Integrating knowledge-guided symbolic regression and model-based design of experiments to automate process flow diagram development
<|reference_start|>Integrating knowledge-guided symbolic regression and model-based design of experiments to automate process flow diagram development: New products must be formulated rapidly to succeed in the global formulated product market; however, key product indicators (KPIs) can be complex, poorly understood functions of the chemical composition and processing history. Consequently, scale-up must currently undergo expensive trial-and-error campaigns. To accelerate process flow diagram (PFD) optimisation and knowledge discovery, this work proposed a novel digital framework to automatically quantify process mechanisms by integrating symbolic regression (SR) within model-based design of experiments (MBDoE). Each iteration, SR proposed a Pareto front of interpretable mechanistic expressions, and then MBDoE designed a new experiment to discriminate between them while balancing PFD optimisation. To investigate the framework's performance, a new process model capable of simulating general formulated product synthesis was constructed to generate in-silico data for different case studies. The framework could effectively discover ground-truth process mechanisms within a few iterations, indicating its great potential for use within the general chemical industry for digital manufacturing and product innovation.<|reference_end|>
@article{rogers2024integrating,
    title={Integrating knowledge-guided symbolic regression and model-based design
  of experiments to automate process flow diagram development},
    author={Alexander W. Rogers, Amanda Lane, Cesar Mendoza, Simon Watson, Adam
  Kowalski, Philip Martin, Dongda Zhang},
    journal={arXiv preprint arXiv:2405.04592},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04592},
    primaryClass={cs.LG}
}