To accelerate data-driven studies for various optimization applications in chemical engineering, a comprehensive machine learning aided multi-objective optimization and multi-criteria decision making (abbreviated as ML aided MOO-MCDM) framework is proposed in the present paper. The framework comprises a total of seven steps; firstly, study the application and its input-output datasets to identify objectives, constraints and required ML models; secondly, select ML model(s) for some or all objectives and constraints; thirdly, train the chosen ML model(s), including finding optimal hyperparameter values in each of them using an advanced/global optimization algorithm; fourthly, formulate the MOO problem for the application; fifthly, select a MOO method and develop/test the program; sixthly, solve the formulated MOO problem with the developed/tested MOO program many times and review the Pareto-optimal solutions obtained; lastly, perform MCDM using several methods and choose one Pareto-optimal solution for implementation. The proposed ML aided MOO-MCDM framework is useful for process design and operation of chemical and related processes. It is shown to be beneficial for the optimization of two complex chemical processes, which are supercritical water gasification process aiming for H₂-rich syngas with lower greenhouse gas emissions, and combustion process in a power plant targeting for higher energy output and lower pollution of the environment.

为了加速化学工程中各种优化应用的数据驱动研究，本文提出了一种全面的机器学习辅助多目标优化和多准则决策（简称 ML 辅助 MOO-MCDM）框架。该框架总共包括七个步骤；首先，研究应用程序及其输入输出数据集，以确定目标、约束和所需的机器学习模型；其次，为部分或全部目标和约束选择 ML 模型；第三，训练选择的 ML 模型，包括使用高级/全局优化算法在每个模型中找到最佳超参数值；第四，为应用制定MOO问题；第五，选择MOO方法并开发/测试程序；第六，用已开发/测试过的 MOO 程序多次解决制定的 MOO 问题，并审查获得的帕累托最优解；最后，使用多种方法执行 MCDM，并选择一种 Pareto 最优解进行实施。提出的 ML 辅助 MOO-MCDM 框架可用于化学和相关过程的过程设计和操作。它被证明有利于优化两个复杂的化学过程，即针对 H 的超临界水气化过程_{具有较低温室气体排放的富2-}富合成气，以及发电厂中的燃烧过程，目标是提高能源输出和降低对环境的污染。