量子优化的氯碱电解多目标控制系统理论研究

Theoretical study on multi-objective control system of chlor-alkali electrolysis based on quantum optimization

针对氯碱电解槽的节能降耗,增加其生产效率,根据氯碱电解的复杂控制过程,设计一种氯碱电解多目标控制系统。首先,基于历史数据分析氯碱电解槽的主要影响因子,在此基础上建立氯碱电解电流效率和直流电耗的Elman神经网络预测模型;然后,利用BP神经网络控制器来提高控制精度和动态跟踪精度,并用量子优化方法对BP神经网络控制器进行优化;最后,利用Matlab进行仿真,并与改进型非劣分类遗传算法(NSGA-Ⅱ)作对比,结果表明文中所提的控制策略有效,可以为氯碱生产过程提供优化操作指导。

The production efficiency is increased due to the energy saving and consumption reduction of chlor-alkali electrolysis cell.A multi-target control system for chlor-alkali electrolysis is designed to deal with the complicated control process of chlor-alkali electrolysis.The main influence factors of chlor-alkali electrolysis cell are analyzed based on historical data.On this basis,an Elman neural network prediction model for chlor-alkali electrolysis current efficiency and DC power consumption is established,and then BP neural network controller is used to improve control precision and dynamic tracking accuracy.The quantum optimization method is adopted to optimize the BP neural network controller.The simulation is carried out with Matlab.It is compared with the improved non-inferior classification genetic algorithm(NSGA-Ⅱ).The results show that the control strategy of the system proposed in this paper is effective and can provide optimal operation guidance for the chlor-alkali production process.