变邻域宽度的爬山微粒群优化算法及其应用

微粒群优化算法（particle swarm optimization algorithm,PSO）是由Kennedy和Eberhart 1995年提出的进化计算算法．PSO简单且具有许多良好的优化性能,但对一些复杂优化问题存在容易陷入局部极值的缺陷．本文提出一种变邻域宽度的爬山微粒群优化算法（hill-climbing PSO with variable width neighborhood,vwnHCPSO）,并用5种测试函数进行测试和比较,然后将vwnHCPSO用于催化裂化装置（FCCU）主分馏塔轻柴油闪点软测量．     

Optimization issues of the broke management system in papermaking

This paper presents an optimization strategy for the design and operation of a broke management system in a papermaking process. A stochastic model based on a two-state Markov process is presented for the broke system and a multiobjective and bi-level stochastic optimization model is developed featuring (i) a multiobjective operational subproblem for the optimization of the broke dosage and (ii) a multiobjective design problem formulation. An efficient optimization strategy is proposed for the operational subproblem along with a simulation based Pareto optimal solution for the design problem, and illustrated with a detailed case study.     

Optimization of Helical Microreactors by a Genetic Algorithm Technique

Micromixing in coiled microreactors is attained by a higher pressure drop and more pumping power. The optimum geometries of helically coiled microreactors were determined by multiobjective optimization based on a genetic algorithm (GA). The segregation index (X_s) values of the Villermaux/Dushman reaction were measured in twelve coiled microchannels. The effects of the geometries including curvature diameter and coil pitch on the mixing performance and pressure drop were investigated. The mixing performance of the microreactors and the pressure drop were considered as the GA objectives. The optimum geometries of the studied coiled microchannels with a trade-off between X_s and friction factors were obtained using GA-based multiobjective optimization.     

基于PSO-SVM的催化裂化装置故障诊断研究

提出基于粒子群优化算法和支持向量机的催化裂化装置反应再生子系统故障诊断方法。利用粒子群优化算法的全局搜索特性,实现支持向量机的参数优化算法。根据支持向量机算法构建了催化裂化装置反应再生子系统故障诊断模型。结果显示,该诊断方法准确率高,具有较高的使用价值。     

A hybrid approach to global optimization using a clustering algorithm in a genetic search framework

The concern of this work is global optimization using genetic algorithms (GAs). In this work we propose a synergy between the cluster analysis technique, popular in classical stochastic global optimization, and the GA to accomplish global optimization. This synergy minimizes redundant searches around local optima and enhances the capability of the GA to explore new areas in the search space. The proposed methodology demonstrates superior performance when compared with the simple GA on benchmark cases. We also report our solution of the optimal pumps configuration synthesis problem.     

基于双种群算法的模拟移动床多目标操作优化

以有限元法求解模拟移动床的稳态TMB模型和动态SMB模型,提出基于Pareto非劣解集的多目标双种群遗传粒子群算法;利用动态SMB模型仿真模拟移动床色谱吸附分离过程,以分离纯度和性能指标分别作为约束条件和目标函数进行多目标操作优化设计.仿真结果表明,SMB模型较之TMB模型更真实可靠,双种群遗传粒子群算法也较单一种群的...     

基于SHPSO-GA-BP的成品汽油调和中加氢汽油组分辛烷值的预测

针对成品汽油调和配方建模中加氢汽油组分辛烷值难以实时获取，考虑遗传算法（genetic algorithm，GA）、粒子群算法（particle swarm optimization，PSO）优化反向传播（back propagation，BP）网络存在的问题，提出了一种串行混合粒子群遗传算法（serial hybrid PSO-GA，SHPSO-GA）优化BP网络，并用于辛烷值的预测建模。该方法首先将PSO算法的输出依据适应度值分为优劣2个种群，弃劣留优；然后对留优种群再进行GA的交叉变异操作，进一步优化种群，经过每一代PSO和GA的交替优化，并将最优种群用于BP网络参数优化；最后基于该方法和工业历史数据，建立了加氢汽油组分辛烷值的预测模型，仿真结果表明，较传统BP，以及改进的GA-BP、PSO-BP、PSO-GA-BP等方法，SHPSO-GA-BP由于将PSO与GA进行更优的深度融合，具有更好的预测性能，可以用于辛烷值的预测。     

结合遗传算子的改进粒子群算法在控制系统设计中的应用

针对粒子群优化算法容易陷入局部最优以及早熟等缺点,结合遗传算法的选择交叉变异算子进行改进,得到一种新型PSO算法.将该方法应用于PID控制系统参数调优和被控对象参数辨识,仿真结果显示所提出的算法优化效果优于基本粒子群优化算法和遗传算法,收敛性能也得到较大提高.     

