粒子群算法求解边值固定的化工动态过程优化问题

针对化工过程系统优化中广泛存在着边值固定的动态优化问题,该问题的求解数学上还没有有效的方法,现今的方法之一是将问题转化为多目标优化问题.本文在粒子群优化(PSO)算法的基础上,提出在PSO算法中加入惩罚项,同时对局部极值与全局极值作进一步的调整,使PSO算法适用于求多目标优化问题理想有效解,该算法对多目标问题起到边优化边求理想有效解的功效;即只用一步即可求理想有效解,这使得在求解速度上大为加快.最后将其用于间歇反应器的最佳反应温度边值固定动态优化控制的实际运用中,取得良好效果.     

基于神经网络模型的混沌优化及其应用

研究一种新型优化算法－混沌优化,提出加快解的疏敛速度和精度新方法,并与精确不可微罚函数结合来求解非线性约束优化问题。对不能用数学解析式精确表达的优化问题利用神经网络建模,在此基础上进行混沌搜索寻优。该方法应用于甲醛生产过程的稳态优化,获得较好的经济效益。     

换热器网络设备面积与清洗时序同步优化

表面结垢会严重影响换热器的传热效率,定期清洗是解决该问题的主要方式.针对以往换热器网络清洗时序优化方法中用于决策的整型变量较多而难以求解的问题,提出以换热器清洗的最大允许污垢热阻为优化变量,取代表示换热器是否清洗的二进制变量,将混合整数非线性规划问题转化成非线性规划问题,能够有效地减小问题规模,降低求解难度.优化过程中兼顾换热器网络的设计型与操作型问题,采用遗传/模拟退火算法同步优化换热器的面积与清洗时序.将该方法用于一个实例,所得年度总费用与文献基本一致,验证了该方法的有效性.     

列队竞争算法求解多产品间歇生产过程排序问题

由于组合爆炸特性,多产品厂的排序问题很难求解大规模甚至中等规模的问题.采用一种新的随机型优化搜索算法--列队竞争算法来对该问题进行求解,引入新的选择策略和变异方法.计算表明同已有的方法相比,该方法求解效率高、收敛速度快、使用简单方便,是一种求解多产品间歇过程排序问题的有效算法,为多目的厂间歇过程排序研究提供了新思路.     

基于CSTR的反应器网络综合双层优化算法

反应器网络综合问题一般都是复杂的非线性规划问题,在分析基于全混流反应器的反应器网络模型特点的基础上,提出了求解该模型的双层优化算法. 通过将反应器网络综合非线性规划问题分解为物流流量和反应器体积空间的线性优化和浓度空间的优化搜索问题,降低了所求解问题的规模和难度,同时利用全局优化算法进行浓度空间的优化搜索,提高了求得全局最优解的概率. 实例研究表明,双层优化算法可以更准确地给出最优的反应器网络结构以及网络中反应器的类型和大小.     

基于自适应粒子群优化算法的预测控制算法及其在加热炉支管平衡中的应用

多变量和输出受限系统的预测控制问题一般表现为一个不易直接求解的多变量且多约束的非线性动态规划问题.传统优化方法在解决此优化问题时,存在易收敛到非法解或局部极小、计算时间长以及对模型参数与初值依赖性强的缺点.提出了一种基于自适应粒子群优化的预测控制算法(APSO-DMC),采用自适应粒子群优化算法(APSO)作为模型预测控制的优化方法,在线实时求解最优控制律,从而有效地克服了传统优化方法的不足.将此算法应用于常减压装置加热炉支管温度平衡控制中,仿真试验结果显示了该方法的有效性.     

多目标过程系统优化的粒子群算法求解

化工领域的过程设计、生产控制、配方和计划等众多问题的数学模型,在考虑产品性能、单位成本、环境影响等诸多因素下,都是多目标优化问题;而求解多目标优化问题,目前还没有有效的方法;现今的做法是把多目标优化通过加权转化为单目标优化,再求解单目标优化问题,但这存在权数不易确定;还忽视了有效解集中存在一个其各目标的值与各目标的最优值距离最近的有效解的问题,称为理想有效解.理想有效解的求法一般分为两步,先求各目标的最优值、再求理想有效解,这将影响求解的速度;为此提出在PSO(粒子群优化)算法中加入惩罚项,同时对PSO算法中的个体极值与全局极值作调整,使PSO算法适用于求多目标优化问题理想有效解,该算法对多目标问题起到边优化边求理想有效解的功效;这使得在求解速度上加快.通过性能测试表明了算法的有效性,最后将算法用于求解多亚甲基多苯基多胺生产过程系统优化取得良好效果.     

