neural networks tend to over-fit to data providing poor performance. In this paper Gaussian processes are used as a method of function approximation to describe the action-value function of a non-isothermal semi-batch reactor. Through the use of analytical uncertainty obtained from Gaussian process predictions, trade off between exploration and exploitation is enabled, allowing for efficient generation of effective policies. Importantly Gaussian processes also enable probabilistic constraint violation to be modelled, ensuring safe constraint satisfaction throughout the learning procedure. On application to the in-silico case study, a safe, effective policy was generated utilising only 100 evaluations of process trajectory with no prior knowledge of the process dynamics. A result that would require significantly more trajectory evaluations when compared to a neural network based approach.
Simultaneous Process Design and Control Optimization Using Reinforcement Learning
Sachio, Steven Imperial College London
Keywords: Scheduling and Optimization, Batch Processes, Model-based Control
Abstract: The performance of a chemical plant is highly affected by its design and control. A design cannot be accurately evaluated without its controls and vice versa. To optimally address design and control simultaneously, one must formulate a bi-level mixed-integer nonlinear program with a dynamic optimization problem as the inner problem; this is intractable. However, by computing an optimal policy using reinforcement learning, a controller with a closed-form expression can be computed and embedded into the mathematical program. In this work, an approach that uses a policy gradient method to compute the optimal policy, which is then embedded into the mathematical program is proposed. The approach is tested in a tank design case study and the performance of the controller is evaluated. It is shown that the proposed approach outperforms current state-of-the-art control strategies. This opens a whole new range of possibilities to address the simultaneous design and control of engineering systems.
Scheduling and Optimization I Regular Session
A Simulation Study on Turnpikes in Stochastic LQ Optimal Control
Ou, Ruchuan TU Dortmund University
Baumann, Michael Heinrich Universität Bayreuth
Gruene, Lars Univ of Bayreuth
Abstract: This paper presents a simulation study on turnpike phenomena in stochastic optimal control problems. We employ the framework of Polynomial Chaos Expansions (PCE) to investigate the presence of turnpikes in stochastic LQ problems. Our findings indicate that turnpikes can be observed in the evolution of PCE coefficients as well as in the evolution of statistical moments. Moreover, the turnpike phenomenon can be observed for optimal realization trajectories and with respect to the optimal stationary distribution. Finally, while adding variance penalization to the objective alters the turnpike, it does not destroy the phenomenon.
Many-Objective Simulation-Based Optimization of an Air Separation Unit
Salas, Santiago D Escuela Superior Politécnica Del Litoral
De Cecchis, Dany Escuela Superior Politécnica Del Litoral
Mr Bryan Vladimir Piguave, Bryan Escuela Superior Politécnica Del Litoral
Keywords: Scheduling and Optimization, Industrial Applications, Energy Processes
Abstract: Air separation systems are crucial in the production of oxygen, which has gained particular relevance during the COVID-19 outbreak. Mechanical ventilation can compensate respiratory deficiencies along with the use of medical oxygen in vulnerable patients infected with this disease. In this contribution, a many-objective simulation-based optimization framework is proposed for determining eleven decision variables for the operation of an air separation unit. The framework combines the capabilities of the process simulator PRO/II with a Python environment. Three objective functions are optimized together towards the construction of a 3-D Pareto front. Results provide insightful information regarding the most adequate operating conditions of the unit, including the definition of an operational window rather than a single operational point.
Optimization of Microfluidic Layouts As a Wired Packing Problem
Sanjy, Andriamiseza Fluigent
Trellet, Mikael Fluigent
Nicolas, Lafitte Fluigent
Clerget, Charles-Henri Acoustic Wells
Petit, Nicolas MINES ParisTech, PSL University
Keywords: Scheduling and Optimization, Industrial Applications
Abstract: This paper presents a methodology to optimize the physical layout of microfluidic components, a key step in the design of custom microfluidic instruments that can be used in various process applications. A mathematical formulation is proposed under the form of a Mixed Integer Linear Problem allowing to treat non overlapping constraints for the multi-objective optimization of layout footprint and connectivity lengths. The method is numerically tested using randomly generated scenarios. Then, a real testcase serves as illustration.
