基于改进人工鱼群算法的化工过程优化

发布时间：2018-04-10 21:18

本文选题：智能优化 + 人工鱼群算法　；参考：《北京化工大学》2015年硕士论文

【摘要】：化工过程存在很多待优化问题,但是往往都比较复杂。并且随着化工过程规模的日益扩大,目标函数变得愈加复杂,同时自变量和约束条件的数目也更多,利用以往的优化算法很难解决。人工鱼群算法(AFSA)是智能计算研究领域的一个新方向,不仅为复杂系统优化问题的解决提供了一种新理论,而且给化工过程优化问题的解决提供了一种新思路。围绕AFSA,本文主要开展了以下工作：(1)简述了化工优化过程的发展历程和所面临的巨大挑战,最优化问题的概念、分类,优化算法的发展概况以及目前常见的几种智能优化算法等。(2)介绍了基本AFSA,包括算法的提出背景,人工鱼的结构,及其基本行为描述、行为选择和算法的寻优原理等。同时,对AFSA的研究概况做了一些综述。(3)通过若干个实验,分析了基本AFSA当中各个参数对算法的影响。针对AFSA的不足之处,对其进行分析改进,提出了一种可以自动地获取虚拟人工鱼视觉感知范围与前进步长的改进人工鱼群算法(IAFSA)。再利用经典函数测试改进的人工鱼群算法,证明其有效性和实用性。(4)把改进后的人工鱼群算法应用于化工过程优化当中。对化工生产中的换热管网(HEN)优化问题与丁烯烷化(BA)过程优化问题进行了模型简化,并且建立它们相应的数学模型。成功的对换热管网优化问题和丁烯烷化过程优化问题进行了优化,提高了效益。
[Abstract]:There are many optimization problems in chemical process, but they are often complicated.With the increasing scale of chemical process, the objective function becomes more and more complex, and the number of independent variables and constraints is more, so it is difficult to solve the problem by using the previous optimization algorithm.Artificial Fish Swarm algorithm (AFSA) is a new direction in the field of intelligent computing. It not only provides a new theory for solving the optimization problems of complex systems, but also provides a new way to solve the optimization problems of chemical processes.In this paper, the following work is mainly carried out around AFSA: 1) briefly describing the development of chemical optimization process and the great challenges it faces, the concept and classification of optimization problems,This paper introduces the basic AFSAs, including the background of the proposed algorithm, the structure of the artificial fish, the basic behavior description, the behavior selection and the optimization principle of the algorithm.At the same time, a review of the research of AFSA is given. (3) through several experiments, the influence of each parameter in the basic AFSA on the algorithm is analyzed.Aiming at the shortcomings of AFSA, this paper analyzes and improves it, and proposes an improved artificial fish swarm algorithm which can automatically acquire the visual perception range and advance time of virtual artificial fish.Then the improved artificial fish swarm algorithm is tested by classical function, which proves its validity and practicability. 4) the improved artificial fish swarm algorithm is applied to the optimization of chemical process.In this paper, the heat transfer pipe network optimization problem and the butene alkylation process optimization problem in chemical production are simplified and their corresponding mathematical models are established.The optimization problem of heat transfer pipe network and alkylation process of butene was successfully optimized and the benefit was improved.
【学位授予单位】：北京化工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TQ015.9;TP18

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