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Nonlinear Optimization Strategies for Process Separations and Process Intensification
Chemie Ingenieur Technik ( IF 1.5 ) Pub Date : 2020-05-11 , DOI: 10.1002/cite.202000014
Lorenz T. Biegler 1
Affiliation  

Advanced nonlinear programming (NLP) strategies based on equation‐oriented (EO) process models are leading to significant improvements in computer‐aided process engineering. The EO paradigm allows the development of large, integrated optimization platforms that expand the scope of continuous optimization tasks in process engineering. In particular, these platforms deploy significantly faster NLP strategies than in commercial simulation tools. Moreover, they exploit exact derivatives and system structure in order to consider much larger and more challenging systems. Finally, they allow the incorporation of much more general models, such as multi‐level optimization and complementarity constraints. For process optimization this allows the treatment of extended models for complex phase equilibrium and process separations. These advances facilitate the optimization of novel integrated systems that arise in process intensification. Several separation case studies are presented that illustrate these optimization concepts and demonstrate their effectiveness for hybrid membrane/distillation separations and reactive distillation systems that typify novel systems in process intensification.

中文翻译:

流程分离和流程强化的非线性优化策略

基于面向方程式(EO)的过程模型的高级非线性编程(NLP)策略正导致计算机辅助过程工程的重大改进。EO范式允许开发大型的集成优化平台,从而扩大过程工程中连续优化任务的范围。特别是,这些平台部署的NLP策略要比商业仿真工具快得多。此外,他们利用精确的导数和系统结构来考虑更大,更具挑战性的系统。最后,它们允许合并更多通用的模型,例如多级优化和互补性约束。对于过程优化,这允许处理扩展模型以实现复杂的相平衡和过程分离。这些进步促进了优化过程中出现的新型集成系统的优化。提出了几个分离案例研究,这些案例说明了这些优化概念,并证明了它们对混合膜/蒸馏分离和反应蒸馏系统的有效性,这些系统代表了过程强化中的新型系统。
更新日期:2020-05-11
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