Integrating Knowledge-Guided Symbolic Regression and Model-Based Design of Experiments to Accelerate Process Flow Diagram Development

• Published in: IFAC-PapersOnLine 2024, Vol.58 (14), p.127-132
• Main Authors: Rogers, Alexander W., Lane, Amanda, Mendoza, Cesar, Watson, Simon, Kowalski, Adam, Martin, Philip, Zhang, Dongda
• Format: Article
• Language: English
• Subjects: interpretable machine learning; knowledge discovery; model-based design of experiments; process flow diagram optimization; symbolic regression
• ISSN: 2405-8963; 2405-8963
• DOI: 10.1016/j.ifacol.2024.08.325

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, process scale-up must currently undergo expensive trial-and-error campaigns. To accelerate process flow diagram (PFD) optimization and knowledge discovery, this work proposes 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 designs a new experiment to discriminate between them while automatically balancing the objective of PFD optimization. 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 within the general chemical industry for digital manufacturing and product innovation.