基于遗传算法的绕管式换热器结构参数优化研究

提出了一种基于遗传算法的绕管式换热器优化设计方法。采用单目标和多目标遗传算法,选取改进熵产数和换热器重量作为目标函数,对结构参数进行优化设计。结果表明,与工程设计值相比,换热器的改进熵产数和重量都得到了明显减小。多目标优化与单目标优化相比结果更优,可得到多组优化解,为绕管式换热器结构参数设计提供更多选择。     

混合装药内弹道性能优化及应用

为了研究混合装药结构对内弹道性能的影响,以某舰炮为工程背景,提出内弹道多目标优化设计方案,研究了目标函数、设计变量及约束条件.采用e-约束法对次要目标函数进行简化处理,利用当前流行的进化算法—遗传算法进行优化计算,并针对内弹道数学模型特性,对算法进行改进.最后,通过进化计算得到的优化方案为一组非劣解集,同时验证了该改进...     

序列数编码的遗传算法柔性优化多股流板翅式换热器通道排列

实数编码的遗传算法在优化多股流板翅式换热器通道排列中进行个体间交叉、变异操作后,存在无法保证各流体的通道数恒定不变的问题。对此,以通道的热负荷累积均方差为目标函数,建立通道排列的优化模型,提出基于序列数编码方式的改进遗传算法进行多股流板翅式换热器通道优化设计,实现了个体间交叉和变异遗传操作。对一包含4个流股57个通道的板翅式换热器通道排列进行优化,并通过多工况点设计对系统柔性进行分析。结果表明:本文优化设计方案的累积热负荷均方差为3562.9W,比文献经验值小2.7%,相比于实数编码遗传算法得到的结果,减少了15.1%,且累积热负荷在零线上下均匀波动。文章表明序列数编码的遗传算法增加了遗传算法种群中个体的多样性,提高了搜索效率具有更好的全局搜索能力。     

约束优化问题的改进混合遗传算法

提出了罚函数与修复的混合策略,改进了罚函数,给出了种群早熟度评价指标,对遗传算法的交叉算子进行了改进,解决了单一使用罚函数方法求约束优化问题所遇到的困难,方便了遗传算法在约束优化问题中的应用,提高了遗传算法在机械及工程中应用的适应性。数值实验证明,该方法比传统的遗传算法处理约束优化问题效率高。     

基于遗传算法的油气混输管网参数优化设计

在地面油气混输管网优化的研究中,结构参数优化是极为重要的一个问题.本文对多目标非线性的规划问题,使用遗传算法对管网结构参数进行优化,提出了目标函数、水力及热力学模型.并得到了应用遗传算法的相关结论     

多变异遗传算法优化生物脱硫反应动力学模型参数

由于常规遗传算法(SGA)的全局寻优效率不高,用于复杂的生物脱硫反应动力学模型参数优化时效果欠佳,为此设计了一种新的多变异遗传算法(MGA)以提高全局寻优效率.MGA的改进措施包括散射变异、微扰变异和单纯形变异各算子的设计,多变异操作实施方案的制定,选择操作和交叉操作方式的选择和改进等.Shaffer′s F6函数和10维Alpine函数测试表明,与SGA相比,MGA的全局寻优效率大大提高.将MGA应用于红球菌DS-3脱除二苯并噻吩(DBT)的动力学模型参数优化,建立了更为准确的反应动力学模型.     

基于遗传算法的层压板强度优化设计

本文的主要目的是研究遗传算法的优化机理并将遗传算法应用到复合材料层压板铺层的优化设计中。在深入理解力学原理,熟悉掌握数值计算方法的基础上对层压板进行强度优化设计。因此,在深入研究单层复合材料以及叠层复合材料的弹性特征理论基础和改进的遗传算法优化理论之后,针对复合材料层压结构遗传算法优化设计中层压结构参数具有离散型的特点,提出了适合复合材料层压结构遗传算法优化设计的整数编码策略,以整数来表征层压结构参数,这种编码方式简单直观、使用方便、易于进行遗传算法的交叉及变异操作。在分析层压结构强度的基础上,针对结构强度优化的目标构造了可用于遗传算法的适应度函数。同时参考了一定的铺层规则,在铺层角度限制为工程中常用的四种角度的前提下,应用遗传算法对层压板进行了铺层优化设计。设计结果表明,由于遗传算法特有的处理离散型问题的优势,在层压板铺层的优化设计中应用遗传算法是可行和可信的。     

