基于LabVIEW的日用陶瓷釉料配方虚拟实验和优化系统

日用陶瓷釉料配方的研制和优化是一个周期长、投入大、结果难以预料的实验过程.本文采用LabVIEW虚拟平台实现配方虚拟实验和优化系统,通过B样条神经网络的非线性逼近能力对陶瓷釉料配方的实验进行精确的逼近,并利用粒子群优化算法对该神经网络的釉料的最优配方进行搜索和优化.仿真结果表明,把B样条神经网络、粒子群优化算法与LabVIEW虚拟平台相结合的方法大大降低了配方实验成本,缩短了实验周期.     

模拟退火算法在橡胶配方优化设计中的应用

根据退火算法是一类求解组合优化问题有效算法。将其应用橡胶配方优化设计,利用计算机进行辅助建模和优化,ＩＩＲ内胎胶料配方实例表明该方法十分有效．     

单纯形遗传优化算法在陶瓷配方中应用

采用了遗传算法对Nelder-Mead单纯形法进行优化,提出了单纯形遗传优化算法,利用遗传算法的变异操作来增强单纯形法的全局搜索能力,并解决标准遗传算法局部搜索能力较弱的问题。将该算法应用于陶瓷配方设计,与单纯形法运算的结果进行了对比,实验结果验证了该算法的优越性。     

正交试验法在提高陶瓷釉面硬度实验中的应用

实验选取4个对陶瓷釉面硬度影响最显著的因素,利用正交试验法进行配方优化实验.实验结果表明:优化配方的釉面硬度较基础釉的硬度均有提高,最高达Hv720,能承受刀叉经常刻划而不致出现痕迹.     

光谱图像分析法在镀层成分识别中的应用

镀层成分识别可以为镀液配方改进和电镀工艺优化提供参考依据。提出了一种基于光谱图像分析的镀层成分识别方法。通过获取镀层光谱图像信息,应用小波分解实现特征提取,采用光谱图像分析方法并结合图像处理算法,对镀层成分进行识别。设计了镀层成分识别系统,介绍了该识别系统的硬件结构,阐述了镀层的光谱图像特征提取与成分识别过程,并进行了实验与分析。     

基于人工神经网络和遗传算法的甲烷制氢催化剂设计

通过筛选辅助组分和制备方法,制备了一种用于甲烷直接制氢的Fe3O4复合氧化物催化剂。应用人工神经网络建立了该催化剂的配方模型,对人工神经网络模型学习算法、激活函数以及网络结构进行了考察,确定了该催化剂辅助设计的步骤及模型的网络结构,将Levenberg-Marquardt方法用于网络的训练,改进了网络的收敛特性,最终获得了泛化能力较强的人工神经网络配方模型。以建立的模型为目标函数,采用改进的混合遗传算法作为优化方法,经过6轮优化,获得了一系列较优的甲烷直接制氢的Fe3O4复合氧化物催化剂配方。选用其中一种优化获得的配方进行甲烷制氢反应,催化剂寿命和氢气生成速率分别达到4.46 h和1.16 mmol·min-1·(g Fe)-1,优于以往报道的催化剂。     

果蝇优化SVM模型对有机化合物熔点的预测

为了有效解决支持向量机(SVM)模型在参数选择上的盲目性问题,进而提高该模型的学习性能和泛化能力,将果蝇优化算法(FOA)引入该领域,提出了一种基于果蝇优化的SVM方法。该方法首先运用果蝇优化优化算法选择全局最优的SVM惩罚因子和核函数参数,从而建立SVM分类模型,进而基于该模型对实际问题进行应用。将该模型应用于对有机化合物的熔点预测问题中,实验结果表明,基于果蝇优化的SVM模型效率高,实际应用效果好。     

