Owing to the complex physical and chemical reactions, optimization of the coal gasification process requires a large computational cost. In chemical engineering design and optimization, the surrogate model is often used to assist the evolutionary algorithm (EA) in solving computationally expensive problems. In this paper, a dynamic model management strategy based on adaptive surrogate selection (ASS) is proposed to identify an appropriate surrogate model to assist the particle swarm optimization (PSO) algorithm faced with complex problems. In ASS, based on the given dataset, a right surrogate model was selected from the diversity model library by adopting the minimum root mean square error and K-fold cross-validation before assisting PSO to identify an optimal solution, and reselected constantly with the addition of a new sample. Based on the ASS, in addition to the adaptive global model, the local model is introduced and selected adaptively to refine the optimal solution more rapidly, then switched dynamically with the global model. The most uncertain sample was also considered for searching the unexplored region and escaping from the local optimum. Comparison of the specific surrogate EAs and three other state-of-the-art surrogate-assisted EAs revealed that the proposed strategy significantly improved the optimization performance of PSO and the effective syngas yield of the coal gasification process.

由于复杂的物理和化学反应，煤气化过程的优化需要大量的计算成本。在化学工程设计和优化中，代理模型通常用于协助进化算法 (EA) 解决计算量大的问题。在本文中，提出了一种基于自适应代理选择（ASS）的动态模型管理策略，以识别合适的代理模型，以协助粒子群优化（PSO）算法面临复杂的问题。在ASS中，基于给定的数据集，采用最小均方根误差和K折交叉验证从多样性模型库中选择一个正确的代理模型，然后辅助PSO确定最优解，并不断地重新选择添加的新样本。基于 ASS，除了自适应全局模型外，还引入和自适应选择局部模型以更快地细化最优解，然后与全局模型动态切换。还考虑了最不确定的样本来搜索未开发区域并逃离局部最优。特定替代 EA 和其他三种最先进的替代辅助 EA 的比较表明，所提出的策略显着提高了 PSO 的优化性能和煤气化过程的有效合成气产率。还考虑了最不确定的样本来搜索未开发区域并逃离局部最优。特定替代 EA 和其他三种最先进的替代辅助 EA 的比较表明，所提出的策略显着提高了 PSO 的优化性能和煤气化过程的有效合成气产率。还考虑了最不确定的样本来搜索未开发区域并逃离局部最优。特定替代 EA 和其他三种最先进的替代辅助 EA 的比较表明，所提出的策略显着提高了 PSO 的优化性能和煤气化过程的有效合成气产率。