Multiobjective optimization under uncertainty of the economic and life‐cycle environmental performance of industrial processes

The combined use of multiobjective optimization and life‐cycle assessment (LCA) has recently emerged as a useful tool for minimizing the environmental impact of industrial processes. The main limitation of this approach is that it requires large amounts of data that are typically affected by several uncertainty sources. We propose herein a systematic framework to handle these uncertainties that takes advantage of recent advances made in modeling of uncertain LCA data and in optimization under uncertainty. Our strategy is based on a stochastic, multiobjective, and multiscenario mixed‐integer nonlinear programming approach in which the uncertain parameters are described via scenarios. We investigate the use of two stochastic metrics: (1) the environmental impact in the worst case and (2) the environmental downside risk. We demonstrate the capabilities of our approach through its application to a generic complex industrial network in which we consider the uncertainty of some key life‐cycle inventory parameters. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2098–2121, 2014     

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.     

Process modeling and optimization of industrial ethylene oxide reactor by integrating support vector regression and genetic algorithm

This article presents an artificial intelligence‐based process modeling and optimization strategies, namely support vector regression–genetic algorithm (SVR‐GA) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR‐GA approach, an SVR model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Genetic Algorithm (GAs) with a view to maximize the process performance. The GA possesses certain unique advantages over the commonly used gradient‐based deterministic optimization algorithms The SVR‐GA is a new strategy for chemical process modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR‐GA strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions when verified in actual plant resulted in a significant improvement in the EO production rate and catalyst selectivity.     

一种新的生产计划与催化裂化装置过程操作集成的闭环策略

Production planning models generated by common modeling systems do not involve constraints for process operations, and a solution optimized by these models is called a quasi-optimal plan. The quasi-optimal plan cannot be executed in practice some time for no corresponding operating conditions. In order to determine a practi- cally feasible optimal plan and corresponding operating conditions of fluidized catalytic cracking unit （FCCU）, a novel close-loop integrated strategy, including determination of a quasi-optimal plan, search of operating conditions of FCCU and revision of the production planning model, was proposed in this article. In the strategy, a generalized genetic algorithm （GA） coupled with a sequential process simulator of FCCU was applied to search operating conditions implementing the quasi-optimal plan of FCCU and output the optimal individual in the GA search as a final genetic individual. When no corresponding operating conditions were found, the final genetic individual based correction （FGIC） method was presented to revise the production planning model, and then a new quasi-optimal production plan was determined. The above steps were repeated until a practically feasible optimal plan and corresponding operating conditions of FCCU were obtained. The close-loop integrated strategy was validated by two cases, and it was indicated that the strategy was efficient in determining a practically executed optimal plan and corresponding operating conditions of FCCU.     

基于遗传算法的汽车永磁发电机多目标优化设计

提出了一种基于遗传算法的汽车永磁发电机多目标优化设计。该设计通过改进的遗传算法进行多目标优化。改进的遗传算法采用二进制编码,规范化几何秩选择,混合交叉及均匀变异。优化结果表明,在单目标函数和多目标函数情形下均搜索到全局最优解,遗传算法为多目标优化提供了有效的途径。同时经过反复计算后,获得一些具有实用价值的经验数据,对永磁电机的设计起到一定指导作用。     

基于PSO的丁二酸发酵动力学模型参数优化

丁二酸是一种重要的化工原料,对丁二酸发酵过程进行模型化研究可以为工艺放大提供必要的基础数据。根据丁二酸发酵过程的实验数据,在已有的丁二酸发酵动力学模型的基础上,采用粒子群优化算法进行模型参数优化研究,求得最优参数并利用其进行过程仿真。结果表明优化后的模型能够更好地模拟丁二酸分批发酵过程。和采用遗传算法进行的研究结果相比,粒子群算法提高了模型计算值与实验测量值的拟合程度,且算法简单,易于实现。     

