Strategies for Optimizing Feed Rate of Fed-Batch Yeast Fermentation by Fuzzy-Neural Network

In this paper,a novel fuzzy neural network model,in which an adjustable fuzzy sub-space was designed by uniform design,has been established and used in fed-batch yeast fermentationas an example.A brand-new optimization sub-network with special structure has been built andgenetic algorithm,guaranteeing the optimization in overall space,is introduced for the feed rateoptimization.On the basis of the model network,the optimal substrate concentration and theoptimal amount of fed-batch at different periods have been studied,aided with the optimizationnetwork and the genetic algorithm separately.The above results can be used as a basis for theestablishment of a fuzzy neural network controller.     

输入训练神经网络的维数约简算法及其在化工过程建模中的应用

Many applications of principal component analysis （PCA） can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on improved input training neural network （IT-NN） is proposed for the nonlinear system modelling in this paper. Momentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network （ANN）, IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network （RBF-NN） to model the yields of ethylene and propylene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.     

炼油厂氢气网络建模与多目标优化(英文)

The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming (MINLP). A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.     

降低精对苯二甲酸溶剂系统醋酸消耗的研究

Decreasing the acetic acid consumption in purified terephthalic acid （PTA） solvent system has become a hot issue with common concern. In accordance with the technical features, the electrical conductivity is in direct proportion to the acetic acid content. General regression neural network （GRNN） is used to establish the model of electrical conductivity on the basis of mechanism analysis, and then particle swarm optimization （PSO） algorithm with the improvement of inertia weight and population diversity is proposed to regulate the operating conditions. Thus, the method of decreasing the acid loss is derived and applied to PTA solvent system in a chemical plant. Cases studies show that the precision of modeling and optimization are higher. The results also provide the optimal operating conditions, which decrease the cost and improve the profit.     

基于ARX-NNPLS模型的优化策略及在聚合物牌号切换过程中的应用(英文)

Since it is often difficult to build differential algebraic equations (DAEs) for chemical processes, a new data-based modeling approach is proposed using ARX (AutoRegressive with eXogenous inputs) combined with neural network under partial least squares framework (ARX-NNPLS), in which less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined with neural network is used in the partial least squares (PLS) inner model between input and output latent variables. In the proposed dynamic optimization strategy based on the ARX-NNPLS model, neither parameterization nor iterative solving process for DAEs is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the process, and the computing time is greatly reduced compared to conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach moves faster to the target values and the computing time is much less.     

催化重整过程的多目标优化

In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reforming process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics reaction network and has been proved to be quite effective in terms of industrial application. The primary objectives include maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet temperatures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm, which is proposed by the authors and named as the neighborhood and archived genetic algorithm （NAGA）, is applied to solve this multiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set.     

利用神经网络预测注塑成型过程熔融温度

Among the processing conditions of injection molding, temperature of the melt entering the mold plays a significant role in determining the quality of molded parts. In our previous research, a neural network was developed to predict the melt temperature in the barrel during the plastication phase. In this paper, a neural network is proposed to predict the melt temperature at the nozzle exit during the injection phase. A typical two-layer neural network with back propagation learning rules is used to model the relationship between input and output in the injection phase. The preliminary results show that the network works well and may be used for on-line optimization and control of injection molding processes.     

基于支持向量机回归的天然气负荷预测研究

Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimum cost. Machine learning techniques have been increasingly applied to load forecasting. A novel regression technique based on the statistical learning theory, support vector machines (SVM), is investigated in this paper for natural gas shortterm load forecasting. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional regression techniques. Using a data set with 2 years load values we developed prediction model using SVM to obtain 31 days load predictions. The results on city natural gas short-term load forecasting show that SVM provides better prediction accuracy than neural network. The software package natural gas pipeline networks simulation and load forecasting (NGPNSLF) based on support vector regression prediction has been developed, which has also been applied in practice.     

基于复合神经网络的离散非线性系统建模与控制

An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.     

酶膜耦合反应器中反冲过程的神经网络模拟

In the enzymatic membrane reactor for separating casein hydrolysate, backflushing technology has been used to decrease the fouling of the membrane. Predication of the backflushing efficiency poses a complex non-linear problem as the system integrates enzymatic hydrolysis, membrane separation and periodic backflushing together. In this paper an alternative artificial neural network approach is developed to predict the backflushing efficiency as a function of duration and interval. A contour plot of backflushing performance is presented to model these effects, and the backflushing conditions have been optimized as duration of 10 s and interval of 10 min using this neural network. Also, simple neural networks are established to predict the time evolution of flux before and after backflushing. The results predicted by the models are in good agreement with the experimental data, and the average deviations for all the cases are well within ±5%. The neural network approach is found to be capable of modeling the backflushing with confidence.     

基于Tent混沌优化的神经网络预测控制

With the unique ergodicity, irregularity, and special ability to avoid being trapped in local optima, chaos optimization has been a novel global optimization technique and has attracted considerable attention for application in various fields, such as nonlinear programming problems. In this article, a novel neural network nonlinear predictive control (NNPC) strategy based on the new Tent-map chaos optimization algorithm (TCOA) is presented. The feedforward neural network is used as the multi-step predictive model. In addition, the TCOA is applied to perform the nonlinear rolling optimization to enhance the convergence and accuracy in the NNPC. Simulation on a laboratory-scale liquid-level system is given to illustrate the effectiveness of the proposed method.     

