Based on NSGA-II and MOPSO optimization of a chemical industrial park

Under the background of the increasing importance of environmental protection and resource efficiency, chemical parks are facing great optimization challenges. This study aims to solve this problem, focusing on the optimization of the energy system of chemical parks, in order to balance the dual goals of cost and pollutant emissions, and propose an optimization method for chemical parks based on NSGA-II (Non-dominated Sorting Genetic Algorithm II) and MOPSO (Multi-Objective Particle Swarm Optimization). Firstly, the load control model of chemical park is established by the fertilizer industry, and then, the two multi-objective optimization algorithms of NSGA-II and MOPSO are combined to find Pareto's frontier solution sets in the multi-dimensional search space, which include the optimal solution for the two goals of cost and carbon emissions. Finally, simulation experiments are carried out using the actual chemical park data to verify the effect of the proposed method in solving complex chemical park energy optimization problems. The results show that this method can achieve significant results in the optimization problem of chemical parks. By considering both cost and environmental impact, a more feasible configuration of the capacity can be found, as well as better economic and environmental benefits.