摘要
实际化工过程反应系统通常由若干个相互关联的平推流反应器串级构成,要建立其机理模型,并估计其动力学参数,就必须反复求解大规模非线性微分方程组,计算代价较高。针对智能优化算法在求解昂贵优化问题时计算成本过高,而工业过程模型往往只需要找到一组满意解即可的特点,提出了一种基于改进状态转移算法的串级平推流反应器动力学参数估计方法。该算法在保留标准状态转移算法全局寻优能力和快速收敛能力的同时,利用对立算子优化初始种群,并根据误差阈值判定满意解中止条件,可有效节约计算时间。以实际炼油企业的加氢裂化装置为研究对象,采用所提方法对其反应系统的动力学参数进行估计,仿真结果表明了所提方法的有效性。
The actual chemical process reaction system usually consists of several interconnected cascaded reactor.To establish its mechanism model and estimate its dynamic parameters,it is necessary to repeatedly solve largescale nonlinear differential equations.The calculation is very expensive.Since this parameter optimization process involves solving large sets of differential equations,it is very time consuming.According to the problem that intelligent optimization algorithm always needs enormous computing resource while a satisfied solution is acceptable for an industrial application,an improved state transfer algorithm(STA)is proposed to estimate the kinetics parameters of the cascaded plug flow reactors model.This method uses an opposite operator to initialize the start status of STA,and a tolerable error threshold to break the optimization process.This improvement is helpful since much computing time is saved while the global search capability and fast convergence capability of standard STA are retained.Simulation study,whose target is optimization the kinetics parameters of the cascaded plug flow reactors of an industrial hydrocracking process,verified the effectiveness and superiority of this proposed method.
作者
薛永飞
王雅琳
孙备
李钱钟
孙家舟
XUE Yongfei;WANG Yalin;SUN Bei;LI Qianzhong;SUN Jiazhou(School of Information Science and Engineering,Central South University,Changsha 410083,Hunan,China;Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)
出处
《化工学报》
EI
CAS
CSCD
北大核心
2019年第2期607-616,共10页
CIESC Journal
基金
国家自然科学基金重大项目(61590921)
国家自然科学基金青年项目(61603418)
关键词
串级平推流反应器
反应动力学
动力学模型
参数估值
状态转移算法
昂贵优化
满意解
cascaded plug flow reactors
reaction kinetics
kinetic modeling
parameter estimation
state transfer algorithm
expensive optimization
satisfied solution