Integration of Planning, Scheduling, and Control for Multi-Product Chemical Systems under Preventive Maintenance
Peng, Yanshuo University of Waterloo
Ricardez-Sandoval, Luis University of Waterloo
Abstract: This study presents a framework to perform simultaneous planning, scheduling, and control (iPSC) under preventive maintenance (PM) for multi-product continuous manufacturing units. PM is an essential part of the chemical industry as they provide robustness to the operation and minimize potential plant shutdowns. Flexible planning periods are also investigated in this work since they can improve process economics. A case study featuring a multi-product CSTR is presented to illustrate the benefits of the proposed framework. The results show that flexible planning periods can lead to considerable economic improvement compared to fixed planning periods. Also, an appropriate PM program combined with flexible planning periods provides the opportunity to optimize the maintenance time instead of fixing those times a priori using process heuristics.
Design and Optimization for Simulated Moving Bed: Varicol Approach
Matos, Joana Laboratory of Separation and Reaction Engineering Associate Labo
Faria, Rui P.V. Laboratory of Separation and Reaction Engineering Associate Labo
Abstract: Simulated Moving bed adsorption processes are characterized by their complex dynamic behavior. Due to this issue, as deeper are explored their design possibilities, more complexity is expected. One example of this is the Varicol configuration. In this operating mode, it is possible to use more than one configuration in two consecutive switches through an asynchronous switch, reducing the unit costs in comparison with standard configurations. This work presents a new strategy for the design and concomitant optimization of SMB devices considering a Varicol operating mode. The optimization will include both the operating conditions and the configuration of the device. The optimization results enable the design of a Varicol configuration for the separation of the bi-naphthol enantiomers which can also be generelazied for other systems. The results have shown that including the configuration in the optimization process allows to increase the productivity of the separation process by 15% while reducing the total unit size.
Optimal Design of SMB Units: A Novel Strategy Based on Particles Swarm Optimization
Keywords: Scheduling and Optimization, Industrial Applications, Modeling and Identification
Abstract: The Simulated Moving Bed (SMB) is a separation device whose use has been increasing, especially in separations that demand high purities like the chiral separation. Process Design is a topic often discussed in the literature and is usually limited to a sensitivity analysis. An optimal design of a SMB unit offers challenges due to its complexity on dynamics and numerical levels. Thus, there is a lack of a consistent methodology to optimize the SMB design. This work is focused on the SMB design which will be made using the Particles Swarm Optimization (PSO) method. For the first time, PSO will be used to choose the configuration, i.e., length of each section, of the True Moving Bed (the theoretical model of the SMB) unit for the case of the separation of the bi-naphthol enantiomers. The operating conditions, in terms of flowrates, will also be optimized. Globally, with the design strategy that was implemented, the system's productivity is at least 30% higher than previous results reported in the literature for the TMB without optimization of the device configuration. The SMB that was designed from this configuration enables to increase the separation productivity at least 20% in comparison with previous results.
Scheduling and Optimization II Regular Session
Nonlinear State Feedback Control Design for Port-Hamiltonian Systems with Unstructured Component
Alavi, Seyedabbas Queen's University at Kingston
Hudon, Nicolas Queen's University
Abstract: This paper considers the problem of state feedback controller design to stabilize generalized Hamiltonian systems with an unstructured component. This class of models enable one to exploit the structure of port-Hamiltonian systems for feedback control design while relaxing the constraint of deriving an exact structured port-Hamiltonian representation. For a given stabilizable nonlinear system, and with some assumptions on the unstructured part of the dynamics, a stabilizing control law is designed and asymptotic stability of a desired equilibrium of the system is demonstrated. A numerical illustration of the proposed approach is presented to demonstrate the design method.
Energy and Carbon-Constrained Production Planning with Parametric Uncertainties
CHATURVEDI, NITIN DUTT Indian Institute of Technology Patna
Kumawat, Piyush Kumar Indian Institute of Technology, Patna
KESHARI, ADITYA Indian Institute of Technology Patna
Keywords: Energy Processes, Industrial Applications, Scheduling and Optimization
Abstract: The intent of this paper is to develop a mathematical model considering uncertainties in aggregate production planning problems. It is being a great advance to consider uncertainty in production planning problems compared to models that do not account for uncertainty which results in poor planning decisions. The paper deals with the planning problems with multiple process routes to satisfy single product demand. Robust optimization is used to target parametric uncertainty to immune the model against it. The objective of the problem is to minimize the overall production cost, capping the total carbon emission and energy consumption. A detailed methodology is presented to develop a robust optimization counterpart handling parametric uncertainties. The model has the ability to control the degree of conservatism for every constraint using budget parameter and guarantees feasibility for the robust optimization problem. The applicability of the model is explained using a case study. The impact of different budget parameters on the objective value is studied, it will assist the planner to prepare the production plan with the known uncertainty level.
