Optimization of chemical steady-state process simulation parameters based on a particle swarm optimization algorithm

Chemical process simulation has been widely used in the petrochemical industry. This has been the main means of process optimization and aided design. The process parameters in a chemical process are diverse and complicated. It is difficult for traditional optimization methods to achieve global optimization by sensitivity analysis and optimization of a small number of key parameters. Therefore, an optimization method for simulating operating parameters of chemical processes based on particle swarm optimization algorithm is proposed in the present paper. The natural gas decarbonization process is chosen as the research object, the process simulation and optimization algorithm are coupled using Aspen HYSYS software. Combined with the knowledge of the process mechanism, the optimization of operation parameters of the natural gas decarbonization steadystate process simulation based on a particle swarm optimization algorithm has been achieved. Under the condition that the product meets the process requirements, and the controllable operation parameters that have a great influence on the process are used as the decision variables, the operation parameters of a 5.8×10⁶ m³/d natural gas purification unit are optimized by taking the maximum decarbonization rate, the minimum operation cost of the unit as the objective function. The optimization results show that fewer plates in the absorption tower and regeneration tower can meet the needs of acid gas removal requirements. Under the condition that the each tray is in a good operating state, the reflux ratio of the regeneration tower is reduced compared with the original process, and the gas-liquid phase load is also reduced to a certain extent, resulting in a decrease of the reboiler load. The temperature of the lean amine liquid into the absorption tower is lower than the original process, so that the positive reaction degree of CO₂ with the alcohol amine liquid is increased, and the increased absorption driving force slows down the corrosion of the equipment. The pressure in the absorption tower is increased compared with the original process, which increases the mass transfer driving force in the tower and the purification of the gas. Based on the particle swarm optimization algorithm for the natural gas decarbonization process, the carbon dioxide content in the purified gas is reduced from 0.16 mol% to 0.05 mol%, and the annual energy consumption cost is reduced by about 13%. The method proposed in the present work can find the global optimal operation scheme quickly and automatically without human involvement, and can be flexibly extended to the process optimization of various industrial processes.