Melt index prediction by RBF neural network with an ICO‐VSA hybrid optimization algorithm

Melt index (MI) is considered as one of the most significant parameter to determine the quality and the grade of the practical polypropylene polymerization products. A novel ICO‐VSA‐RNN (RBF neural network with ICO‐VSA algorithm) MI prediction model is proposed based on radial basis function (RBF) neural network and improved chaos optimization (ICO), and variable‐scale analysis (VSA), where the ICO is first added and then combined with the VSA to overcome the defects of ICO and VSA, then the parameters of the RBF neural network are optimized with them. At last, the RBF neural network model for MI prediction model is developed. Further researches on the optimal RBF neural network model of MI prediction are carried out with the data from a real industrial plant, and the prediction results show that the performance of this prediction model is much better than the RBF neural network model without optimization. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

混合神经网络在污水毒性预测中的应用

分析了污水处理厂污水毒性对其它工艺参数的影响,选取DO、0RP、NH4-N及COD等过程参数为毒性测量的辅助变量.介绍了粒子群算法和混沌算法,运用混沌粒子群算法和BP算法相结合的混合神经网络,对生化池污水毒性进行预测,实现毒性的定量测量和报警.仿真结果表明,混合神经网络的泛化性能和时间复杂度要优于BP神经网络.     

融合过程先验知识的递归神经网络模型及其应用

大部分化工过程具有非线性特性,一般的线性建模方法难以有效应用。针对非线性化工过程动态建模,提出了一种基于过程先验知识的递归神经网络模型,充分发掘化工过程隐含的先验知识,并将这些先验知识以非线性约束的形式嵌入NARMAX结构的前馈神经网络中,同时基于增广拉格朗日乘子法约束处理机制,用PSO-IPOPT混合优化算法对过程先验知识递归神经网络权值进行优化。该过程先验知识递归神经网络模型对非线性化工过程动态建模,不仅有良好的建模精度和预测外推能力,而且能避免零增益的出现和增益反转,确保网络模型在实际应用中的安全性。文中以环管式丙烯聚合反应过程实际工业数据验证了所提网络模型的有效性。     

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。     

NEURAL NETWORK-BASED PREDICTIVE CONTROL FOR MULTIVARIABLE PROCESSES

In this paper, the systematic derivations of setting up a nonlinear model predictive control based on the neural network are presented. This extends our previous work (Chen, 1998) into a multivariable system to explore the characteristics of the design. There are two stages for the development of nonlinear neural network predictive control: a neural network model and a control design. In the neural network model design, a parallel multiple-input, single-output neural network autoregressive with a model of exogenous inputs (NNARX) is proposed for multistep ahead predictions. In control design, the controller with extended control horizon is developed. The Levenberg-Marquardt algorithm is applied to training the NNARX model. Also, the sequential quadratic programming is used to search for the optimal manipulated inputs. The gradient of the objective function and constraints that require computation of Jacobian matrices are completely derived for optimization calculation. To demonstrate the control ability of MIMO cases, the proposed method is applied through two nonlinear simulation problems.     

Neural network-based predictive control for multivariable processes

In this paper, the systematic derivations of setting up a nonlinear model predictive control based on the neural network are presented. This extends our previous work (Chen, 1998) into a multivariable system to explore the characteristics of the design. There are two stages for the development of nonlinear neural network predictive control: a neural network model and a control design. In the neural network model design, a parallel multiple-input, single-output neural network autoregressive with a model of exogenous inputs (NNARX) is proposed for multistep ahead predictions. In control design, the controller with extended control horizon is developed. The Levenberg-Marquardt algorithm is applied to training the NNARX model. Also, the sequential quadratic programming is used to search for the optimal manipulated inputs. The gradient of the objective function and constraints that require computation of Jacobian matrices are completely derived for optimization calculation. To demonstrate the control ability of MIMO cases, the proposed method is applied through two nonlinear simulation problems.     

粒子群优化神经网络的电镀金刚石套钻使用寿命预测

电镀金刚石套钻广泛应用各种加工领域,而套钻使用寿命受到多种因素影响,具有一定的非线性变化特点,为了准确预测电镀金刚石套钻的使用寿命,提出了基于粒子群优化神经网络的电镀金刚石套钻使用寿命预测模型。对电镀金刚石套钻使用寿命预测现状进行分析,针对BP神经网络参数优化问题,采用粒子群优化算法确定最优参数,建立电镀金刚石套钻使用寿命的预测模型,通过仿真实验对其有效性和优越性进行分析。实验结果表明,模型可以准确刻画影响参数与电镀金刚石使用寿命之间的变化关系,获得比对比模型更高的预测精度,实际应用价值更高。     

潜油电泵智能节电装置的研制

在进入高含水期的油田开发中,潜油电泵得到广泛应用,而如何提高其采油系统效率,降低电泵采油井耗电量,成为油田节能减排工作的重点。本文设计了一种新型智能潜油电泵有载调压变压器,给出了结构组成及工作流程,进行了室内外的现场实验,结果表明,该装置可以有效地选择最佳电压,节电效果显著。     

基于BP神经网络的中深孔爆破块度预测

利用神经网络极强的非线性动态处理能力,对开磷集团用沙坝矿中深孔爆破块度进行预测.选取岩体特性、炸药性能、爆破参数作为神经网络的输入层,平均块度和大块率作为输出层,建立神经网络预测模型,进行块度预测.对比神经网络预测和工业试验所得的数据,表明神经网络预测爆破块度能取得良好的效果.该方法对于优化中深孔爆破参数具有重要意义.