Assessment of Demand Response Particibility Potential Based on Cost-Effective Process Design
Liu, Yu University of California, Davis
Campos, Gustavo University of California, Davis
Palazoglu, Ahmet Univ. of California at Davis
El-Farra, Nael H. University of California, Davis
Abstract: In this work, a model-based analytical framework is proposed to evaluate the cost-effectiveness of process design alternatives with respect to their ability to participate (particibility) in Demand Response (DR) services (such as load shifting under the Day-Ahead market) through the use of supply curves. The supply curves relate available DR capability to the investment cost of process design, and are constructed based on a specific DR service. Using the California Independent System Operator (CAISO) wholesale electricity market as a reference, the proposed framework is implemented to demonstrate the cost-benefits of considering DR aspects at the design stage. As a motivating example, the developed framework is illustrated using a CSTR-storage model, for which supply curves are generated under different scenarios. A visualization is provided of the effect of the process design capacities on the DR capability, as well as the limitations on the load-shifting capacity of a given process design.
Value Chain Planning Optimization: A Data Driven Digital Twin Approach
Ferranti, Filippo Politecnico Di Milano
Manenti, Flavio Politecnico Di Milano
Vingerhoets, Geoff BASF Antwerpen
Vallerio, Mattia BASF Antwerpen
Keywords: Industrial Applications, Scheduling and Optimization, Big Data Analytics and Monitoring
Abstract: The long term production planning for a large chemical production site, where 10+ different chemical plants share raw materials, infrastructure (e.g., tank farm, filling stations) and utilities (e.g. steam, electricity, technical gasses) might prove to be a challenging task. This paper introduces a data driven approach to build a digital twin of a chemical production site to aid the relevant decision makers in defining and evaluating the economic impact of a long term (i.e., several months ahead) production planning. Each chemical plant and energy production unit on site is represented by simple regression models relating the consumption of raw materials and utilities to its products. The resulting system of algebraic equations has been inserted in an optimization environment with the objective of maximizing the profit. In the optimization, also the electricity and steam generation were introduced to obtain a global energy balance of the production site. This combination resulted in a multi period Mixed-Integer Linear Programming (MILP) problem. The effect of electricity price and external temperature on the optimization results are also investigated.
Integration of Scheduling and Control under Stochastic Parametric Uncertainty with Varying Unit Operation Times for Chemical Batch Plants: A Back-Off Approach
Rodríguez Vera, Héctor Uriel University of Waterloo
Abstract: A new back-off methodology is presented in this work as an approach for solving MIDO formulations arising for the optimal scheduling and control of flow-shop batch plants under stochastic parametric uncertainty. The proposed algorithm decomposes the MIDO problem into a scheduling problem, a dynamic optimization problem and a unit time operation minimization problem. These problems are solved iteratively using back-off terms. Parametric uncertainty is modeled using statistical distribution functions and are embedded in the algorithm to ensure dynamic feasibility of the optimal control actions under stochastic realizations in those parameters. The proposed algorithm identifies scheduling and control decisions offline. To exemplify this methodology, the integration of scheduling and control of a flow-shop batch plant is considered. The results show that unit operation times chosen from optimization are better suited to accommodate stochastic parametric uncertainty while the control actions enforce process operational and product quality constraints at reasonable costs.
