摘要
鉴于标准粒子群算法在常压塔参数优化问题中不易收敛的不足,本文针对常压塔参数优化问题本身,在粒子速度更新公式中加入了化工先验知识抽象获得的"控制因子(ChemMat)",通过该"控制因子"调整粒子的速度方向,进而调整模型的输入变量,使得产品干点满足工艺要求。改进的粒子群算法应用于优化常压塔模型,以常压塔进料物流温度、气提蒸汽进料流量和常压塔操作参数共15个变量作为控制变量,在4个产品的干点值满足工艺要求条件下最大化轻油产量和最小化能量消耗量。经过仿真实验证明,与标准粒子群算法以及自适应惯性权值改进的粒子群算法相比,采用控制因子的粒子群算法能够在较短的迭代次数里获取优于当前状态的常压塔操作参数。在相同的迭代次数的条件下,采用控制因子的粒子群算法能够搜寻到更优的操作参数。
Since the convergence of standard PSO is insufficient with atmospheric tower parameters optimization problem.A control factor（ChemMat）obtained by chemical prior knowledge is added to the formula of updating particle＇s velocity.It is used to adjust the partials＇ position to improve model of input variables,so that the dry point of ASTM of four products can meet the technological requirements.The improved PSO is used to optimize atmospheric tower parameter,by feed temperature of atmospheric tower,feed flow of steam and operation parameters of atmospheric tower such 15 variables as the control variables,and in the condition of the dry point of ASTM of four products meeting the technological requirements,the biggest naphtha,kerosene,diesel output and minimum energy consumption can be obtained.Through the simulation experiments,compared with the standard PSO,the improved PSO can obtain operating parameters which is better than the current state of atmospheric in shorter time.And in the same iterations,the improved PSO can obtain better operation parameters.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2010年第10期1345-1348,共4页
Computers and Applied Chemistry
基金
国家杰出青年科学基金(60625302)
国家高技术研究发展计划(863)(2008AA042902)
上海市科技攻关项目(08DZ1123100)
高等学校学科创新引智计划(B08021)
上海市重点学科建设项目资助(B504).
