乙醛生产过程中的软测量实现

针对乙醛生产过程 ,建立关键过程变量粗乙醛浓度的软测量模型 ,并在此基础上建立粗乙醛的实时收率预测模型。对软测量实现中涉及到的回归变量选择、样本预处理、回归一致性分析、实时校正机制等关键技术进行讨论。该系统的预测值与离线分析值平均相对偏差为 1.2 %。     

乙醛生产过程优化

依据乙醛生产的反应机理和影响因素的分析 ,利用生产中的实际数据 ,通过数学回归建立了乙醛生产过程的数学模型 ,并以达到最大产量为目标 ,求得现有装置最大生产能力及适宜操作条件。根据优化的结果 ,经过 1998年和 1999年在乙醛装置实际生产中的应用 ,生产负荷达到了设计负荷的 12 7% ,成效显著 ,实现了优化目的     

化工过程建模中的数据挖掘技术

对数据挖掘技术在化工过程建模中的应用现状进行了综述，介绍了一些主要的数据挖掘技术，其中包括神经网络、遗传算法、主元分析和偏最小二乘法等；展望了数据挖掘技术在化工过程建模中的应用前景。     

乙醛缩合法生产乙酸乙酯的发展前景

1前言乙酸乙酯是一种重要的有机溶剂,广泛用于油漆。涂料、油墨、粘合剂等方面。日本和美国年产量均在10万t以上,我国目前总生产能力为566万t/a。随着我国工业,特别是汽车工业的发展,乙酸乙酯的需求量将会大幅度地增加。乙酸乙酯的生产方法主要有两种：一是传统的直接酯化法,即乙酸和乙醇在硫酸等催化剂的存在下进行反应合成乙酸乙酯;另一方法是乙醛缩合法,即两分子乙醛在乙醇铝的催化作用下,经Tishchenko反应生成乙酸乙酯。与酯化法相比缩合法具有原料单耗低,工艺简单、设备腐蚀小,三废排放量少等优点,但我国受原料及技术方面…     

一氯乙醛生产与应用

主要介绍了一氯乙醛的生产方法及其应用。     

数据挖掘技术及其在化工过程中的应用

数据挖掘技术是当今一项新兴技术，它综合运用人工智能、计算智能、模式识别、数理统计等先进技术从大量数据信息中挖掘和发现有价值和隐含的知识。本文介绍了一些主要用于化工过程的数据挖掘技术，如粗集理论、人工神经网络、遗传算法、主元分析和偏最小二乘法等，并对其在化工过程建模、控制、故障诊断和优化中的应用进行了综述。     

乙醛生产中催化剂氯化钯耗量的降低措施

简述了乙醛生产过程中的催化反应及催化剂再生原理 ,分析了钯含量对催化剂活性的影响。针对引起钯耗的原因 ,提出 7条降低钯耗的措施。改造后的运行结果表明 ,催化剂活性得以提高 ,既降低了钯耗 ,也降低了乙醛装置其他主要物耗。     

生产乙醛的非溶剂工艺

英国加的夫（Cardiff）大学和美国李海（Lehigh）大学的科学家开发出一种催化剂，不用溶剂就可以将乙醇转化为乙醛。研究者认为，先前开发的非溶剂路线的尝试均告失败，是因为现有的催化剂（尤其是用于乙醇氧化法）活性不够。这种新型催化剂，以TiO2为载体，由金和钯的纳米晶粒构成，可使乙醇的氧化率增加25％。乙醛被广泛地用于化学品、医药和香水工业。传统路线主要是通过铬酸盐、高锰酸盐等昂贵和有毒的氧化剂，将溶液中的乙醇氧化而生成乙醛。     

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。     

A concrete mix proportion design algorithm based on artificial neural networks

The concepts of five parameters of nominal water-cement ratio, equivalent water-cement ratio, average paste thickness, fly ash-binder ratio, grain volume fraction of fine aggregates and Modified Tourfar's Model were introduced. It was verified that the five parameters and the mix proportion of concrete can be transformed each other when Modified Tourfar's Model is applied. The behaviors (strength, slump, et al.) of concrete primarily determined by the mix proportion of concrete now depend on the five parameters. The prediction models of strength and slump of concrete were built based on artificial neural networks (ANNs). The calculation models of average paste thickness and equivalent water-cement ratio can be obtained by the reversal deduction of the two prediction models, respectively. A concrete mix proportion design algorithm based on a way from aggregates to paste, a least paste content, Modified Tourfar's Model and ANNs was proposed. The proposed concrete mix proportion design algorithm is expected to reduce the number of trial and error, save cost, laborers and time. The concrete designed by the proposed algorithm is expected to have lower cement and water contents, higher durability, better economical and ecological effects.     

Optimization of acrylic dry spinning production line by using artificial neural network and genetic algorithm

Acrylic fibers are synthetic fibers with wide applications. A couple of methods can be utilized in their manufacture, one of which is the dry spinning process. The parameters in this method have nonlinear relationships, making the process very complex. To the best of the authors' knowledge, no comprehensive study has yet been conducted on the optimization of acrylic dry spinning production using computer algorithms. In this study, such parameters as extruder temperature in and around the head, solution viscosity, water content in the solution, formic acid content of the solution, and the retention time of the solution in the reactor were measured in an attempt to predict the behavior of the dry spinning process. The color index of the manufactured fibers was used as an indicator of production quality and statistical methods were employed to determine the parameters affecting the process. An artificial neural network (ANN) using the back propagation training algorithm was then designed to predict the color index. ANN parameters including the number of hidden layers, number of neurons in each layer, adaptive learning rate, activation functions, number of max fail epochs, validation and test data were optimized using a genetic algorithm (GA). The trial and error method was used to optimize the GA parameters like population size, number of generations, crossover or mutation rates, and various selection functions. Finally, an ANN with a high accuracy was designed to predict the behavior of the dry spinning process. This method is capable of preventing the manufacturing of undesired fibers. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Prediction of thermodynamic properties of polyvinylpyrrolidone solutions by artificial neural networks coupled with genetic algorithm

