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丙烷预冷混合制冷液化流程(C3-MR)在液化天然气(LNG)生产中应用最广。该流程的优化属于非线性问题,优化结果受到过程变量和算法的影响。本文基于HYSYS软件模拟,对C3-MR流程用MATLAB嵌入粒子群算法(PSO)优化制冷剂组分、流量以及流程压力以降低过程能耗。研究结果表明,对C3-MR流程使用PSO算法优化迭代20次便收敛,优化后理论能耗低于公开文献报导的序列二次规划(SQP)和BOX方法的结果。Propane pre-cooled mixed refrigerant (C3-MR) process is the most widely used process for LNG production. Nonlinearly optimization of the process depends on process variables and algorithm. The refrigerant components, flow rate and process pressure of the C3-MR process were optimized by HYSYS with Particle Swarm Optimization (PSO) embedded in MATLAB. Optimized parameters in PSO converge after 20 iterations for the C3-MR process. The resultant theoretic energy consumption is lower than that obtained by the sequential quadratic programming method (SQP) and BOX method in reported literatures
Propane Precooled Hybrid Refrigeration Liquefaction Processes (C3-MR) are the most widely used in the production of liquefied natural gas (LNG). The optimization of this process is nonlinear, and the optimization results are affected by process variables and algorithms. Based on the HYSYS software simulation, the embedded Particle Swarm Optimization (PSO) for C3-MR process is used to optimize the refrigerant composition, flow rate and process pressure to reduce the process energy consumption. The results show that the optimal energy consumption of the C3-MR flow is 20 times lower than that of the SQP and BOX methods. Propane pre-cooled mixed refrigerant (C3-MR) process is the most widely used process for LNG production. Nonlinearly optimization of the process depends on process variables and algorithm. The refrigerant components, flow rate and process pressure of the C3-MR process were optimized by HYSYS with Particle Swarm Optimization (PSO) embedded in MATLAB. Optimized parameters in PSO converge after 20 iterations for the C3-MR process. The resulting theoretic energy consumption is lower than that obtained by the sequential quadratic programming method (SQP) and BOX method in reported literatures