  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.
