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Data-driven strategies for extractive distillation unit optimization
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DOI：10.1016/j.compchemeng.2022.107970.png)
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We provide insights for the development of a fast, dynamic, and adaptive way of modeling and optimizing chemical processes. We investigate two data-driven methodologies to optimize the energy cost of an extractive distillation process using an Aspen simulator. The first method uses surrogate-based optimization to explore two model-building techniques: Automated Learning of Algebraic Models's (ALAMO) generalized linear models and neuron network models with rectifier (ReLU) activation function. We compare the accuracy and performance of these models when embedded into mathematical programming models. The second approach uses black-box optimization (BBO) to optimize problems directly using simulation results. We compare four BBO solvers and three different penalty functions to address constraints. We find that ALAMO performs well for a less complex system, whereas the ReLU network performs better for a more complex one. BBO with a smooth penalty function for constraints is the more effective approach for problems considered in this study.
Keyword:
Data-driven optimization
Surrogate modeling
Black-box optimization
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3.9
论文数:
8.1K
被引数:
1.7W
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