丙纶强力丝复合稳定体系母料的研制与应用

利用稳定剂复配技术,通过优化配方研制出丙纶强力丝复合稳定体系母科。应用实验表明:该母料防老化性能好,可完全满足丙纶强力丝多方面防老化应用需要。     

基于人工神经网络和遗传算法的甲烷制氢催化剂设计

通过筛选辅助组分和制备方法,制备了一种用于甲烷直接制氢的Fe₃O₄复合氧化物催化剂。应用人工神经网络建立了该催化剂的配方模型,对人工神经网络模型学习算法、激活函数以及网络结构进行了考察,确定了该催化剂辅助设计的步骤及模型的网络结构,将Levenberg-Marquardt方法用于网络的训练,改进了网络的收敛特性,最终获得了泛化能力较强的人工神经网络配方模型。以建立的模型为目标函数,采用改进的混合遗传算法作为优化方法,经过6轮优化,获得了一系列较优的甲烷直接制氢的Fe₃O₄复合氧化物催化剂配方。选用其中一种优化获得的配方进行甲烷制氢反应,催化剂寿命和氢气生成速率分别达到4.46 h和1.16 mmol·min^-1·（g Fe）^-1,优于以往报道的催化剂。     

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。     

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。     

Artificial Intelligence Techniques to Optimize the EDC/NHS-Mediated Immobilization of Cellulase on Eudragit L-100

Two artificial intelligence techniques, namely artificial neural network (ANN) and genetic algorithm (GA) were combined to be used as a tool for optimizing the covalent immobilization of cellulase on a smart polymer, Eudragit L-100. 1-Ethyl-3-(3-dimethyllaminopropyl) carbodiimide (EDC) concentration, N-hydroxysuccinimide (NHS) concentration and coupling time were taken as independent variables, and immobilization efficiency was taken as the response. The data of the central composite design were used to train ANN by back-propagation algorithm, and the result showed that the trained ANN fitted the data accurately (correlation coefficient R(2) = 0.99). Then a maximum immobilization efficiency of 88.76% was searched by genetic algorithm at a EDC concentration of 0.44%, NHS concentration of 0.37% and a coupling time of 2.22 h, where the experimental value was 87.97 ± 6.45%. The application of ANN based optimization by GA is quite successful.     

基于GA-BP网络混凝投药系统预测模型的研究

针对BP网络建模易陷入局部极小、收敛速度慢等缺点,建立GA-BP网络预测模型,为混凝投药系统生产指导提供决策依据。利用遗传学习算法具有全局寻优的特点,同时优化BP网络的初始权值和网络结构,建立GA-BPNN混凝投药的预测控制模型。通过算法比较和模型仿真结果分析,GA-BP混合模型较BP模型收敛速度快,其平均预测相对误差仅为9.94%,预测精度远高于BP模型。表明GA-BP模型可以有效、可靠地用于混凝剂投加量预测控制系统的生产指导中。     

基于遗传神经网络的微滤膜通量的预测

针对BP神经网络在寻优过程中容易陷入局部极小值的缺点,将遗传算法和BP神经网络相结合,构造了一种基于遗传算法的反向传播（GABP）神经网络。网络的训练分为两步：首先利用遗传算法群体寻优策略,采用遗传算法对网络权值和阈值进行全局搜索,保证其落入全局最优点的邻域;然后再用梯度法对网络权值进行细化训练以进一步减小误差,使其收敛于全局最优解或性能很好的近似最优解。网络训练时使用的数据是在不同操作条件 （温度、压力、浓度）下,用标准孔径为0.1 μm 的亲水聚偏氟乙烯微滤膜过滤牛血清白蛋白(BSA)溶液时得到的膜通量实验数据,用训练好的GABP神经网络对微滤膜过滤BSA的膜通量预测研究结果表明,与传统的BP算法相比,GABP神经网络算法改善了网络收敛速度以及膜通量预测的准确度。     

Performance evaluation of an ANN–GA aided experimental modeling and optimization procedure for enhanced synthesis of marine biosurfactant in a stirred tank reactor

BACKGROUND: An improved resilient back‐propagation neural network modeling coupled with genetic algorithm aided optimization technique was employed for optimizing the process variables to maximize lipopeptide biosurfactant production by marine Bacillus circulans. RESULTS: An artificial neural network (ANN) was used to develop a non‐linear model based on a 2⁴ full factorial central composite design involving four independent parameters, agitation, aeration, temperature and pH with biosurfactant concentration as the process output. The polynomial model was optimized to maximize lipopeptide biosurfactants concentration using a genetic algorithm (GA). The ranges and levels of these critical process parameters were determined through single‐factor‐at‐a‐time experimental strategy. Improved ANN‐GA modeling and optimization were performed using MATLAB v.7.6 and the experimental design was obtained using Design Expert v.7.0. The ANN model was developed using the advanced neural network architecture called resilient back‐propagation algorithm. CONCLUSION: Process optimization for maximum production of marine microbial surfactant involving ANN‐GA aided experimental modeling and optimization was successfully carried out as the predicted optimal conditions were well validated by performing actual fermentation experiments. Approximately 52% enhancement in biosurfactant concentration was achieved using the above‐mentioned optimization strategy. © 2012 Society of Chemical Industry     

