Hybrid modeling and optimization of acetylene hydrogenation process

The mathematical model of the acetylene hydrogenation reactor established by traditional single modeling method does not meet the needs of industrial practical applications in predictive performance. This paper proposes a mechanism and neural network nesting modeling method, which fully utilizes the mechanism model. It makes full use of mass and energy balance information in mechanism model to reduce the degree of constraint violation of the neural network model, which can describe the process characteristics of industrial reactor well. The optimization problem which targets the operational profits as the objective function is studied basing on the hybrid model. The main decision variables include several key parameters, such as the reactor feed hydrogen-alkyne ratio, the feed temperature, and the two-stage reactor operating cycle and many more. For the long-term operation of the reactor, processing capacity of the reactor will decrease due to the decreased catalyst activity, and an improved optimizing strategy is proposed by adjusting the hydrogen-alkyne ratio as well as the reaction temperature simultaneously. The sequence method is used to discretize the operating cycle of the reactor. The two-stage difference algorithm is improved by introducing differential mutation strategy and potential solution alternative strategy. Then the optimization problem is solved by combining the incremental coding method with the improved two-stage difference algorithm. And the results confirm the effectiveness. Furthermore the optimal operating cycle and operating strategy of the reactor are given. © All Right Reserved.