Highly Active/Selective Heterogeneous Catalyst Co/Ts-1 for Epoxidation of Styrene by Molecular Oxygen: Effects of Catalyst Preparation Conditions and Reaction Conditions on the Reaction

In the study, highly active/selective heterogeneous catalyst Co/TS-1 has been prepared successfully. The catalyst was characterized by powder X-ray diffraction (XRD), Fourier transform infrared (FT-IR), diffuse reflectance UV–visible(DR UV–vis) and transmission electron microscopy (TEM). Liquid-phase catalytic epoxidation of styrene to styrene epoxide by molecular oxygen was carried out at atmospheric pressure in the presence of Co/TS-1 catalyst. Sacrificial co-reductant or other promoted reagent such as t-butyl hydroperoxide (TBHP) was not added in the reaction system. A 94.5 mol% conversion of styrene with an epoxide selectivity of 74.3 mol% was attained after 3.5 h reaction. It is noteworthy that there is a synergy between the Co and the Ti in the catalyst in the liquid phase epoxidation of styrene.     

Optimization for Injection Molding Process Conditions of the Refrigeratory Top Cover Using Combination Method of Artificial Neural Network and Genetic Algorithms

The process conditions have important influence on final part quality in injection molding, and how to get optimum process conditions is the key to improving part quality. Sinkmarks on the surfaces of injection-molded parts is one of the problems that limit the overall success of injection molding technology, and the presence of sinkmarks significantly impairs the surface quality of injection molded parts. A combination method of artificial neural network and genetic algorithms is proposed to optimize the injection molding process, and the processing parameters of a refrigeratory top cover are optimized using the combining method to minimize the sinkmarks on the part. The results indicate the combining method is an effective tool for the process optimization of injection molding.     

基于人工神经网络的橡胶螺杆挤出机智能化设计

通过人工神经网络建立了挤出机的智能化设计模型.该模型可以进行多影响因素下的多目标分析,并应用于橡胶挤出机的结构选型、结构设计、找出最佳的工艺参数等.改进了过去单凭产量来选择螺杆直径的简略的方法.由实验数据和专家经验作为样本训练得到的智能模型,能达到更符合实际情况、综合考虑产量和胶料特性等诸多因素来选择出合理的挤出机直径和挤出机工作转速等多目标的智能化的设计方法.     

Control Parameterization‐Based Adaptive Particle Swarm Approach for Solving Chemical Dynamic Optimization Problems

A control parameterization‐based particle swarm optimization (CP‐PSO) approach is presented which combines control parameterization with particle swarm optimization to solve dynamic optimization problems in chemical engineering. To improve search efficiency and convergence rate, a control parameterization‐based adaptive particle swarm optimization (CP‐APSO) approach is proposed, in which inertia weight and acceleration coefficients are updated according to population distribution characteristics. Three benchmark chemical dynamic optimization problems are explored as illustration. The results demonstrate that CP‐APSO is efficient for solving a general class of chemical dynamic optimization problems and CP‐APSO largely outperforms CP‐PSO on the convergence rate.     

Supported Nano-Gold Catalysts for Epoxidation of Styrene and Oxidation of Benzyl Alcohol to Benzaldehyde