Alternatives for multiobjective optimization of a polymerization process

The optimal control policies for a polymerization process, particularly for batch free‐radical polymerization of methyl methacrylate, were determined using a multiobjective optimization technique. The process objectives considered in the optimization include monomer conversion, polydispersity index, polymerization degree, and total reaction time, weighted and combined in a scalar objective function. The decision variables were the initial concentration of the initiator and the temperature represented by isothermal steps. For solving the optimization problem, several methods based on sequential quadratic programming and a genetic algorithm were used and compared. Combining them into a hybrid method (the genetic algorithm provided the initial values for the traditional iterative method) led to the best results. The aims of this study were to develop an approach for optimizing the polymerization process and to describe alternatives for formulating and solving this problem, emphasizing the importance of user decision in choosing solutions based on technological criteria. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 3680–3695, 2006     

A property‐based approach to the synthesis of material conservation networks with economic and environmental objectives

This article presents a multiobjective optimization model for the recycle and reuse networks based on properties while accounting for the environmental implications of the discharged wastes using life‐cycle assessment. The economic objective function considers fresh sources and treatment costs, whereas the environmental objective function is measured through the eco‐indicator 99. The model considers constraints in the process sinks as well as in the environment based on stream properties such as pH, chemical oxygen demand, toxicity, density, and color, in addition to the composition of the waste streams. A global optimization procedure is developed by indirectly tackling properties through property operators and by segregating the process streams before treatment. Three examples are included, and the results show that it is possible to consider simultaneously the trade‐offs between the total annual costs and the overall environmental impact using the proposed methodology. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Optimization of a Reverse Osmosis System Using Genetic Algorithm

Abstract

Reverse Osmosis (RO) has found extensive application in industry as a highly efficient separation process. In most cases, it is required to select the optimum set of operating variables such that the performance of the system is maximized. In this work, an attempt has been made to optimize the performance of RO system with a cellulose acetate membrane to separate NaCl‐Water system using Genetic Algorithm (GA). The GAs are faster and more efficient than conventional gradient based optimization techniques. The optimization problem was to maximize the observed rejection of the solute by varying the feed flowrate and overall permeate flux across the membrane for a constant feed concentration. To model the system, a well‐established transport model for RO system, the Spiegler‐Kedem model was used. It was found that the GA converged rapidly to the optimal solution at the 8th generation. The effect of varying GA parameters like size of population, crossover probability, and mutation probability on the result was also studied. The algorithm converged to the optimum solution set at the 8th generation. It was also seen that varying the computational parameters significantly affected the results.     

多目标多级串联系统优化的模糊优选动态规划技术

将模糊优选理论与动态规划技术有机地结合起来,提出多目标多级串联系统优化的模糊优选动态规划技术。为解决化工系统中的多目标多阶段优化问题提供一条新路。最后例举一多级反应器系统最优控制应用算例。     

基于子系统关联变量轮换的大系统在线协调优化

化工过程复杂大系统的在线优化过程中,存在流程前后的关联导致优化时间过长或得不到优化解的问题,需要按子系统优化并协调的优化方法来解决,而协调优化方法存在关联变量寻优方向不一致的问题。提出了一种基于子系统间关联变量轮换思想的分解协调优化方法,对大系统分解得到的子系统以轮换的方式进行优化,子系统中包含的多个优化问题分别在固定关联变量优化独立变量和固定独立变量优化关联变量的条件下求解,此轮换过程迭代进行,直至满足优化终止条件。将提出的方法应用在催化裂化装置仿真实例中与整体优化方法的结果作比较,证明了方法的有效性。最后,将基于关联变量轮换的协调优化方法应用在化工过程的在线优化中,结果表明本方法在在线优化应用中是可行的。     

Multiobjective Optimization of the Industrial Naphtha Catalytic Re-forming Process

1 INTRODUCTION Petroleum refining and petrochemical industries aim at maximizing one prime product while simulta-neously minimizing another accessory product to im-prove the quality of the prime product. Unfortunately, the two requirements are often conflicting or incon-sistent. It is necessary to determine the trade-off com-promises to balance the two objectives[1,2]. As the core of aromatics complex unit, catalytic reforming is a very important process for transforming naphtha into arom…