The present invention relates to systems and methods of process or system modeling and control and, specifically, process modeling and control using a calculated confidence in the system model.
In a conventional fossil fuel-fired power plant or generating unit, a fossil fuel/air mixture is ignited in a boiler. Large volumes of water are pumped through tubes inside the boiler, and the intense heat from the burning fuel turns the water in the boiler tubes into high-pressure steam. In an electric power generating application, the high-pressure steam from the boiler passes into a turbine comprised of a plurality of turbine blades. Once the steam hits the turbine blades, it causes the turbine to spin rapidly. The shaft of the spinning turbine is linked to the shaft of a generator, and the rotating shaft within the generator may be used to create an electric potential.
As used herein, the term “power generating plant” refers to one or more power generating units. Each power generating unit may drive one or more turbines used for generating electricity. A power generating unit is typically powered by fossil fuels (including but not limited to, coal, natural gas or oil), and includes a boiler for producing high temperature steam; air pollution control (APC) devices for removal of pollutants from flue gas; a stack for release of flue gas; and a water cooling system for condensing the high temperature steam. A typical power generating unit will be described in detail below in relation to FIG. 1.
As will be appreciated, boiler combustion or other characteristics of a fossil fuel-fired power generating unit are influenced by dynamically varying parameters of the power generating unit, including, but not limited to, air-to-fuel ratios, operating conditions, boiler configuration, slag/soot deposits, load profile, fuel quality and ambient conditions. Changes to the business and regulatory environments have increased the importance of dynamic factors such as fuel variations, performance criteria, emissions control, operating flexibility and market driven objectives (e.g., fuel prices, cost of emissions credits, cost of electricity, etc.).
Further, about one half of the electric power generated in the United States is generated using coal-fired power generating units. Coal-fired power generating units used in power plants typically have an assortment of air pollution control (APC) devices installed for reducing nitrogen oxides (NOx), sulfur oxides (SOx), and particulate emissions. In this regard, selective catalytic reduction (SCR) systems are used for NOx reductions. Spray dry absorbers (SDA) and wet flue gas desulfurization (FGD) systems are used for SOx reductions. Electro-static precipitators (ESPs) and fabric filters (FF) are used for reducing particulate emissions.
Over the past two decades, combustion optimization systems have been implemented for advanced control of the combustion process within the furnace. Typically, combustion optimization systems interface with the distributed control system (DCS) of a power generating unit. Based upon the current operating conditions of the power generating unit, as well as a set of operator specified goals and constraints, the combustion optimization system is used to compute the optimal fuel-to-air staging within the furnace to achieve the desire goals and constraints.
Combustion optimization systems were originally implemented to reduce nitrogen oxides (NOx) produced in the furnace and emitted to the atmosphere via the stack. For example, U.S. Pat. No. 5,280,756 teaches a method and system for controlling and providing guidance in reducing NOx emissions based upon controllable combustion parameters and model calculations while maintaining satisfactory plant performance. U.S. Pat. No. 5,386,373 teaches the use of a predictive model of emissions, including NOx, in conjunction with a control system, while U.S. Pat. No. 6,381,504 describes a method for optimally determining the distribution of air and fuel within a boiler by aggregating the distributions of air and fuel into two common variables, performing an optimization, and then computing the optimal distribution of fuel and air based upon the optimal values of the aggregated variables. U.S. Pat. No. 6,712,604 describes a system for controlling the combustion of fuel and air in a boiler such that the distributions of NOx and CO are maintained to average less than the maximum permitted levels.
In addition, combustion optimization approaches have been used to control boiler parameters in addition to NOx, including unit heat rate, boiler efficiency, and mercury emissions. For example, U.S. Pat. No. 7,398,652 teaches an approach to modeling controllable losses in a power generating unit and a method for optimizing the combustion process based upon these controllable losses. U.S. Pat. No. 8,644,961 teaches a method for reducing mercury emissions from a coal-fired power plant while observing limits on the amount of carbon in the fly ash produced. U.S. Pat. No. 7,522,963 teaches a controller for directing operation of an air pollution control system, such as an FGD or SCR, such that a predefined optimization objective is minimized. Optimization techniques have also been used to control the removal of soot within a boiler. For example, U.S. Pat. No. 6,736,089 teaches a method for removal of soot based upon optimizing a set of boiler performance parameters using a model of the cleanliness factors of heat transfer surfaces. Optimization has also been extended beyond specific components within a power generating unit, such as the boiler, FGD and SCR. As provided in U.S. Pat. No. 7,844,351, which is hereby incorporated herein in its entirety, an approach to optimization of multiple components, within a single power generating unit or multiple power generating units, is described.
Neural networks have been used to both predict and optimize industrial systems. For example, U.S. Pat. No. 5,167,009 teaches a method for predicting output data of a process using a neural network and subsequently using the prediction in a control or optimization system. As another example, U.S. Pat. No. 7,123,971 teaches an approach to training neural network models to predict the change in the output based upon the change in the inputs. Using this approach, referred to as disturbance rejection based training, the model is trained to learn the cause of the change in an output rather than simply learning the correlation structure of the data which results in a significant improvement in optimization results. In addition, U.S. Pat. No. 6,725,208 teaches the use of a Bayesian training technique of combining multiple weighted neural network models to form a model used in optimization of a process. This type of model can be used to estimate the uncertainty in the predicted output, though these capabilities are limited.
Thus, the prior art describes methods and systems for using models, including neural network models, to optimize industrial systems and processes. In addition, disturbance rejection based training has been found to provide more accurate neural network models for use in process optimization. The prior art, however, does not describe a disturbance rejection based neural network training method for predicting change in the output due to change in the inputs along with an estimate of the uncertainty in the predicted change in the output nor does it describe how such a neural network model could be used to improve optimization. The present invention provides systems and methods that overcome the abovementioned shortcomings of the prior art, and provide advantages over conventional approaches to control and optimization industries such as power generation.