化工网络中的高性能微分博弈数值优化算法

针对化工网络中大规模、非线性动态优化问题求解复杂度高、收敛难度大以及求解精度低等问题,在原始微分博弈数值优化算法的基础上开发了一套高性能的优化求解策略。该策略注重优化求解的初值生成,从而保证优化求解的大范围收敛;同时该策略还提出优化求解精确性提升算法,在提升求解精度的同时保证了优化结果的最优性。最后采用一个典型的化工网络作为仿真案例,验证了高性能优化求解策略的有效性。

全球化工网络发展概述

据有关资料统计,1999年全球已有155个国家和地区开展国际间电子商务,上网人数达到3.5亿人。1998年底,中国上网人数已达210万,比1997年增长2.5倍,电子商务网站达100多家。1999年中国上网人数达430万人,比1998年增长1倍多,2000年初,中国上网人数达890万人,又比1999年增长1.1倍,网上商店达700多家。

中国入世第一案--化工网络名所有权的诉讼案

2001年10月5日，日内瓦世界知识产权组织(wTPo)发出的一纸正式应诉通知书送到了杭州世信信息技术有限公司总经验孙德良的手中，一场跨国知识产权争端由此拉开帷幕。“如果中国的法律能够对企业的英文名称给予明确的保护的话，如果我们有足够适应WTo环境的专业人才的话，我们或许不会陷入这场跨国知识产权争端。”孙德良无奈地说，而这一切都缘起于一个价值千金的传奇“域名”。

一类化工流程网络结构的自动分析算法

通过建立序贯有向割集，对化工流程网络实现系统分割之后，从一个兼顾序贯模块法和联立模块法的流程迭代收敛行为的综合切断准则出发，借助于基本有向回路集进行优化切断，从而完成了整个流程网络结构的自动分析。这个算法思想及例题在计算机上经过编程实现，证明具有清晰、简捷快速的特点。

山西省煤化工行业网络信息公共服务平台方案设计

山西省煤化工网络信息公共服务平台借助山西省"十三五"规划把现代煤化工列为山西省九大新兴产业之一的利好形势,运用电子商务这种新兴的营销手段,通过开辟综合门户平台、手机客户端(APP)、微信公众号三大便捷服务通道,打造了一个互联互通、资源共享、服务协同的煤化工行业服务体系,为行业相关主体之间架构了一张以现代信息技术为基础的通讯服务网络,促进了山西省煤化工行业服务体系的创新与发展。

大型化工企业网络的优化发展模型

提出了大型化工企业网络的发展模型，并分析了我国不同发展条件下的两个实例。

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Prediction of pre-oxidation efficiency of refractory gold concentrate by ozone in ferric sulfate solution using artificial neural networks

An artificial neural network model was developed to predict the oxidation of refractory gold concentrate （RGC） by ozone and ferric ions. The concentration of ozone and ferric ions, pulp density, oxygen amount, leaching time and temperature were employed as inputs to the network; the output of the network was the percentage of the ferric extraction iron from RGC. The multilayered feed-forward networks were trained by 33 sets of input-output patterns using a back propagation algorithm; a three-layer network with 8 neurons in the hidden layer gave optimal results. The model gave good predictions of high correlation coefficient （R2=0.966）. The predictions by ANN are more accurate when compared with conventional multivariate regression analysis （MVRA）. In addition, calculation with ANN model indicates that temperature is the predominant parameter and ozone concentration is the lesser influential parameter in the pre-oxidation process of refractory gold ore. The ANN neural network model accurately estimates the ferric extraction during pretreatment process of RGC in gold smelter plants and can be used to optimize the process parameters.

动态博弈框架下的分布式动态优化

为了实时在线求解复杂的大规模动态优化问题,本文基于动态博弈理论提出了一种分布式动态优化方案,滚动合作博弈优化(RCGO).首先基于滚动时域优化框架,该方案将原本复杂的大规模动态优化问题分解为若干简单的小规模局部优化子问题,使得计算复杂度降低从而保证优化求解的实时性.之后本文基于动态博弈提出了分解迭代法求解各局部动态优化子问题,并对RCGO优化方案下系统稳定性进行分析.最后本文选择一个化工过程网络作为仿真案例,基于RCGO方案得到了极大化经济效益下该网络的最优操作.优化结果表明在求解复杂大规模动态优化问题时,RCGO方案较传统的集中式优化方案在由系统经济效益、闭环控制性能及优化求解实时性等组成的综合指标上有较大优势.