基于效-费比TMD装置寻优的多种群遗传算法的改进

目前对于调谐质量阻尼器（TMD）性能的研究越来越多，但从效-费比方面对TMD减震控制系统进行经济性研究较少。为了获得最优效-费比下的TMD最优参数，同时兼顾成本控制与运算速度，对多种群遗传算法（MPGA）进行改进。改进内容包括从传统的单种群寻优转化为多种群同时寻优；改进适应度函数，满足目标优化同时兼顾效-费比。改进后该算法比传统遗传算法有更稳定的运算速度与运算精度，在TMD减振系统的设计中，不仅考虑减震效果因素和技术因素，还考虑经济因素，以使安装的成本和效果达到良好的平衡，既达到预期效果，又保护环境资源。     

基于神经网络和遗传算法的塑料热压成型多目标优化

论述了人工神经网络和遗传算法在塑料热压成型工艺优化中的应用,首先利用人工神经网络建立热压成型工艺参数与零件性能之间关系的数学模型,然后用遗传算法对工艺参数优化。根据多目标函数优化问题的单目标化思想,对优化后的单目标进行分解,得到最优工艺参数条件下的塑料热压产品性能,从而为建立和控制塑料热压成型工艺参数提供了一种行之有效的方法。     

基于EBF神经网络和粒子群算法的注射成型优化设计

基于拉丁超立方设计建立了椭球基（EBF）神经网络模型描述注塑工艺参数与翘曲值间的函数关系,将EBF神经网络模型与Kriging模型对比,说明EBF神经网络模型可以准确地描述注塑工艺参数与翘曲值之间的函数关系,并结合多目标粒子群算法对工艺参数进行优化,并与邻域培植遗传算法优化结果对比,说明多目标粒子群算法的优点。结果表明,基于EBF神经网络模型和粒子群优化算法可以使塑料出水管翘曲值减小11.64 %,同时使保压时间和冷却时间总和减小了2.13 s,从而在出水管批量生产过程中减少了生产时间。     

Surrogate-model-based,particle swarm optimization,and genetic algorithm techniques applied to the multiobjective operational problem of the fluid catalytic cracking process

Abstract

This article provides a concise multiobjective optimization methodology for an industrial fluid catalytic cracking unit (FCCU) considering stochastic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), based on surrogates or meta-models in order to approximate the objective function. A FCCU was considered and simulated in an AspenONE process simulator. In addition the article examines the claim that PSO has the same effectiveness (finding the optimal global solution) as GA, but with significantly better computational efficiency (fewer function evaluations). The optimization results obtained with the PSO technique, based on the evaluation of less functions and adjustment of less parameters, showed a 3% increase in yield of naphtha as compared to results obtained with the GA technique. Finally, the results of the optimization obtained with the stochastic optimization techniques were compared and analyzed with a deterministic one. The performance targets of the multiobjective operational optimization supported the FCCU design and production planning to ensure refinery profitability and a regulatory environment.     

Multiobjective differential evolution (MODE) for optimization of adiabatic styrene reactor

In this paper, a novel algorithm is proposed for solving multiobjective optimization problems. The proposed algorithm, multiobjective differential evolution (MODE), is applied to optimize industrial adiabatic styrene reactor considering productivity, selectivity and yield as the main objectives. Five combinations of the objectives are considered. Pareto set (a set of equally good solutions) obtained for all the cases is compared with results reported using non-dominated sorting genetic algorithm (NSGA). The results show that all objectives besides profit can be improved compared to those reported using NSGA and current operating conditions. The Pareto optimal front provides wide-ranging optimal operating conditions and an appropriate operating point can be selected based on the requirements of the user.     

管道周向励磁漏磁检测磁路设计

管道周向励磁漏磁检测技术是一种新的检测方法,对于检测和定量评定轴向导向缺陷具有潜在优势。磁化器磁路设计是漏磁检测需要解决的首要问题,采用磁路计算原理研究管道磁化磁路中永磁体长度、厚度和宽度改变时,管道表面缺陷产生的漏磁场的变化情况,得到了缺陷漏磁场随永磁体长度、厚度和宽度改变时的变化规律。此规律有助于从整体上把握永磁体磁路设计,改善磁化效果,提高漏磁检测灵敏度,进而为漏磁检测磁路的设计提供依据。     

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.     

