Multi-objective optimisation with hybrid machine learning strategy for complex catalytic processes
Multi-objective optimisation with hybrid machine learning strategy for complex catalytic processes
复制标题
针对复杂催化过程的混合机器学习策略的多目标优化
DOI：
10.1016/j.egyai.2021.100134
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Tai X
中科院分区：
文献类型：
--
作者：
Tai X
催化化学过程，如加氢裂化、气化和热解在可再生能源和净零转型中发挥着至关重要的作用。由于操作过程中的复杂和非线性行为，催化化学过程需要一个强大的建模工具来预测和优化智能操作，快速的绿色工艺路线发现和快速的工艺设计。然而，由于缺乏有效的建模和优化工具箱，挑战仍然存在，这不仅需要精确的分析，还需要快速的优化。在这里，我们提出了一种混合机器学习策略，将基于物理的连续统集总动力学模型嵌入到数据驱动的人工神经网络框架中。将该混合模型作为多目标优化的替代模型，并在加氢裂化过程的基准测试中进行了验证。结果表明，与基于物理的仿真结果相比，该混合代理模型的均方误差小于0.01。然后将该混合模型与非主导排序遗传算法（NSGA-II）相结合，作为替代模型来评估和优化加氢裂化过程的收率和选择性。多目标优化的帕累托前线能够识别目标函数之间的权衡曲线，这对工艺设计决策至关重要。我们的工作表明，在各种复杂的催化化学过程中，采用混合机器学习策略作为代理模型是一种很有前途的方法，可以实现精确的计算和快速的优化。
Catalytic chemical processes such as hydrocracking, gasification and pyrolysis play a vital role in the renewable energy and net zero transition. Due to the complex and non-linear behaviours during operation, catalytic chemical processes require a powerful modelling tool for prediction and optimisation for smart operation, speedy green process routes discovery and rapid process design. However, challenges remain due to the lack of an effective modelling and optimisation toolbox, which requires not only a precise analysis but also a fast optimisation. Here, we propose a hybrid machine learning strategy by embedding the physics-based continuum lumping kinetic model into the data-driven artificial neural network framework. This hybrid model is adopted as the surrogate model in the multi-objective optimisation and demonstrated in the benchmarking of a hydrocracking process. The results show that the novel hybrid surrogate model exhibits the mean square error less than 0.01 by comparing with the physics-based simulation results. This well-trained hybrid model was then integrated with non-dominated-sort genetic algorithm (NSGA-II) as the surrogate model to evaluate and optimise the yield and selectivity of the hydrocracking process. The Pareto front from the multi-objective optimisation was able to identify the trade-off curve between the objective functions which is essential for the decision-making during process design. Our work indicates that adopting the hybrid machine learning strategy as the surrogate model in the multi-objective optimisation is a promising approach in various complex catalytic chemical processes to enable an accurate computation as well as a rapid optimisation.
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