SuSAINV01
Advances in Set-Based and Stochastic Methods for Estimation and Control of
Nonlinear Process Systems Invited Session
Organizer: Paulen, Radoslav Slovak University of Technology in Bratislava
Organizer: Houska, Boris ShanghaiTech University
Organizer: Villanueva, Mario Eduardo ShanghaiTech University
Organizer: Mesbah, Ali University of California, Berkeley
Organizer: Chachuat, Benoit Imperial College London
08:00-18:00, Paper SuSAINV01.1
An Adaptive Correction Scheme for Offset-Free Asymptotic Performance in Deep Learning-Based Economic MPC (I)
Krishnamoorthy, Dinesh Norwegian University of Science and Technology
Keywords: Model-based Control, Industrial Applications, Scheduling and Optimization
Abstract: There has been an increasing interest in explicit and cheap-to-evaluate control policies that approximate (computationally expensive) control laws such as model predictive control (MPC). However, approximate control policies are subject to approximation errors, leading to asymptotic performance losses. The contribution of this paper is three-fold: (i) a closed-loop training scheme is presented for deep neural network approximation of economic MPC; (ii) an online adaptive correction scheme is presented to account for the performance losses induced by approximation errors; and (iii) an online performance verification scheme is presented to ensure that the approximate control policy converges to an equilibrium point of the system. The proposed approach is illustrated using a Williams-Otto reactor problem.
Enclosing Reachable Sets for Nonlinear Control Systems Using Complementarity-Based Intervals (I)
Cao, Huiyi McMaster University
Khan, Kamil McMaster University
Keywords: Scheduling and Optimization, Batch Processes
Abstract: Enclosing the reachable sets of nonlinear control systems is useful in state estimation and safety verification for chemical process models. We present a new approach for computing time-varying interval bounds for dynamic systems based on differential inequalities. Instead of using interval arithmetic like established approaches, we instead generate bounding information for the right-hand side (RHS) function by optimizing its convex relaxations. Complementarity formulations are explored, and are found to be particularly beneficial if the RHS function is quadratic and we employ the αBB relaxations.
Set-Membership Estimation Using Ellipsoidal Ensembles (I)
Valiauga, Petra Slovak University of Technology in Bratislava
Feng, Xuhui ShanghaiTech University
Villanueva, Mario Eduardo ShanghaiTech University
Paulen, Radoslav Slovak University of Technology in Bratislava
Houska, Boris ShanghaiTech University
Abstract: This paper is about a set-membership based state- and parameter estimation approach for nonlinear dynamic systems under the assumption that all measurement errors are bounded. In detail, we propose an outer approximation method, where the set of states and parameters that is consistent with the incoming measurement bounds is over-approximated by an intersection of ellipsoids. We introduce computationally tractable methods for propagating such ellipsoidal ensembles through dynamic systems and construct an associated recursive estimation algorithm. We also show how to select the past measurements such that the intersection problem remains tractable on long time horizons. The proposed approach is illustrated by applying it to a batch membrane process.
Interval State Estimation Based on Constraint Propagation for a Lithium-Ion Cell Using an Equivalent Circuit Model (I)
Locatelli, Diego Università Degli Studi Di Pavia
Rego, Brenner Santana Federal University of Minas Gerais
Raffo, Guilherme Vianna Federal University of Minas Gerais
Raimondo, Davide Martino Università Degli Studi Di Pavia
Abstract: Battery management systems (BMSs) are responsible for controlling and monitoring battery operations. In order to be effective, BMSs rely on mathematical models. The parameters of these latter are usually obtained through an identification procedure which provides limited accuracy. In addition, only cell current and voltage are usually measurable. For this reason, the model states need to be estimated. In the following, we propose an interval state estimation of a lithium-ion cell when using an equivalent circuit model, taking into account bounded parametric uncertainty and measurement noise. Simulation results show the suitability of the proposed interval estimator to provide a tight enclosure of the states, which is essential for fault detection and model-based control of a lithium-ion cell.
Energy, CO2 Capture, Management and Utilization Invited Session
Organizer: Ricardez-Sandoval, Luis University of Waterloo
Organizer: Budman, Hector M. Univ. of Waterloo
Optimising Carbon Capture and Storage Supply Chains for the European Industry (I)
d'Amore, Federico University of Padova
Romano, Matteo Carmelo Politecnico Di Milano
Keywords: Energy Processes, Scheduling and Optimization
Abstract: Carbon capture and storage is considered of fundamental importance to achieve a remarkable decarbonisation of steel, cement and refining sectors. To operate carbon capture and storage at scale and address its inherent complexity, mathematical programming techniques can be exploited to optimise such systems. This contribution proposes a Europe-wide, spatially-explicit, time-dependent, carbon capture and storage chains optimisation, based on mixed integer linear programming architecture. Capture plants can be installed in all significant industrial CO2 emitters, which comprise 25 steel mills, 111 cement plants and 59 refineries. A techno-economic description of capture plants is provided, based on scale effects and different options. Transport can be operated through pipelines and offshore storage is taken into account in the North Sea and Adriatic area. The analysis allows identifying the most promising sectors and optimal specific plants where capture should be operated, and the evolution of the system throughout the time horizon. Considering a time-varying carbon reduction target, the avoidance cost is 75.6 e/t of CO2 for a North Sea targeted network, and decreases by 1.9% when sequestration in the Adriatic Sea is also taken into account.
