基于BP神经网络的陶瓷裂纹釉釉面效果预测模型及应用

人工神经网络具有巨量并行、结构可变和高度非线性等特点,其建立数学模型并不需要预先知道太多有关问题背景的知识,这尤其适用于陶瓷釉研究中某些机理尚未完全清楚、传统数学方法无法分析的情况[1]。将人工神经网络技术用于裂纹釉的配方性能分析,以釉面裂纹为研究对象,选取了8种釉料的化学成分,在均匀实验设计的基础上,用BP人工神经网络对所得实验结果进行了分析,并且用图形化方式直观地表达了出来。根据实验结果,人工神经网络模型能较准确地预测出陶瓷的釉面效果,从而为研究裂纹釉提供了一种新的思路和有效手段。     

凝胶注模成型制备BaNd2Ti5O14介电陶瓷的电学性能

讨论了用凝胶注模成型制备BaNd2Ti5O14介电陶瓷的过程。本实验以化学式为BaNd2Ti5O14的介电陶瓷为固相粉末,以丙烯酰胺(MBAM)为凝胶有机单体,用传统球磨的方法制备出了高固相、低粘度的陶瓷浆料(浓悬浮体);采用正交实验设计法,优化出生坯密度最大的配方。分析了凝胶注模成型与干压成型制备的BaNd2Ti5O14介电陶瓷的体积密度、结构均匀性以及电学性能不同的原因。结果表明:凝胶注模成型制备的BaNd2Ti5O14介电陶瓷具有体积密度高、结构均匀的特点。合理使用凝胶注模成型工艺可以提高陶瓷介电常数、降低介质损耗。     

钛酸钡基铁电材料的介电非线性研究

通过A位或B位掺杂改性得到的钛酸钡基铁电材料,在外加直流偏置电场作用下,具有介电常数非线性可调的优异介电性能,可以广泛地应用于压控可调陶瓷电容器和微波可调器件领域。针对钛酸钡基铁电材料的介电非线性特性,讨论了BaTiO_3基铁电材料分别在铁电相、顺电相以及相转变温度场附近的介电非线性机理和相关理论;结合笔者近年来有关介电非线性研究的实验结果和相关文献报道,综述了不同A位离子(如Sr~(2-)和Ca~(2-)等)或B位离子(如Zr~(4 )和Sn~(4-)等)掺杂的BaTiO_3基铁电材料体系的介电非线性研究现状,分别对不同物质形态(陶瓷块体、薄膜和厚膜)的BaTiO_3基铁电材料的介电非线性研究及其应用进行了对比分析;并对钛酸钡基铁电材料今后的发展趋势和研究方向进行了展望。     

CaCu3Ti4O12 高介电材料的研究现状与发展趋势

CaCu3Ti4O12(CCTO)介电陶瓷材料以其高介电常数、高热稳定性和强烈的非线性等优异性能,在科学研究和实际应用中成为当前介电材料领域研究的热点.本文综述了迄今为止介电常数最高的CCTO钙钛矿体系介电陶瓷材料的相关进展,介绍了几种制备方法,评述了CCTO高介电陶瓷的掺杂改性和复合的研究现状,并对其发展趋势作了展望.     

氧化锌压敏陶瓷材料介电性能的研究

通过实验对ＺｎＯ压敏陶瓷元件的介电特性进行了研究，在微观结构的基础上导出晶粒间界的电容公式，与由实验作出的Ｃ－Ｖ曲线，两者基本一致，提出了控制压敏电阻器固有电容的方法，在理论的指导下，采用适当的配方和工艺，可以制得介电性能良好的适应不同要求的压敏电阻器。     

BP神经网络模型在橡胶配方优化中的应用

利用神经网络中的误差逆传播校正算法(BP模型)建立起橡胶配合剂与性能之间的非线性多目标模型，用遗传算法对BP神经网络模型的算法进行了改进，以试验数据为基础进行神经网络的训练，得到可预测橡胶配方性能的BP神经网络模型。以胶粉在全钢子午线轮胎胎侧胶中研究为例，对BP神经网络模型进行了验证。同时，与回归分析方法进行了比较，结果令人满意。     

锆钛酸钡（BZT）陶瓷制备及其介电性能的研究进展

锆钛酸钡(BzT)具有介电非线性强、漏电流小、介电常数高、居里温度可调、耐高压等特点,备受人们的关注.本文综述了锆钛酸钡陶瓷的制备方法及其晶粒尺寸、组成对BZT陶瓷介电性能的影响等方面的研究进展,并提出了在研究中亟待解决的问题.     

