Integrating knowledge-guided symbolic regression and model-based design of experiments to automate process flow diagram development

TL;DR

This paper presents a novel digital framework combining symbolic regression and model-based design of experiments to automatically identify process mechanisms and optimize process flow diagrams in chemical manufacturing.

Contribution

It introduces an integrated approach that automates process mechanism discovery and PFD optimization using symbolic regression within a design of experiments framework.

Findings

Framework effectively discovers ground-truth process mechanisms within few iterations.

Simulated data demonstrates potential for digital manufacturing and product innovation.

Method accelerates process understanding and reduces trial-and-error in chemical industry.

Abstract

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.