模糊自适应遗传算法及其在化工中的应用

Considering that the performance of a genetic algorithm (GA) is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm (FAGA) combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.     

一种改进的混合遗传算法及在化工过程动态优化中的应用(英文)

The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. Genetic algorithm (GA) has been proved to be a feasible method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm (HIGA) for solving dynamic optimization problems is proposed to overcome these defects. Simplex method (SM) is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Gaussian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.     

基于神经网络与遗传算法结合的硝化细菌发酵培养基优化(英文)

Two artificial intelligence techniques, artificial neural network and genetic algorithm, were applied to optimize the fermentation medium for improving the nitrite oxidization rate of nitrite oxidizing bacteria. Experiments were conducted with the composition of medium components obtained by genetic algorithm, and the experimental data were used to build a BP (back propagation) neural network model. The concentrations of six medium components were used as input vectors, and the nitrite oxidization rate was used as output vector of the model. The BP neural network model was used as the objective function of genetic algorithm to find the optimum medium composition for the maximum nitrite oxidization rate. The maximum nitrite oxidization rate was 0.952 g 2 NO-2-N·(g MLSS)-1·d-1 , obtained at the genetic algorithm optimized concentration of medium components (g·L-1 ): NaCl 0.58, MgSO 4 ·7H 2 O 0.14, FeSO 4 ·7H 2 O 0.141, KH 2 PO 4 0.8485, NaNO 2 2.52, and NaHCO 3 3.613. Validation experiments suggest that the experimental results are consistent with the best result predicted by the model. A scale-up experiment shows that the nitrite degraded completely after 34 h when cultured in the optimum medium, which is 10 h less than that cultured in the initial medium.     

基于学习速率自适应的脉冲神经网络快速学习方(英文)

For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs), three learning rate adaptation methods (heuristic rule, delta-delta rule, and delta-bar-delta rule), which are used to speed up training in artificial neural networks, are used to develop the training algorithms for feedforward SNN. The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem, Iris dataset, fault diagnosis in the Tennessee Eastman process, and Poisson trains of discrete spikes. The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm. Furthermore, if the adaptive learning rate is used in combination with the momentum term, the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence. In the three learning rate adaptation methods, delta-bar-delta rule performs the best. The delta-bar-delta method with momentum has the fastest convergence rate, the greatest stability of training process, and the maximum accuracy of network learning. The proposed algorithms in this paper are simple and efficient, and consequently valuable for practical applications of SNN.     

基于多智体免疫网络算法的精馏装置板效率估计(英文)

Based on the immune mechanics and multi-agent technology, a multi-agent artificial immune network (Maopt-aiNet) algorithm is introduced. Maopt-aiNet makes use of the agent ability of sensing and acting to overcome premature problem, and combines the global and local search in the searching process. The performance of the proposed method is examined with 6 benchmark problems and compared with other well-known intelligent algorithms. The experiments show that Maopt-aiNet outperforms the other algorithms in these benchmark functions. Furthermore, Maopt-aiNet is applied to determine the Murphree efficiency of distillation column and satisfactory results are obtained.     

一种新的基于误差矢量化的选择性神经网络集成方法及其在高密度聚乙烯串级反应过程中的应用(英文)

Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.     

基于差分进化算法的控制变量参数化方法及其在化工过程动态优化中的应用(英文)

Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution (DE) algorithm and control vector parameterization (CVP) is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the proposed methods.     

基于免疫系统的自适应差分进化算法及在水银氧化动力学参数估计中的应用

A new version of differential evolution (DE) algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution (ISDE), is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters’ self-adaptation. The performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm and other well-known self-adaptive DE algorithms. The experiments conducted show that the ISDE clearly outperforms the other DE algorithms in all benchmark functions. Furthermore, ISDE is applied to develop the kinetic model for homogeneous mercury (Hg) oxidation in flue gas, and satisfactory results are obtained.     

