基于粒子群优化算法的双混合制冷剂液化工艺参数优化

Optimization of parameters of dual mixed refrigerant liquefaction process based on Particle Swarm Optimization algorithm

双混合制冷剂(DMR)液化工艺中,冷剂组分和工艺参数复杂且相互影响,可利用算法对其优化,从而降低工艺能耗。以比功耗为目标函数,选择粒子群优化(PSO)算法对冷剂流量、压力等工艺参数进行了全局优化。结果表明,PSO算法优化效果相对较佳,优化后工艺的比功耗降低至0.2639 kW·h/kg,低于相关文献报道,也低于其他算法的优化结果;换热器换热效率提高,且可通过多级节流进一步减小局部换热温差;总?损失为19590 kW,相比教学自学优化(TLSO)算法降低了10.79%,[火用]效率为43.22%,其中压缩机的?损失最高。

In the dual mixed refrigerant(DMR) liquefaction process, the refrigerant components and process parameters are complex and affect each other. The algorithm can be used to optimize them, so as to reduce the process energy consumption. Taking the specific power consumption as the objective function, Particle Swarm Optimization(PSO) algorithm was selected to globally optimize the process parameters such as refrigerant flow and pressure. The results show that the optimization effect of PSO algorithm is relatively good, and the specific power consumption of the optimized process is reduced to 0.2639 kW·h/kg, which is lower than that reported in the references and the optimization results of other algorithms. The heat exchange efficiency of the heat exchanger is improved,and the local heat exchange temperature difference can be further reduced through multi-stage throttling. The total exergy loss is 19590 kW,which is reduced by 10.79% compared with the teaching-learning self-study optimization(TLSO) algorithm, and the exergy efficiency is 43.22%. Among the main equipments, the exergy loss of compressor is the highest.