In the present study, the artificial neural networks coupled with the genetic algorithm (ANN–GA) models were used to predict the thermodynamic properties of polyvinylpyrrolidone (PVP) solutions in water and ethanol at various temperatures, mass fractions, and molecular weights of polymer. The genetic algorithm (GA) was used to find the best weights and biases of the network and improve the performance of ANNs. The proposed model was composed of three input variables including the temperature of the solution, the mass fraction, and molecular weight of the polymer. Density, viscosity, and surface tension of PVP solutions with various molecular weights (10,000, 25,000, and 40,000) in water and ethanol have been measured in the temperature range 20–55°C and various mass fractions of polymer. The ANN–GA models were trained by the experimental datasets and the prediction of density, surface tension, and viscosity of PVP solutions was performed using these models. The predicted values were compared with the experimental ones and the mean absolute relative error was less than 0.5% for the density and surface tension and about 3% for the viscosity of solutions.     

An algorithm to determine sample sizes for optimization with artificial neural networks

This article presents an algorithm developed to determine the appropriate sample size for constructing accurate artificial neural networks as surrogate models in optimization problems. In the algorithm, two model evaluation methods—cross‐validation and/or bootstrapping—are used to estimate the performance of various networks constructed with different sample sizes. The optimization of a CO₂ capture process with aqueous amines is used as the case study to illustrate the application of the algorithm. The output of the algorithm—the network constructed using the appropriate sample size—is used in a process synthesis optimization problem to test its accuracy. The results show that the model evaluation methods are successful in identifying the general trends of the underlying model and that objective function value of the optimum solution calculated using the surrogate model is within 1% of the actual value. © 2012 American Institute of Chemical Engineers AIChE J, 59: 805–812, 2013     

基于遗传BP神经网络预测硫在高含硫气体中溶解度

为更精确地关联预测硫在高含硫气体中的溶解度,提出将遗传算法(GA)和LM-反向传播神经网络(LM-BP ANN)相结合的预测模型。设计了该模型的计算过程,讨论了模型参数的设置。以温度、压力和气体组分作为BP神经网络预测模型的输入变量,利用GA优化了BP神经网络的初始权值和阈值,采用遗传算法优化后的BP神经网络计算了元素硫在高含硫气体中的溶解度。结果表明,该模型训练结果与实测值之间的平均相对误差为5.90%,测试结果与实测值的平均相对误差为5.54%;该方法较BP神经网络模型具有预测精度高、收敛速度快的优点;该模型具有较好的模拟及内推、外推功能。     

基于神经网络和遗传算法建立的氧化铁红生产工艺优化模型

在氧化铁红生产中,产品质量和生产工艺优化是一个关键的问题.利用遗传算法与人工神经网络技术(GA-ANN)相结合的方法,建立了氧化铁红产品质量预报与生产工艺优化模型.网络结构设计采用遗传算法以及自适应和加动量项调整学习速率等措施,避免了复杂优化问题陷入局部最小的问题.将该模型应用到氧化铁红生产中,能使操作条件始终保持在优化状态,有利于挖掘生产潜力.在线应用表明:该模型具有高的学习精度和快的收敛速度,预报氧化铁红的三氧化二铁质量分数,允许误差小于3%的命中率可达90%以上.     

Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

The availability of reduced-dimensional, accurate dynamic models is crucial for the optimal operation of chemical processes in fast-changing environments. Herein, we present a reduced modeling approach for rectification columns. The model combines compartmentalization to reduce the number of differential equations with artificial neural networks to express the nonlinear input–output relations within compartments. We apply the model to the optimal control of an air separation unit. We reduce the size of the differential equation system by 90% while limiting the additional error in product purities to below 1 ppm compared to a full-order stage-by-stage model. We demonstrate that the proposed model enables savings in computational times for optimal control problems by ~95% compared to a full order and ~99% to a standard compartment model. The presented model enables a trade-off between accuracy and computational efficiency, which is superior to what has recently been reported for similar applications using collocation-based reduction approaches.     

基于人工神经网络的过热蒸汽热流量计量系统

提出基于人工神经网络的过热蒸汽热流量测试技术,建立了基于人工神经网络模型的过热蒸汽质量和热流量的间接测量系统。在压力为0.28MPa—10.0MPa、温度为140℃—490℃范围,建立了过热蒸汽密度和密焓积的双输出人工神经网络模型,实现了热流量测量及质量流量的温度、压力补偿,并对模型的精度和外延性进行分析。模型输出密度的最大误差为2.047%,密焓积的最大误差为1.717%,具有较高的精度。     

Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm

A new method for catalyst design was discussed based on artificial neural network, which was developed to simulate the relations between catalyst components and catalytic performance in the previous research. For enhancing efficiency of catalyst design, a new hybrid GA tested by TSP was generated for global optimization to design the ‘optimal’ catalyst. A multi-turn design strategy was described. Based on the previous research, the design method was applied for designing multi-component catalyst for methane oxidative coupling, some better catalysts, in which C₂ hydrocarbon yields were greater than 25% were designed. When reacting on the best catalyst, GHSV was , CH₄:O₂ was 3, reaction temperature was , methane conversion and C₂ hydrocarbon selectivity were 37.79% and 73.50%, respectively (C₂ hydrocarbon yield was 27.78%), which was higher than that of previous reported catalysts on no diluted gas condition, and showed a better prospect for industrialization of methane oxidative coupling. The research also showed that the new catalyst design method is highly efficient and universal.