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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.04592 |
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| _version_ | 1866909194944249856 |
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| author | Rogers, Alexander W. Lane, Amanda Mendoza, Cesar Watson, Simon Kowalski, Adam Martin, Philip Zhang, Dongda |
| author_facet | Rogers, Alexander W. Lane, Amanda Mendoza, Cesar Watson, Simon Kowalski, Adam Martin, Philip Zhang, Dongda |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04592 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating knowledge-guided symbolic regression and model-based design of experiments to automate process flow diagram development Rogers, Alexander W. Lane, Amanda Mendoza, Cesar Watson, Simon Kowalski, Adam Martin, Philip Zhang, Dongda Machine Learning 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. |
| title | Integrating knowledge-guided symbolic regression and model-based design of experiments to automate process flow diagram development |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.04592 |