A new weighted optimal combination of ANNs for catalyst design and reactor operation: Methane steam reforming studies

Catalyst design and evaluation is a multifactorial multiobjective optimization problem and the absence of well‐defined mechanistic relationships between wide ranging input‐output variables has stimulated interest in the application of artificial neural network for the analysis of the large body of empirical data available. However, single ANN models generally have limited predictive capability and insufficient to capture the broad range of features inherent in the voluminous but dispersed data sources. In this study, we have employed a Fibonacci approach to select optimal number of neurons for the ANN architecture followed by a new weighted optimal combination of statistically‐derived candidate ANN models in a multierror sense. Data from 200 cases for catalytic methane steam reforming have been used to demonstrate the veracity and robustness of the integrated ANN modeling technique. © 2011 American Institute of Chemical Engineers AIChE J, 58: 2412–2427, 2012     

一种新型交流永磁伺服电动机齿槽转矩性能测试装置的构建及应用研究

由于交流永磁伺服电动机的固有特点,存在齿槽转矩,影响电动机的低速性能和高精度定位,因此在研发阶段对电动机齿槽转矩相关的电磁设计和测试评估十分必要.根据在某公司的实际应用,对所构建的这种新型永磁伺服电动机齿槽转矩测试装置进行了详细论述,同时通过对现有齿槽转矩测试方法与装置的对比分析,系统地阐述了所述测试装置的实用性与可行性.     

Interactive solution to multiple criteria problems in chemical process design: An approach by pairwise comparison

A process design problem is considered as a multiobjective problem that is characterized by conflicting objectives such as economics, operability, and so on. A method developed for solving multiobjective problems in man-machine interactive mode has been extensively applied to the basic process design of chemical process systems. AS an illustrative example, an optimal design of a simple, yet practical, chemical process system has been taken up to show the practical usefulness of the present method.     

Sm(Co_(bal)Ni_(0.18)Fe_(0.1)Zr_(0.04))_x的永磁薄膜性能的研究

稀土永磁合金薄膜在微机电系统(MEMS)、磁记录介质、自旋电子元件和医疗科学领域有着重要的应用。Sm Co基永磁薄膜,其内禀居里温度高于其他永磁薄膜,高温条件下有更高的稳定性,因而在MEMS中具有潜在的应用前景。采用复合靶和纯钐靶共溅射制备出Sm Co基永磁薄膜样品,研究了薄膜合金中Sm含量以及退火温度等对Sm(CobalNi0.18Fe0.1Zr0.04)x相结构、微观形貌以及磁性能的影响,得出以下结论:退火温度对薄膜织构也有明显影响。随着退火温度提高,薄膜织构增强,矫顽力提高。随着Sm含量的提高,薄膜中Sm Co相析出增多,组织更加均匀,矫顽力明显提高。     

Multiobjective dynamic optimization of semibatch copolymerization reactors

Polymerization reactor optimal design and control problems are inevitably multiobjective in nature. Copolymerization problems encompass an even larger set of objective functions than homopolymerization. This work describes in some detail a set of typical components of the vector of objective functionals for copolymerization reactor problems. A subset of these is selected for detailed examination employing multiobjective function optimization techniques. Noninferior, or Pareto, sets of optimal solutions have been determined for the dual objectives of narrowing both copolymer composition and molecular weight distributions in the styrene-acrylonitrile system. Noninferior solutions occur when the two objectives are in conflict and no objective can be improved without sacrifice of the other. We show for several sets of control variables that, in the S-AN system, one should seldom, if ever, control with respect to one objective functional exclusively. Compromise policies can be found which are close to utopian for both objectives. A better quantitative understanding of the trade-offs between objectives in copolymerization engineering has been achieved.     

双色尾灯灯罩注射成型影响因素多目标优化

以某汽车双色尾灯灯罩为研究对象,选取模具温度、第一色熔体温度、第一色保压压力、第一色保压时间、第二色熔体温度、第二色保压压力和第二色保压时间为设计变量,采用最优拉丁超立方试验设计,使用Moldflow软件对双色尾灯灯罩的第一色体积收缩率、第二色体积收缩率和总翘曲量进行有限元模拟。利用模拟结果建立这3个目标值的二阶响应面模型。以这3个值最小化进行多目标优化,将优化后工艺参数进行数值模拟。结果表明:基于响应面模型的多目标优化预测结果与软件模拟结果差距较小,证明所建立的模型可以预测双色汽车尾灯灯罩的成型质量,指导生产出变形量小且满足要求的注塑件,也提高了零件设计到生产的效率。