基于FCMISVM的自适应软测量建模算法及其在PX吸附分离过程中的应用

To overcome the problem that soft sensor models cannot be updated with the process changes, a soft sensor modeling algorithm based on hybrid fuzzy c-means (FCM) algorithm and incremental support vector machines (ISVM) is proposed. This hybrid algorithm FCMISVM includes three parts: samples clustering based on FCM algorithm, learning algorithm based on ISVM, and heuristic sample displacement method. In the training process, the training samples are first clustered by the FCM algorithm, and then by training each clustering with the SVM algorithm, a sub-model is built to each clustering. In the predicting process, when an incremental sample that represents new operation information is introduced in the model, the fuzzy membership function of the sample to each clustering is first computed by the FCM algorithm. Then, a corresponding SVM sub-model of the clustering with the largest fuzzy membership function is used to predict and perform incremental learning so the model can be updated on-line. An old sample chosen by heuristic sample displacement method is then discarded from the sub-model to control the size of the working set. The proposed method is applied to predict the p-xylene (PX) purity in the adsorption separation process. Simulation results indicate that the proposed method actually increases the model’s adaptive abilities to various operation conditions and improves its generalization capability.     

陶瓷支撑的有机复合膜及其在渗透汽化中的应用(英文)

Pervaporation(PV),as an environmental friendly and energy-saving separation technology,has been received increasing attention in recent years.This article reviews the preparation and application of macroporous ceramic-supported polymer composite pervaporation membranes.The separation materials of polymer/ceramic composite membranes presented here include hydrophobic polydimethylsiloxane(PDMS) and hydrophilic poly(vinyl alcohol)(PVA),chitosan(CS) and polyelectrolytes.The effects of ceramic support treatment,polymer solution properties,interfacial adhesion and incorporating or blending modification on the membrane structure and PV performance are discussed.Two in-situ characterization methods developed for polymer/ceramic composite membranes are also covered in the discussion.The applications of these composite membranes in pervaporation process are summarized as well,which contain the bio-fuels recovery,gasoline desulfuration and PV coupled proc-ess using PDMS/ceramic composite membrane,and dehydration of alcohols and esters using ceramic-supported PVA or PVA-CS composite membrane.Finally,a brief conclusion remark on polymer/ceramic composite mem-branes is given and possible future research is outlined.     

对二甲苯二次氧化过程中液相反应的研究

通过半连续实验考察了对二甲苯(PX)二次氧化过程中的液相氧化,并对该过程进行了模拟计算。设计和进行了包含PX液相氧化、中断反应以及降温二次液相氧化三个阶段的实验;通过动力学实验测定和数据回归确定了基于芳烃氧化自由基链式反应机理的PX液相氧化动力学模型的参数;采用建立的PX液相氧化机理模型对PX分段氧化过程进行了预测。结果表明,对中断反应后的降温二次液相氧化间歇过程,采用第一阶段末期自由基浓度做初值时模型预测值与实验值符合良好;而假设自由基初值浓度为零时,PX液相氧化动力学模型对二次氧化的液相反应预测效果较差,其原因可能是中断降温后的氧化母液中存在过氧基团会使二次液相氧化反应极快地被再引发启动。     

对二甲苯氧化鼓泡塔反应器的模拟

对二甲苯(PX)鼓泡塔式反应器由反应段和塔顶的脱水段组成.在对反应段流体流动特性分析的基础上,建立了PX液相空气氧化反应的双级气泡模型,用Matlab工具求解模型方程.脱水段则采用化工流程模拟软件进行模拟.在VB语言平台上实现了两段计算的连接,从而完成了整个带脱水段的鼓泡塔式反应器的模拟.模型计算值与工业实际值基本吻合,验证了模型的合理性.     

基于一种改进的自适应遗传算法估算环已烷自催化氧化反应动力学参数

A modified genetic algorithm of multiple selection strategies, crossover strategies and adaptive operator is constructed, and it is used to estimate the kinetic parameters in autocatalytic oxidation of cyclohexane. The influences of selection strategy, crossover strategy and mutation strategy on algorithm performance are discussed. This algorithm with a specially designed adaptive operator avoids the problem of local optimum usually associated with using standard genetic algorithm and simplex method. The kinetic parameters obtained from the modified genetic algorithm are credible and the calculation results using these parameters agree well with experimental data. Furthermore, a new kinetic model of cyclohexane autocatalytic oxidation is established and the kinetic parameters are estimated by using the modified genetic algorithm.     

减少湿式氧化脱硫副反应的方法探析

湿式氧化法脱硫过程中,无可避免地会发生副反应,在脱硫液中产生S2O2-3、SCN-、CN-、SO2-4等酸根盐.由于这些副盐存在于液体中,提取、分离较为困难,处理工艺也较为复杂.要减少湿式氧化法脱硫副反应的产生,首先必须搞清楚脱硫反应机理、脱硫催化剂的主要组成.     