共沉淀法合成Bi掺杂钛酸钡基介电陶瓷的研究

采用化学共沉淀法合成了性能良好的Bi掺杂钛酸钡基介电陶瓷粉料，研究了合成工艺中的几个主要因素，研究了Bi掺杂量对材料性能的影响；利用SEM、XRD分析了材料微观结构，探讨了材料的介电性能与微观结构的关系。     

晶粒尺寸对Ba0.80Sr0.20TiO3陶瓷介电铁电特性的影响

利用微波烧结技术和传统烧结技术制备了晶粒尺寸在0.8～15μm的Ba0.80Sr0.20TiO3陶瓷，并对样品的介电特性和铁电特性进行了测试，分析了晶粒尺寸对材料介电和铁电性能的影响，实验结果表明，微波烧结技术可以有效地控制Ba0.80Sr0.20TiO3陶瓷的晶粒尺寸，晶粒尺寸降低，材料的介电常数大幅度提高，弥散指数降低，在外电场作用下，材料介电常数呈现明显的非线性效应，晶粒尺寸的大小对非线性效应产生影响，随晶粒尺寸的降低，材料的矫顽电压，剩余极化和自发极化都有所提高。     

凝胶注模成型制备大尺寸臭氧发生器陶瓷基板

讨论了用凝胶注模成型制备大尺寸臭氧发生器陶瓷基板的过程。本实验以化学式为Ba（Sm，Nd）2Ti5O14的介电陶瓷为固相粉末．以丙烯酰胺（MBAM）为凝胶有机单体，用传统球磨的方法制备出了高固相、低粘度的陶瓷浆料（浓悬浮体）。分析了凝胶注模成型与干压成型制备的Ba（Sm，Nd）2Ti5O14陶瓷基板的体积密度、结构均匀性以及电学性能不同的原因。结果表明：凝胶注模成型制备的Ba（Sm，Nd）2Ti5O14陶瓷基板具有体积密度商、结构均匀的特点。合理使用凝胶注模成型工艺可以提高陶瓷介电常数、抗电强度和降低介质损耗。     

Modeling and analysis of the electrical properties of PZT through neural networks

Application of ANN (Artificial neural network) to the electrical properties analysis of PZT is discussed in this paper. The same set of results of PZT samples were analyzed by a back-propagation (BP) network in comparison with a multiple nonlinear regression analysis (MNLR) model. The results revealed that the ANN model is much more accurate than MNLR model. The ANN approach also gave quite encouraging predictions for formulations not included in the train set samples, indicating that the BP network is a very useful and accurate tool for the properties analysis and prediction of multi-component solid solution piezoelectric ceramics.     

基于神经网络-遗传算法优化生物柴油制备工艺

根据生物柴油制备的实验数据,用人工神经网络(ANN)的反向传播(BP)算法建立了生物柴油转化率神经网络预测模型,提出了适宜的人工神经网络拓扑结构,讨论了BP算法中学习速率、动量系数及过拟合现象对网络的影响。实验数据检验表明,ANN方法能准确地关联生物柴油制备工艺条件与转化率的关系,转化率预测平均相对误差为1.917%,复相关系数R为0.9996;该神经网络预测模型用遗传算法优化,得到了最佳生物柴油制备条件。     

BP神经网络的发展及其在化学化工中的应用

人工神经网络（ANN）由于本身具有极强的非线性映射能力、容错性、自学习能力得到广泛的应用。基于反向传播算法（BP）的神经网络作为ANN重要组成部分,在涉及多种非线性因素建模时,相对于传统的反应机理建模显示出巨大的优势。虽然神经网络的发展几经繁荣与冷落,但目前在不同领域已经获得成功的应用。本文概述了BP神经网络的映射原理、缺点以及相应的改进方法,介绍其在催化剂设计、动力学模拟、理化特性估算、过程控制与优化、化学合成与反应性能预测的应用现状,展示了使用不同优化方法的改进模型在实验设计与优化方面取得的成果。最后指出未来BP神经网络的发展要进一步结合数据深度挖掘与机器学习等技术,为今后化学化工领域的研究提供强有力的工具。     

