文献类型：专利

英文题名：Optimization operation method of industrial device PX for oxidizing reaction process of dimethylbenzene comprises optimizing parameters of operation conditions based on PX liquid-phase catalytic reaction kinetic model and acetic acid

作者：QIAN F;ZHONG W;DU W;QI R

机构：[1]UNIV EAST CHINA SCI&TECHNOLOGY

申请号：CN101811961-B

公开日：2012-12-12

语种：英文

收录：DERWENT

摘要：NOVELTY - An optimization operation method of industrial device PX comprises (A) determining multi-objective optimized target function and optimized variable of PX oxidizing reaction process of industrial device; (B) setting parameters of multi-objective evolutionary algorithm based on elite algorithm and individual movement; collecting data of the industrial reactor; and (C) optimizing parameters of operation conditions based on PX liquid-phase catalytic reaction kinetic model and acetic acid, PX combustible loss model, and multi-objective evolutionary algorithm. USE - Method is used for optimization operation of industrial device PX for oxidizing reaction process of dimethylbenzene. ADVANTAGE - The method can optimize conditions of oxidizing reaction process of dimethylbenzene, provides evidence to improve manufacturing technique, and saves energy and reduces energy consumption. DETAILED DESCRIPTION - Optimization operation method of industrial device PX comprises (A) determining multi-objective optimized target function and optimized variable of PX oxidizing reaction process of industrial device; (B) setting parameters of multi-objective evolutionary algorithm based on elite algorithm and individual movement; collecting data of the industrial reactor; and (C) optimizing parameters of operation conditions based on PX liquid-phase catalytic reaction kinetic model and acetic acid, PX combustible loss model and multi-objective evolutionary algorithm based on elite algorithm and individual movement until reaching optimizing target. The step (C) includes: (A) randomly generating corresponding single target subpopulation to each optimized target; (B) counting target function of all units of each single target subpopulation, comparing all units of single target subpopulation based on Pareto optimal solution definition to obtain initial Pareto optimal solution and memorizing it in elite population; (C) selecting best unit from union set of single target subpopulation and Pareto optimized elite population as parent unit of subpopulation and repeating selecting process until selecting all parent units from single target subpopulation for first target of multi-objective optimized problems; (D) using cross and mutation operator to parent units of single target subpopulation to obtain single target sub population of the circle; (E) using cross and mutation operator to Pareto elite population to generate new units of sub population; (F) evolving units of single target subpopulation of the circle and elite population based on Pareto optimizing concept to obtain new units of subpopulation which is compared with units of Pareto optimizing elite population of former circle to obtain Pareto optimizing elite population of the circle; and (G) repeating above steps until reaching optimizing target, where Pareto optimizing elite population is optimization solution. The target function is minimum 4-CBA content based on PX combustible loss model, minimum acetic acid combustible loss and minimum PX combustible loss based on acetic acid and PX combustible loss model.