基于简约空间序列二次规划算法和混合求导方法的精馏塔操作优化

以开放式方程建立精馏塔严格机理模型,对精馏塔的操作优化进行了研究.根据精馏塔操作优化命题自由度低、模型结构稀疏且导数难以解析求解的特点,提出了基于简约空间序列二次规划算法（SQP）和混合求导方法相结合的精馏塔操作优化计算方法.在该方法中优化命题采用简约空间SQP算法求解,求解过程中需要的导数信息采用解析表达和预处理的自动微分技术求取.计算结果表明,本文方法在计算效率上大大高于基于差分求导的标准SQP算法,有利于在线实时优化的实施.另外本文优化结果也表明,精馏塔操作优化对提高产品产量、提高综合经济效益具有明显作用.     

基于遗传算法和粒子群优化算法的电力系统无功优化

从数学的角度分析,电力系统无功优化是一个多变量、多约束、非连续性的混合非线性规划问题,因此,优化过程十分复杂.以减少有功网损为目标函数建立电力系统无功优化计算的数学模型,基于遗传算法和粒子群优化算法,提出一种新颖的混合策略来求解无功优化问题.IEEE 6和IEEE 14节点系统的仿真计算结果表明:与单一的遗传算法或粒子群优化算法相比,该混合策略在优化效果方面具有明显的优势.     

基于鲁棒模型优化的工艺设计与控制系统集成

采用一种基于鲁棒模型优化方法求解工艺设计与控制集成问题,其核心思想是采用参数不确定状态空间模型描述闭环系统的非线性行为,基于该模型以及二次李雅普诺夫函数可估计系统在外部扰动作用下过程变量变化的最大边界,同时也可测试系统的鲁棒稳定性。该方法避免了求解集成优化问题常用的动态优化方法,可将其转化成一个非线性规划问题。该方法应用于CSTR反应过程的集成优化设计中,结果表明在存在外部扰动时,不仅可以保证过程的经济性能和动态性能,也可保证系统的鲁棒稳定性。     

An efficient constraint handling method with integrated differential evolution for numerical and engineering optimization

Constrained optimization problems are very important as they are encountered in many engineering applications. Equality constraints in them are challenging to handle due to tiny feasible region. Additionally, global optimization is required for finding global optimum when the objective function and constraints are nonlinear. Stochastic global optimization methods can handle non-differentiable and multi-modal objective functions. In this paper, a new constraint handling method for use with such methods is proposed for solving equality and/or inequality constrained problems. It incorporates adaptive relaxation of constraints and the feasibility approach for selection. The recent integrated differential evolution (IDE) with the proposed constraint handling technique is tested for solving benchmark problems with constraints, and then applied to many chemical engineering application problems with equality and inequality constraints. The results show that the proposed constraint handling method with IDE (C-IDE) is reliable and efficient for solving constrained optimization problems, even with equality constraints.     

Efficient constraint handling scheme for differential evolutionary algorithm in solving chemical engineering optimization problem

This paper introduces a new constraint handling scheme developed for the differential evolutionary algorithm to solve constrained optimization problems. The developed approach uses a repair algorithm based on the gradient information derived from the equality constraint set to correct infeasible solutions. A dominance-based selection scheme is also applied to incorporate constraints into the objective function. To illustrate the developed algorithm and to compare its efficiency with other tradition method, several test problems and chemical engineering optimization problems are used. A traditional constraint handling technique is compared; both in terms of solution quality and the number of function evaluations required. The performance of our developed scheme compares favorably with traditional penalty function method. Our developed algorithm can effectively handle constraints encountered in chemical engineering optimization problems.     

