Chemical plant design and optimisation have proven challenging due to the complexity of these real-world systems. The resulting complexity translates into high computational costs for these systems' mathematical formulations and simulation models. Research has illustrated the benefits of using machine learning surrogate models as substitutes for computationally expensive models during optimisation. This paper extends recent research into optimising chemical plant design and operation. The study further explores Surrogate Assisted Genetic Algorithms (SA-GA) in more complex variants of the original plant design and optimisation problems, such as the inclusion of parallel and feedback components. The novel extension to the original algorithm proposed in this study, Surrogate Assisted NSGA-\Romannum{2} (SA-NSGA), was tested on a popular literature case, the Pressure Swing Adsorption (PSA) system. We further provide extensive experimentation, comparing various meta-heuristic optimisation techniques and numerous machine learning models as surrogates. The results for both sets of systems illustrate the benefits of using Genetic Algorithms as an optimisation framework for complex chemical plant system design and optimisation for both single and multi-objective scenarios. We confirm that Random Forest surrogate assisted Evolutionary Algorithms can be scaled to increasingly complex chemical systems with parallel and feedback components. We further find that combining a Genetic Algorithm framework with Machine Learning Surrogate models as a substitute for long-running simulation models yields significant computational efficiency improvements, 1.7 - 1.84 times speedup for the increased complexity examples and a 2.7 times speedup for the Pressure Swing Adsorption system.
翻译:由于这些现实世界系统的复杂性,化学厂设计和优化已证明具有挑战性。由此产生的复杂性转化为这些系统数学配方和模拟模型的高计算成本。研究表明了在优化期间使用机器学习代孕模型替代计算昂贵模型的好处。本文扩展了最近对优化化工厂设计和操作的研究。本研究进一步探讨了原厂设计和优化的复杂变体中的代金辅助遗传变体(SA-GA)和许多机械学习模型,如纳入平行和反馈组件。本研究中提议的原算法的新型扩展值为原算法的计算成本,Surrogate 辅助NSGA-\Romannum{2}(SA-NSGA)模型,在优化期间,利用机器学习代孕代金模型替代成本模型。我们进一步进行了广泛的实验,比较了各种超重精密的遗传化精选法化技术以及许多机器学习模型作为代孕模型。两种系统的结果都说明了使用遗传变精精精法改进模型的效益。在本项研究中,Surro Sloverial-rial-ligalimalimal imal-imalimalimal-imalimation Procial-de Procial-laphislation 设计和不断更新的系统可以进一步确认一个比重的系统,从而确认一个精化的系统,可以进一步的系统。