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Surrogate-based multi-objective optimisation via tree regression
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AI
DOI：10.1016/j.compchemeng.2025.109538.png)
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Abstract
En 中文
Modern industries rely on advanced modelling techniques to enhance process efficiency, yet the computational complexity of these models often limits their direct use in optimisation. To tackle this issue, surrogate-based approaches for optimising manufacturing flowsheets can be used. In this work, we introduce a multi-objective tree regression approach for surrogate-based optimisation, integrating a multi-target tree regression model to approximate complex process dynamics. The proposed approach can be extended and formulated as a strategic decision-making problem, to reveal optimal trade-offs between conflicting objectives such as yield, process mass intensity, and purity in Pharmaceutical Manufacturing. By combining Pareto-fronts with game-theoretic and/or compromise solutions, the methodology offers a systematic way to define the limits of the feasible space and identify optimal operational strategies in the absence of decision making preferences. The proposed approach enhances interpretability, computational efficiency, and practical applicability, offering a powerful tool for decision-making in pharmaceutical manufacturing and beyond.
Keywords:
Multi-objective process optimisation
Tree Regression
Surrogate-based optimisation
Decision making
Pharmaceutical industry
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3.9
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286
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