The fluctuation of decision parameters will deviate from the optimal decision, which will have significant impact on the optimization performance of chemical processes. To reduce optimization performance deterioration caused by fluctuation of decision parameters in chemical processes, a multi-objective robust optimization scheme is developed to assess performance robustness. In addition, based on the model that maps decision parameters to objective performance through neural network, a new robustness evaluation metric is created as the fitness value of the multi-objective evolutionary algorithm (for improving the strength Pareto evolutionary algorithm (SPEAII)) to elaborate the relationship between robustness and fluctuation. The efficacy of the proposed method is verified with HCN production process application by comparing with genetic algorithm (GA) and weighted single-objective robust optimization.

决策参数的波动将偏离最佳决策，这将对化学过程的优化性能产生重大影响。为了减少由于化学过程中决策参数波动而导致的优化性能下降，开发了一种多目标鲁棒优化方案来评估性能鲁棒性。另外，基于通过神经网络将决策参数映射到目标性能的模型，创建了一个新的鲁棒性评估指标，作为多目标进化算法的适用性值（用于改进强度帕累托进化算法（SPEAII）），鲁棒性和波动之间的关系。