A generalized framework for solving dynamic optimization problems using the artificial chemical process paradigm: Applications to particulate processes and discrete dynamic systems

The solution of optimal control problems (OCPs) becomes a challenging task when the analyzed system includes non-convex, non-differentiable, or equation-free models in the set of constraints. To solve OCPs under such conditions, a new procedure, LARES-PR, is proposed. The procedure is based on integrating the LARES algorithm with a generalized representation of the control function. LARES is a global stochastic optimization algorithm based on the artificial chemical process paradigm. The generalized representation of the control function consists of variable-length segments, which permits the use of a combination of different types of finite elements (linear, quadratic, etc.) and/or specialized functions. The functional form and corresponding parameters are determined element-wise by solving a combinatorial optimization problem. The element size is also determined as part of the solution of the optimization problem, using a novel two-step encoding strategy. These building blocks result in an algorithm that is flexible and robust in solving optimal control problems. Furthermore, implementation is very simple.The algorithm's performance is studied with a challenging set of benchmark problems. Then LARES-PR is utilized to solve optimal control problems of systems described by population balance equations, including crystallization, nano-particle formation by nucleation/coalescence mechanism, and competitive reactions in a disperse system modeled by the Monte Carlo method. The algorithm is also applied to solving the DICE model of global warming, a complex discrete-time model.     

模块环境下的filter-SQP用于过程优化

引言 20世纪70年代中期以来,经过许多学者的努力,SQP法成为求解非线性规划(NLP)问题最有效的方法之一.     

改进的膜计算仿生优化算法及在汽油调和中的应用

为提高膜计算仿生优化算法在求解流程工业复杂优化问题的计算性能,提出一种改进的膜计算仿生优化算法。该算法采用一个新的不确定性提取规则取代改进前的提取规则。4个有约束标准测试函数被用于检验该算法的计算性能,计算结果及对比显示了改进算法在鲁棒性和效率等方面优于改进前算法。改进算法应用于汽油调和优化问题,更高利润的配方及算法的计算效率证实了改进算法的优越性和实用性。     

PGS-COM: A hybrid method for constrained non-smooth black-box optimization problems: Brief review,novel algorithm and comparative evaluation

In the areas of chemical processes and energy systems, the relevance of black-box optimization problems is growing because they arise not only in the optimization of processes with modular/sequential simulation codes but also when decomposing complex optimization problems into bilevel programs. The objective function is typically discontinuous, non-differentiable, not defined in some points, noisy, and subject to linear and nonlinear relaxable and unrelaxable constraints. In this work, after briefly reviewing the main available direct-search methods applicable to this class of problems, we propose a new hybrid algorithm, referred to as PGS-COM, which combines the positive features of Constrained Particle Swarm, Generating Set Search, and Complex. The remarkable performance and reliability of PGS-COM are assessed and compared with those of eleven main alternative methods on twenty five test problems as well as two challenging process engineering applications related to the optimization of a heat recovery steam cycle and a styrene production process.     

分级优化用于边值固定的化工动态优化问题

针对边值固定的化工动态优化问题, 提出了分级优化策略, 包括约束优先与目标优先两种方案, 它们的基本思想是将原问题转化为一系列的边值无约束问题, 采用目前成熟的优化算法加以集成即可实现. 对于控制变量受箱型约束的问题, 采用三角函数转换将其转化为控制无约束问题. 分级优化策略避免了罚函数策略的缺陷. 实例研究显示了分级优化策略能以足够的精度满足边值约束, 三角函数转换法是可行的.     

Optimal robust optimization approximation for chance constrained optimization problem

Chance constraints are useful for modeling solution reliability in optimization under uncertainty. In general, solving chance constrained optimization problems is challenging and the existing methods for solving a chance constrained optimization problem largely rely on solving an approximation problem. Among the various approximation methods, robust optimization can provide safe and tractable analytical approximation. In this paper, we address the question of what is the optimal (least conservative) robust optimization approximation for the chance constrained optimization problems. A novel algorithm is proposed to find the smallest possible uncertainty set size that leads to the optimal robust optimization approximation. The proposed method first identifies the maximum set size that leads to feasible robust optimization problems and then identifies the best set size that leads to the desired probability of constraint satisfaction. Effectiveness of the proposed algorithm is demonstrated through a portfolio optimization problem, a production planning and a process scheduling problem.     

基于聚类分析和可视化的增强遗传算法—I.算法的引出、原理与分析

提出了一种基于数据可视化的聚类分析法(ClusterConstrainedMapping,CCM)和人机结合的增强遗传算法,可保证进化过程在可行域中进行,不需要任何惩罚函数参数,可有效地进行带约束问题的优化.