Nano-gold particles supported on different alkaline earth oxides (viz. MgO, CaO, BaO and SrO), Gr. IIIa metal oxides (viz. Al₂O_3, Ga₂O_3, In₂O₃ and Tl₂O₃), transition metal oxides (viz. TiO_2, Cr₂O_3, MnO_2, Fe₂O_3, CoO_x, NiO, CuO, ZnO, Y₂O₃ and ZrO₂), rare earth metal oxides (viz. La₂O_3, Ce₂O_3, Nd₂O_3, Sm₂O_3, Eu₂O_3, Tb₂O_3, Er₂O₃ and Yb₂O₃) and U₃O₈ [all prepared by depositing gold on corresponding metal oxide support by deposition precipitation (DP) and/or homogeneous deposition precipitation (HDP) method] were evaluated for their catalytic performance in the liquid phase epoxidation of styrene by tert-butyl hydroperoxide (TBHP) to styrene oxide and also in the solvent-free benzyl alcohol-to-benzaldehyde oxidation (by molecular oxygen or TBHP) reactions. For the epoxidation, the catalytic performance (styrene oxide yield) of the most promising nano-gold catalysts prepared by the HDP method was in the following order: Au/MgO > Au/Tl₂O₃ > Au/Yb₂O₃ > Au/Tb₂O₃ > Au/CaO (or TiO₂). However, for the oxidation of benzyl alcohol to benzaldehyde by molecular oxygen, the order of choice for the most promising catalysts (based on benzaldehyde yield) was Au/U₃O₈ > Au/Al₂O₃ > Au/ZrO₂ > Au/MgO. Whereas, when TBHP was used as an oxidizing agent for the benzyl alcohol oxidation, the order of choice for the most promising catalysts was Au/U₃O₈ > Au/MgO > Au/TiO₂ > Au/ZrO₂ > Au/Al₂O₃. The catalytic performance of a particular supported nano-gold catalyst was thus found to depend on the reaction catalysed by them. Moreover, it is strongly influenced by a number of catalyst parameters, such as the metal oxide support, the method of gold depositon on the support, the gold loading and also on the catalyst calcination temperature. Nano-gold particles-support interactions seem to play an important role in controlling the deposition of gold (amount of gold deposited and size and morphology of gold particles), formation of different surface gold species (Au⁰, Au¹⁺ and Au³⁺) and electronic properties of gold particles and, consequently, control the catalytic performance (both the activity and selectivity) of the supported nano-gold catalysts in the reactions. The nano-gold catalysts prepared by the HDP method showed much better catalytic performance than those prepared by the DP, coprecipitation or impregnation method; in general, the HDP method provided supported gold catalysts with much higher gold loading and/or smaller size gold particles than that achieved by the DP and other methods.     

基于EBF神经网络和粒子群算法的注射成型优化设计

基于拉丁超立方设计建立了椭球基（EBF）神经网络模型描述注塑工艺参数与翘曲值间的函数关系,将EBF神经网络模型与Kriging模型对比,说明EBF神经网络模型可以准确地描述注塑工艺参数与翘曲值之间的函数关系,并结合多目标粒子群算法对工艺参数进行优化,并与邻域培植遗传算法优化结果对比,说明多目标粒子群算法的优点。结果表明,基于EBF神经网络模型和粒子群优化算法可以使塑料出水管翘曲值减小11.64 %,同时使保压时间和冷却时间总和减小了2.13 s,从而在出水管批量生产过程中减少了生产时间。     

苯乙烯-马来酸酐本体共聚合过程的神经网络建模

人工神经网络(ANN)是一种有效的建模方法，尤其适用于机理复杂的化工过程，故应用ANN技术来研究苯乙烯-马来酸酐半连续本体共聚合过程的建模方法，并用原始实验数据训练BP网络，来预测本体共聚合过程的目标变量——反应转化率是合适的。由于标准BP训练算法的训练速度较慢，提出了一种改进的训练算法(marquardt算法)来提高网络的训练速度。结果表明，改进的训练算法提高收敛速度10倍以上，在不同的初始条件下，如停留时间5小时、聚合温度110-120℃和马来酸酐进料分量7％-10％，能得到满意的收敛点。在3个输入和1个输出(转化率)的情况下，估计结果的最大相对误差为10％-15％，平均相对误差小于5％。转化率的模型预测结果与原始实验数据具有良好的拟合。此方法可以有效地用于此类聚合过程的模型化。     

6万t/a苯乙烯装置精馏工艺设计与优化

介绍了苯乙烯精馏工艺及其在国内外的发展状况,并在设计过程中分别对粗分塔塔釜的不同温度工况进行了对比。采用了高釜温的精馏方案新工艺,对装置的聚合损失、化学品消耗、设备投资等进行了优化。     

应用群能量恒定粒子群优化算法的批次流加发酵过程批次间优化(英文)

An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter iden-tification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.     

