Optimization of operating conditions for industrial p-xylene oxidation process based on a novel adaptive immune genetic algorithm

The p-xylene (PX) oxidation process is of great industrial importance because of the strong global polyester fiber demand. Usually, the optimization of operating conditions for this process is specified in terms of the combustion loss caused by the side reactions. When the optimization objective only refers to the economical performance of the p-xylene oxidation process, the product of the side reactions become the most crucial variables of the objective function. However, most of the research only considered the main reactions of the p-xylene oxidation process while neglecting the side reactions. The formation of main side product CO_x was estimated by neural network. This not only neglected the effect of the side reactions but cannot be applied at other conditions. An industrial p-xylene oxidation model comprising the main reactions and side reactions based on the radical reaction mechanism was developed. A modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases was applied to optimize the operating conditions for the industrial p-xylene oxidation model. The results showed the efficiency of the proposed approach and the benefit of the optimal operating conditions.