对二甲苯与羟胺盐反应体系的产物调控规律

以钼化合物为催化剂,考察了对二甲苯与羟胺盐体系一步合成2,5-二甲基苯酚或2,5-二甲基苯胺的反应.探讨了反应介质、催化剂加入量、反应温度以及时间等因素对该体系反应产物的影响.结果发现,当反应介质为体积比4∶10∶1的水-乙酸-硫酸溶液,催化剂加入量为0.5 g,羟胺/对二甲苯的物质的量之比为1～1.2之间,温度80℃、时间4h的反应条件下,对二甲苯-羟胺盐体系可以生成2,5-二甲基苯酚产物,其选择性为83％.另外,在反应介质为体积比10∶5～13∶2的乙酸-硫酸溶液,对二甲苯使用量为20 mmol,催化剂加入量为0.25 g,反应温度为85℃,反应时间为4h的条件下,对二甲苯-羟胺盐体系可以高选择性地生成2,5-二甲基苯胺,其选择性为88％.     

聚醚改性聚硅氧烷硅氢加成反应中副反应控制的研究

在聚醚改性聚硅氧烷的接枝过程中,硅氢加成反应进行的同时,伴有一些副反应发生。从合成聚醚改性聚硅氧烷的原料出发,利用GPC测定方法,研究硅氢加成反应过程中交联物生成的原因及其控制,实验得出交联物的生成归因于聚醚的自氧化和含氢聚硅氧烷中的硅氢键。Si—H键在氯铂酸的作用下容易发生水解并进一步发生交联,水分是交联物生成的主要因素。指出了含N化合物和羧酸或羧酸盐作为硅氢加成反应添加剂的研究前景。     

钼铁催化剂的微结构及其对二甲苯选择氧化的催化性能

采用溶胶-凝胶法制备了Mo-Fe催化剂,考察了其对对二甲苯催化氧化合成对苯二甲醛的催化性能.结果表明,对二甲苯在该催化剂上的转化率为86%,对苯二甲醛的选择性为50%.采用X射线衍射(XRD)、程序升温还原(TPR)和射线光电子能谱(XPS)等手段对Mo-Fe催化剂进行了表征.分析表明,Mo-Fe催化剂主要由MoO_与Fe_2(MoO_4)_3两种物相组成,两者的比例约为11:8.催化剂稳定性实验结果表明,Mo-Fe催化剂在450℃下反应,其活性可稳定在15左右.催化剂失活主要是催化剂中MoO_3被还原为MoO_2以及Fe_2(MoO_4)_3结构被破坏.     

改进极值策略的多目标粒子群算法及其在PX氧化过程中应用

提出了一种新的用于求解多目标问题的粒子群算法,该算法采用一种新的全局极值和个体极值选取策略,提升了种群逼近Pareto最优前沿的稳定性和精度,同时为了提升种群跳出局部最优的能力,提出两步变异操作.此外还采用了外部存档存储每一代产生的非支配解,并且使用动态更新的拥挤距离来维持外部存档的规模.然后,通过典型的ZDT系列测试函数对该算法进行评估,并与MOEA/D、NNIA和NSGA-Ⅱ 3种多目标优化算法进行比较.实验结果显示,新算法相较于其他算法具有较好的分布性与收敛性.最后将其应用于PX氧化反应操作优化中,在相同计算成本的条件下,新算法优化后的醋酸和PX燃烧损失明显下降,成本损失大幅减少.     

针对多目标优化的精英保留非劣排序遗传算法

遗传算法是模拟自然界生物进化过程的计算模型。作为一种有效的全局并行优化搜索工具,它具有简单、通用、鲁棒性强和适于并行分布处理的特点以及广泛的应用潜力。本文阐述了遗传算法的基本原理和方法,并着重介绍了一种改进的遗传算法——精英保留非劣排序遗传算法(NSGA-Ⅱ),并将其应用于化工中的多目标优化。     

基于遗传算法的石油机械优化设计

本文介绍了遗传算法的基本原理、基本概念和特点,并以石油机械中广泛使用的一单级斜齿圆柱齿轮传动优化为例说明了遗传算法的优越性。实例分析表明：将遗传算法应用于石油机械产品优化设计中能够取得很好的设计结果和良好的经济效益。