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 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 effectively discoverd ground-truth process mechanisms within a few iterations, indicating its great potential for the general chemical industry for digital manufacturing and product innovation.

中文翻译：

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集成知识引导的符号回归和基于模型的实验设计以自动化流程图开发


新产品必须快速配制才能在全球配制产品市场取得成功；然而，关键产品指标 (KPI) 可能很复杂，而且人们对化学成分和加工历史的了解很少。因此，扩大规模目前必须经历昂贵的试错活动。为了加速流程流程图（PFD）优化和知识发现，这项工作提出了一种新颖的数字框架，通过将符号回归（SR）集成到基于模型的实验设计（MbDoE）中来自动量化流程机制。每次迭代，SR 都会提出可解释的机械表达式的帕累托前沿，然后 MbDoE 设计一个新的实验来区分它们，同时平衡 PFD 优化。为了研究该框架的性能，构建了一个能够模拟通用配方产品合成的新工艺模型，以生成不同案例研究的计算机数据。该框架在几次迭代内有效地发现了真实的过程机制，表明其在通用化学工业数字化制造和产品创新方面的巨大潜力。