Asymmetric Epoxidation of Styrene on the Heterogenized Chiral Salen Complexes Prepared from Organo-Functionalized Mesoporous Materials

Organosilane-modified mesoporous materials have been prepared under mild and acidic conditions by a solvent evaporation method using C₁₆TMABr surfactant as a template. The mesoporous samples synthesized in ethanol solvent by using this evaporation method showed a fully disordered pore system, but those obtained under hydrothermal conditions had highly ordered pores. The chiral salen Mn(III) complexes were immobilized on these organosilane-functionalized mesoporous silicas by a grafting method. The catalysts used in the asymmetric epoxidation of styrene and cis-stilbene and the effect of different mesoporous structures on the reactivity was investigated. Similar enantioselectivities were observed by using these heterogenized salen complexes as compared with reaction under homogeneous conditions.     

Modeling and Optimization of Copper Flash Smelting Process Based on Neural Network

The copper flash smelting process neural network model (CFSPNNM) was developed, its input layer includes eightnodes: oxygen grade (OG), oxygen volume per ton of concentrate (OVPTC), flux rate (FR) and quantities of Cu, S, Fe, SiO2and MgO in copper concentrate; output layer includes three nodes: matte grade, matte temperature and Fe/SiO2 in slag, andnet structure was 8-13-10-3. Then, the internal relationship between the technological parameters and the objectiveparameters was built after the CFSPNNM was trained by using GA-BP algorithm. Moreover, the technological parameterswere optimized by using genetic algorithms (GA) to make energy consumption the lowest. Simulation results showed that theCFSPNNM had high prediction precision and good generalization performance. Compared with the practical average data, theenergy consumption can be reduced by 6.8% if the smelting process is controlled by adopting the optimized technologicalparameters.     

基于动态神经网络的非线性过程在线预测

神经网络需满足以下两个条件方能用于非线性过程的在线预测:①神经网络必需以某种递推的方式出现;②神经网络的学习算法应尽可能简洁快速.为此改造泛回归神经网络(GRNN),运用递推更新的样本数据集训练GRNN,构成动态泛回归神经网络.该动态神经网络训练方便快捷,能够满足在线预测的实时性的要求.仿真实验表明预测值较观测值有一定滞后,但均能尾随观测值而变化,达到了预期的目标.     

基于CAE和T-S神经网络的储物箱注塑工艺优化

针对塑件翘曲变形过大而导致塑件注塑失效的问题,通过运用CAE分析得出了影响翘曲变形过大的主要因素为收缩不均;采用正交试验方法获得了初步优化后参数,为Tθ(230℃)Ts(65℃)PI(70 MPa)ti(3.5 s)Ph1(60 MPa)th1(10 s)Ph1(75 MPa)th1(12 s)tc(6 s),对应的翘曲值为5.53 mm。在此基础上,再次运用GSO算法对改进的T-S模糊神经网络进行预测,得到了进一步优化的翘曲值,为3.49 mm,对应优化后的工艺参数为Tθ(230℃)Ts(68℃)PI(70 MPa)ti(4 s)Ph1(65 MPa)th1(8 s)Ph1(75 MPa)th1(14 s)tc(4 s),将优化后的工艺参数应用于实际注塑后,塑件的实效问题得到了有效解决,具有较强的实践参考价值。     

高温变换催化剂制备条件的神经网络优化

将影响低汽气比条件下LB型节能高温变换催化剂活性的主要因素作为人工神经网络的特征输入向量,将全部实验数据分为训练集和预测集,运用Matlab神经网络工具箱,按改进的Bayes自动归一化算法建立反向传播神经网络模型,不仅可防止网络陷入局部最小,而且提高了网络训练精度和泛化能力。适当拓宽正交实验各因素的水平范围,经过不同因素、不同水平间的组合模拟,预测出LB型节能高温变换催化剂的最佳制备条件为氧化铈质量分数0.76%、氧化铜质量分数5.8%、氧化铬质量分数8.6%、氧化镧质量分数1.0%、铁液浓度92 g/L、中和过程最终pH值9.5。在最佳条件下试制催化剂在低汽气比下的平均活性达77.6%。     