一种基于并行计算的混合算法优化有分流换热网络

换热网络优化是化工过程系统工程领域的研究难点,其数学模型具有高度的非凸、非线性,在使用单一启发式算法优化时,往往具有局限性。研究以换热网络的年综合费用最小为目标,针对强制进化随机游走（RWCE）算法在优化时由于个体间独立进化,导致优化过程中信息缺乏交流的问题,提出将遗传算法（GA）与其混合。混合后的算法在保持前一半优势种群中的个体单独进化的基础上,通过周期性的交叉、变异等操作产生子代来替换掉劣势种群,从而增强了原有算法的整型变量优化能力,并弥补了弱势个体无法更新的不足。为了兼顾算法在大种群下优化有分流换热网络的计算效率,节约时间成本,使用OpenMP系统将混合算法实现了并行化设计。通过三个不同规模的换热网络问题对并行后的混合算法进行验证,结果表明该算法能在有效提升优化质量的前提下相比串行算法大幅缩短计算时间,其中两个算例突破了目前文献最优解。     

Use of genetic algorithms in the optimization of free radical polymerizations exhibiting the trommsdorff effect

The genetic algorithm (GA) is adapted and used to obtain optimal temperature histories for methyl methacrylate polymerizations. The reaction time is minimized, while simultaneously requiring the attainment of design values of the final monomer conversion and number average chain length. The technique is robust, and gives near-global optimal solutions. As such, it can easily be used for on-line optimizing control of free radical polymerization reactors in which the reaction is associated with the Trommsdorff effect. The results obtained from GA can be improved further if these are provided as initial guesses to a computer code using the Pontryagin minimum principle with the first order control vector iteration method. © 1997 John Wiley & Sons, Inc.     

Emission control in palm oil mills using artificial neural network and genetic algorithm

The present study utilized a combination of artificial neural network (ANN) and genetic algorithms (GA) to optimize the release of emission from the palm oil mill. A model based on ANN is developed from the actual data taken from the palm oil mill. The predicted data agree well with the actual data taken. GA is then employed to find the optimal operating conditions so that the overlimit release of emission is reduced to the allowable limit.     

柔性换热器网络综合与清洗时序安排同步优化

提出在柔性换热器网络设计时考虑设备投入运行后实施在线清洗时序安排的同步优化方法,实现换热器网络动态综合。为降低该问题计算的复杂性,提出采用两阶段法。第一阶段利用虚拟温-焓图法初步综合获得考虑设备结垢过程和流股入口温度、热容流率发生变化时的多周期柔性网络,在同步优化之前识别出较优匹配候选。第二阶段,换热网络结构、设备面积和清洗时序安排利用遗传/模拟退火算法进行同步优化。通过积分方法来计算随时间变化的公用工程消耗,所建立的数学模型因此比以往文献更加严格。计算实例表明,所提方法有效降低年度总费用,同时能够解决大规模柔性换热器网络综合问题。     

柔性换热网络综合与清洗计划同步优化(英文)

A novel methodology is presented for simultaneously optimizing synthesis and cleaning schedule of flexible heat exchanger network (HEN) by genetic/simulated annealing algorithms (GA/SA). Through taking into account the effect of fouling process on optimal network topology, a preliminary network structure possessing twofold oversynthesis is obtained by means of pseudo-temperature enthalpy (T-H) diagram approach prior to simultaneous optimization. Thus, the computational complexity of this problem classified as NP (Non-deterministic Polynomial)-complete can be significantly reduced. The promising matches resulting from preliminary synthesis stage are further optimized in parallel with their heat exchange areas and cleaning schedule. In addition, a novel continuous time representation is introduced to subdivide the given time horizon into several variable-size intervals according to operating periods of heat exchangers, and then flexible HEN synthesis can be implemented in dynamic manner. A numerical example is provided to demonstrate that the presented strategy is feasible to decrease the total annual cost (TAC) and further improve network flexibility, but even more important, it may be applied to solve large-scale flexible HEN synthesis problems.