Production and Operation Optimization of Hydrocracking Process Based on Dual Mutation Evolution Algorithm

The hydrocracking process converts heavy oil into high-quality finished oil during petroleum refining, with high conversion rates and good selectivity. Accurate and timely production operation optimization is crucial for the production efficiency and product quality of hydrocracking. Heuristic evolutionary algorithms have been widely used due to their advantages of simple operation and high performance. However, traditional evolutionary algorithms carry too much mutation information due to individual mutation crossover operations and retain too little genetic information, which limits the algorithm's optimization ability and convergence speed. This article proposes a dual mutation improvement strategy for the traditional evolutionary algorithm family, which adjusts the dimensions beyond the range in the individual and introduces new mutation operations to enhance the inheritance ability of the population's genetic information. Finally, the proposed method was applied to the optimization of an actual hydrocracking production operation, and the results showed that the improved evolutionary algorithm family has better optimization ability and faster convergence speed.