Optimal Design and Control of a Multiscale Model for a Packed Bed Chemical-Looping Combustion Reactor (I)
Toffolo, Kathryn University of Waterloo
Keywords: Energy Processes, Industrial Applications, Model-based Control
Abstract: Chemical-Looping Combustion (CLC) is a novel carbon capture technology that can be implemented in fossil-fired plants to facilitate the isolation and capture of CO2. In this work, we present a dynamic multiscale model of this process and implement simultaneous design and control of this reactor in order to investigate its feasibility for industrial use. The model considers both the macroscale reactor behavior and the microscale particle behavior. The optimal design and control formulation is posed as a nonlinear optimization problem that was solved using the direct transcription approach. The optimal solution is compared to that obtained from a sequential design and control approach. The results show that the sequential approach converged to a design that was not able to control the outlet temperature adequately, thus illustrating the benefits of a simultaneous design and control approach.
An Optimization Framework for Solving Integrated Planning and Scheduling Problems for Dense Energy Carriers (I)
Allen, R. Cory Texas A&M Energy Institute
Baratsas, Stefanos G. Texas A&M University
Kakodkar, Rahul Texas A&M University
Avraamidou, Styliani Texas A&M University
Powell, Joseph B. Royal Dutch Shell
Heuberger, Clara F. Royal Dutch Shell
Demirhan, Cosar Doga Texas A&M Energy Institute, Texas A&M University
Pistikopoulos, Efstratios N. Texas A&M University
Abstract: Due to rapid population growth, the global demand of energy and water resources are accelerating. Simultaneously, there is a global trend to transition to renewable energy systems. Recently, researchers have begun to investigate how green dense energy carriers (DECs) can be apart of this transition. In this work, we present an optimization framework for solving large-scale supply chain problems and we apply it to explore the economic and environmental impacts of DECs. Specifically, we look at utilizing DECs to transport renewable energy produced in areas with high solar and wind potentials to regions with low renewable potential. To reduce the computational burden of the large-scale optimization problem, we have developed a greedy randomized adaptive search procedure (GRASP). The GRASP leverages the linear programming (LP) relaxation of the problem to generate feasible solutions. We have found that the GRASP is able to reduce the computational time by approximately two orders of magnitude as compared to a commercial grade mixed-integer linear programming (MILP) solver ran out-of-the-box on large-scale instances.
A Simple Combined Dynamic-Equilibrium Model of CO2 Capture by a Pelleted Zeolite Sorbent
Bisone, Luigi Consultant
Bittanti, Sergio Politecnico Di Milano
Canevese, Silvia RSE S.p.A
Davarpanah, Elahe Politecnico Di Torino
De Marco, Antonio Consultant
Notaro, Maurizio RSE Milan
Prandoni, Valter RSE (Ricerca Sistema Energetico)
Keywords: Batch Processes, Modeling and Identification
Abstract: Carbon dioxide (CO2) can be effectively captured from gas mixtures by adsorption on zeolite pellets in fixed-bed columns. In this paper we start from an experimental analysis of CO2 adsorption on a sample of few pellets, to end up with a simulation model for an adsorption column filled with a large number of pellets. The initial experimental analysis is based on dedicated isothermal tests carried out with a gas adsorption analyzer. For the relation between the measured amount of adsorbed CO2 per unit sorbent mass and the input adsorption pressure and temperature, an equilibrium Three-Site Langmuir (TSL) equation is here adopted. Its unknown parameters, namely the saturation capacities, the affinities and the adsorption energies, are estimated by minimizing the error between the equation and the measures. Then, for a whole adsorption column, a simple dynamic model is worked out, based on one-dimensional partial differential equations along the column axial direction: these account basically for mass and energy conservation, for the bulk gas flow and for the porous sorbent. The overall description obtained by combining the TSL equation with the rest of the model is validated against experimental tests, consisting of inlet CO2 molar fraction steps, on a laboratory-scale column.
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