OPTIMIZATION METHOD ON IMPELLER MERIDIONAL CONTOUR AND 3D BLADE

An optimization method for 3D blade and meridional contour of centrifugal or mixed-flow impeller based on the 3D viscous computational fluid dynamics （CFD） analysis is proposed. The blade is indirectly parameterized using the angular momentum and calculated by inverse design method. The design variables are separated into two categories： the meridional contour design vari- ables and the blade design variables. Firstly, only the blade is optimized using genetic algorithm with the meridional contour remained constant. The artificial neural network （ANN） techniques with the training sample data schemed according to design of experiment theory are adopted to construct the response relation between the blade design variables and the impeller performance. Then, based on the ANN approximated relation between the meridional contour design variables and impeller per- formance, the meridional contour is optimized. Fewer design variables and less calculation effort is required in this method that may be widely used in the optimization of three-dimension impellers. An optimized impeller in a mixed-flow pump, where the head and the efficiency are enhanced by 12.9% and 4.5% respectively, confirms the validity of this newly proposed method.

Modeling and Optimization for Heat Exchanger Networks Synthesis Based on Expert System and Genetic Algorithm

A new superstructure form of heat exchanger networks (HEN) isproposed based on expert system system (ES). The new superstructureform is combined with the practical engineering. The differentinvestment cost formula for Different heat exchanger is alsopresented based on ES. The mathematical model for the simultaneousoptimization Of network configuration is established and solved by agenetic algorithm. This method can deal with larger scale HENsynthesis and the optimal HEN configuration is obtainedautomatically. Finally, a case study is presented to Demonstrate theeffectiveness of the method.

Simulation and Optimization for Thermally Coupled Distillation Using Artificial Neural Network and Genetic Algorithm

In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neural network based on the simulation results with ASPEN PLUS. Modified genetic algorithm was used to optimize the model. With the proposed model and optimization arithmetic, mathematical model can be calculated, decision variables and target value can be reached automatically and quickly. A practical example is used to demonstrate the algorithm.

Study on optimization control method based on artificial neural network

In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limi-tations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advan-tages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With op-timization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

Ratio of Fe-Al compound at interface of steel-backed Al-graphite semi-solid bonding plate

The ratio of Fe-Al compound at the bonding interface of solid steel plate to Al-7graphite slurry was used to characterize the interracial structure of steel-Al-7graphite semi-solid bonding plate quantitatively. The relationship between the ratio of Fe-Al compound at interface and bonding parameters （such as preheat temperature of steel plate, solid fraction of Al-7graphite slurry and rolling speed） was established by artificial neural networks perfectly. The results show that when the bonding parameters are 516 ℃ for preheat temperature of steel plate, 32.5% for solid fraction of Al-7graphite slurry and 12 mm/s for rolling speed, the reasonable ratio of Fe-Al compound corresponding to the largest interfacial shear strength of bonding plate is obtained to be 70.1%. This reasonable ratio of Fe-Al compound is a quantitative criterion of interracial embrittlement, namely, when the ratio of Fe-Al compound at interface is larger than 70.1%, interfacial embrittlement will occur.

A Method for Solving Computer-Aided Product Design Optimization Problem Based on Back Propagation Neural Network

Because of the powerful mapping ability, back propagation neural network (BP-NN) has been employed in computer-aided product design (CAPD) to establish the property prediction model. The backward problem in CAPD is to search for the appropriate structure or composition of the product with desired property, which is an optimization problem. In this paper, a global optimization method of using the a BB algorithm to solve the backward problem is presented. In particular, a convex lower bounding function is constructed for the objective function formulated with BP-NN model, and the calculation of the key parameter a is implemented by recurring to the interval Hessian matrix of the objective function. Two case studies involving the design of dopamine β-hydroxylase (DβH) inhibitors and linear low density polyethylene (LLDPE) nano composites are investigated using the proposed method.

Optimization of Fermentation Media for Enhancing Nitrite-oxidizing Activity by Artificial Neural Network Coupling Genetic Algorithm

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.

Improved wavelet neural network combined with particle swarm optimization algorithm and its application

An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.

Recovery prediction of copper oxide ore column leaching by hybrid neural genetic algorithm

The artificial neural network(ANN)and hybrid of artificial neural network and genetic algorithm(GANN)were appliedto predict the optimized conditions of column leaching of copper oxide ore with relations of input and output data.The leachingexperiments were performed in three columns with the heights of2,4and6m and in particle size of<25.4and<50.8mm.Theeffects of different operating parameters such as column height,particle size,acid flow rate and leaching time were studied tooptimize the conditions to achieve the maximum recovery of copper using column leaching in pilot scale.It was found that therecovery increased with increasing the acid flow rate and leaching time and decreasing particle size and column height.Theefficiency of GANN and ANN algorithms was compared with each other.The results showed that GANN is more efficient than ANNin predicting copper recovery.The proposed model can be used to predict the Cu recovery with a reasonable error.

Dimensionality Reduction with Input Training Neural Network and Its Application in Chemical Process Modelling

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 im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum 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 propyl-ene 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.