基于LCPSO算法的相稳定性判别  

Linear constraint particle swarm optimization algorithm for phase stability analysis

作　　者：成飙[1] 郑启富[2] 陈德钊[1] 贺益君[1] 

机构地区：[1]浙江大学化学工程与生物工程学系,浙江杭州310027 [2]浙江工业大学浙西分校化工系,浙江衢州324000

出　　处：《化工学报》2007年第12期2957-2963,共7页CIESC Journal

基　　金：国家自然科学基金项目(20276063)~~

摘　　要：相稳定性判别为相平衡计算的基本课题,常采用Gibbs自由能曲面与切平面的距离函数(TPDF)最小化方法求解。对于强非理想体系,或在高压条件下,其TPDF表现出复杂形态,有平凡解和多极值,传统方法难以求得满足约束的全局最小值,从而导致判别失误。粒子群算法(PSO)虽有全局优化性能,但也会陷于局部极小,且缺少约束处理机制。为此,分析了PSO内在蕴含的线性特点,在种群初化、粒子运动等环节提出应对策略,构建线性约束粒子群算法(LCPSO),确保种群在可行空间内搜索。还增设调变参数、局部加速等措施,以兼顾算法的全面探测和细化挖掘的能力,提高其全局优化效能。经多个实例的测试表明,LCPSO适用面广,既可用于超额自由能、状态方程等各类热力学模型,又能克服混合模型一阶不连续的困难,应用范围从液液相分裂拓展到汽液液相分裂。与确定性全局算法IN/GB相比,LCPSO速率高,效果好,尤对多元体系更具优势。The tangent plane distance（TPD）method is the most popular method for phase analysis,which needs to find the global minimum value of TPD function.Because the Gibbs free energy function has multiple minima caused by the strong nonideality and the optimization problem always has a tiresome trivial solution,local optimization algorithm will not converge to global minimum in general,which leads to wrong judgment for phase stability.Linear constraint particle swarm optimizatioo algorithm is a global optimization dealing with constraints of non-negative and affine real space.It generates initial particle population in a feasible space and utilizes the intrinsic linear operation,which makes the whole population always evolves in the feasible space.Considering the characteristics of tangent plane distance method,local acceleration and stopping criteria were devised,which increases the efficiency of algorithm.This method can be applied to any thermodynamic model,including activity coefficient model and EOS model,and this work extends phase analysis from liquid-liquid phase splitting to liquid-liquid-vapor phase splitting.

关 键 词：相稳定性 相平衡 全局优化 粒子群 约束 

分 类 号：TQ013.1[化学工程]