Multi-model approach based on parametric sensitivities - A heuristic approximation for dynamic optimization of semi-batch processes with parametric uncertainties
Multi-model approach based on parametric sensitivities - A heuristic approximation for dynamic optimization of semi-batch processes with parametric uncertainties
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DOI：
10.1016/j.compchemeng.2016.12.004
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Jennifer Puschke;A. Zubov;J. Košek;A. Mitsos
中科院分区：
文献类型：
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作者：
Jennifer Puschke;A. Zubov;J. Košek;A. Mitsos
最优过程通常表现出主动路径约束。因此，过程模型中的参数不确定性可能会导致违反约束。提出了一种启发式方法来克服这一挑战。由于最坏情况模型，标称模型通过附加路径约束进行了优化。基于约束对不确定参数的敏感性，提出了选择这些模型的启发式方法。所提出的近似并不能保证稳健的可行性，但与仅使用标称模型的优化相比，路径约束违规不太可能发生。介绍了两个案例研究：复杂的乳液共聚过程（具有 139 个方程的 DAE）和青霉素的形成（四个微分方程和两个代数方程）。两个案例研究的结果表明，与名义情况下的优化相比，多模型方法不会违反参数不确定性集不同场景的路径约束。
Optimal processes often exhibit active path constraints. Parametric uncertainties in the process model might thus lead to constraint violations. A heuristic approach is presented to overcome this challenge. The nominal model is optimized with additional path constraints due to worst-case models. A heuristic method of choosing these models is proposed based on sensitivities of the constraints with respect to the uncertain parameters. The presented approximation does not guarantee robust feasibility, but path constraint violations are less likely to occur compared to the optimization using the nominal model solely. Two case studies are presented: a complex emulsion copolymerization process (DAE with 139 equations) and the penicillin formation (four differential equations and two algebraic equations). The results of both case studies show that, in contrast to the optimization in the nominal case, the multi-model approach does not violate the path constraints for different scenarios of the parametric uncertainty set.