基于粒子群优化的磨矿分级过程多层数据协调

准确稳定的过程数据是选矿厂进行过程优化控制和决策管理的依据,今针对磨矿分级过程数据特点,建立了多层数据协调模型,包括总物料平衡层、粒度分布/品位层和不同粒度下的成分分析层(金属分布率层);针对模型维数较高的问题,引入粒子群优化(PSO)算法进行求解。根据不同的测量信息,可选择相应的层次进行协调,并采用从低层向高层逐层协调的方法,实现了部分非线性约束到线性约束的转化,提高了数据协调效率。将该多层模型和PSO算法用于某选矿厂磨矿分级过程实际生产数据的协调,结果表明协调后的数据更准确、更稳定,包含的信息更丰富完整。     

基于BP 神经网络的塑料齿轮玩具注射成型优化分析

以某塑料拼插齿轮玩具为研究对象,采用自然平衡法设计1模144腔注塑模具。对有限元模型进行合理简化,并采用Moldflow软件进行塑料齿轮注射成型过程中的流动和翘曲分析。针对初始方案中出现的熔接痕和翘曲等缺陷,建立齿轮玩具BP 人工神经网络模型,通过BP神经网络算法训练各工艺参数,并对体积收缩率和总翘曲量进行预测。将训练后较优的工艺参数组合应用于注射成型后,使得该塑料齿轮熔接痕分布改变,翘曲变形量明显降低。     

基于神经网络的丙烯腈流化床反应器的离线模拟与优化

A mathematical model is developed for an industrial acrylonitrile fluidized-bed reactor based on artificial neural networks.A new algorithm,which combines the characteristics of both genetic algorithm(GA) and generalized delta-rule(GDR) is used to train artificial neural network (ANN) in order to avoid search terminated at a local optimal solution.For searching the global optimum,a new algorithm called SM-GA,incorporating advantages of both simplex method (SM) and GA, is proposed and applied to optimize the operating conditions of an acrylonitrile fluidized-bed reactor in industry.     

多目标过程系统优化的粒子群算法求解

化工领域的过程设计、生产控制、配方和计划等众多问题的数学模型,在考虑产品性能、单位成本、环境影响等诸多因素下,都是多目标优化问题;而求解多目标优化问题,目前还没有有效的方法;现今的做法是把多目标优化通过加权转化为单目标优化,再求解单目标优化问题,但这存在权数不易确定;还忽视了有效解集中存在一个其各目标的值与各目标的最优值距离最近的有效解的问题,称为理想有效解.理想有效解的求法一般分为两步,先求各目标的最优值、再求理想有效解,这将影响求解的速度;为此提出在PSO(粒子群优化)算法中加入惩罚项,同时对PSO算法中的个体极值与全局极值作调整,使PSO算法适用于求多目标优化问题理想有效解,该算法对多目标问题起到边优化边求理想有效解的功效;这使得在求解速度上加快.通过性能测试表明了算法的有效性,最后将算法用于求解多亚甲基多苯基多胺生产过程系统优化取得良好效果.     

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.     

实验室条件下乙苯脱氢制苯乙烯反应条件研究

苯乙烯是重要的有机化工原料。随着需求量的逐渐增加,苯乙烯的生产规模在逐渐的增大,生产方法也在不断的改进。乙苯脱氢法仍是其中的主要生产方法。在实验室的条件下,采用等温式乙苯催化脱氢小型装置,考察反应温度以及水和乙苯的进料比对实验结果的影响,并根据实验结果找出实验室条件下最佳的反应温度范围以及合适的进料比范围。