应用经遗传算法优化的BP神经网络预测催化裂化装置焦炭产率

焦炭是催化裂化装置的主要副产物,准确预测催化裂化焦炭产率对提高装置的操作平稳度和经济效益具有重要意义。人工神经网络(ANN)具有强大的自学习和自适应能力,在非线性预测方面具有明显的优势。本研究将遗传算法(GA)与BP神经网络相结合,基于某炼厂催化裂化装置的生产数据,分别从原料、催化剂和操作条件3个方面选取28个关键影响参数建立了催化裂化焦炭产率预测模型,分别将BP神经网络和经遗传算法优化的BP神经网络(GA-BP)的预测结果与工业数据进行对比。结果表明,经遗传算法优化的预测模型无论在预测结果的准确性还是稳定性方面效果更好。最后,本研究还通过考察原料残炭、反应温度等单一关键参数对焦炭产率的影响,进一步证明了经遗传算法优化的BP神经网络预测模型的准确性。     

Biosorption of Pb(II) using Gundelia tournefortii: Kinetics,equilibrium, and thermodynamics

In this paper, the feasibility of Gundelia tournefortii was studied as a novel, high-capacity biosorbent for removing lead ions from synthetic wastewater in a batch system. The effects of various parameters such as temperature, initial concentration, initial pH, biosorbent dosage, and contact time were investigated. Based on batch results, the optimum operating conditions were found to be pH 5, biosorbent dosage of 25 mg, and temperature of 20°C in the range of lead initial concentrations from 5 to 100 mg/L. The equilibrium contact time was 60 min. The biosorption mechanism can be well described by the Langmuir isotherm with a monolayer maximum adsorption capacity of 144.928 (mg/g) at 20°C and a pseudo-second-order kinetic model. Thermodynamic studies proved that the sorption process was physical, spontaneous, feasible, random, and exothermic. In the second step, the ability of artificial neural network (ANN) to predict the adsorption capacity of Gundelia tournefortii for the removal of Pb(II) from aqueous solution was examined. The model was developed using a three-layer feed-forward back-propagation (BP) network with 5, 12, and 1 neurons in the first, second, and third layers, respectively. The Levenberg–Marquardt BP training algorithm (LMA) was found to be the best BP algorithm with a minimum mean squared error of 0.000867 and a minimum relative squared error of 0.032771. The comparison between the results of ANN and experimental data showed that ANN has a superior performance (R²= of 0.998) in the prediction of the Pb(II) removal process.     

Comparison of Estimation Capabilities of the Artificial Neural Network with the Wavelet Neural Network in Lipase-Catalyzed Synthesis of Triethanolamine-Based Esterquats Cationic Surfactant

In this study, estimation capabilities of the artificial neural network (ANN) and the wavelet neural network (WNN) based on genetic algorithm were investigated in a synthesis process. An enzymatic reaction catalyzed by Novozym 435 was selected as the model synthesis process. The conversion of enzymatic reaction was investigated as a response of five independent variables; enzyme amount, reaction time, reaction temperature, substrates molar ratio and agitation speed in conjunction with an experimental design. After training of the artificial neurons in ANN and WNN, using the data of 30 experimental points, the products were used for estimation of the response of the 18 experimental points. Estimated responses were compared with the experimentally determined responses and prediction capabilities of ANN and WNN were determined. Performance assessment indicated that the WNN model possessed superior predictive ability than the ANN model, since a very close agreement between the experimental and the predicted values was obtained.     

咖啡因在水和乙醇中的溶解度及其关联

The solubility of caffeine in water and ethanol at 0—50 ℃ was measured using the laser method. The results were regressed with an empirical equation and simplified EOS correlation. A 2 - 2 - 1 backpropagation (BP) artificial neural network (ANN) model was selected from many other models. The prediction of interpolation and extrapolation of the data was made with trained 2 - 2 - 1 BP ANN model. The result was satisfactory.     

基于神经网络-遗传算法优化制氢工艺水碳比

根据某炼油厂制氢车间的生产数据,用人工神经网络(ANN)的反向传播(BP)算法建立了制氢装置转化生产中的水碳比神经网络预测模型,生产数据的检验表明,ANN方法能准确地关联和预报制氢装置转化生产中的水碳比,水碳比预测平均相对误差为2．83％;该神经网络预测模型用遗传算法优化并得到了最佳制氢工艺操作条件。     

DEVELOPMENT OF ANN MODEL FOR NON-LINEAR DRYING PROCESS

ABSTRACT

This paper presents an application of artificial neural network (ANN) technique to develop a model representing the non-linear drying process. The air heat plant (AHP), an important component in drying process is fabricated and used for building the ANN model. An optimal feed forward neural network topology is identified for the air heating system set-up. The training sets are obtained from experimental data. Back propogation algorithm with momentum factor is used for training. The results show that the back propogation ANN can learn the functional mapping between input and output. The advantages of ANN model developed for AHP is highlighted. The developed model can be used for control purposes.     

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