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A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier

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Chinese Journal of chemical Engineering 2015年第9期23卷 1484-1501页

作者：曹萃文张亚坤于腾顾幸生辛忠李杰 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology State Key Laboratory of Chemical Engineering East China University of Science and Technology State Key Laboratory of Multiphase Complex Systems Institute of Processing Engineering Chinese Academy of Sciences

Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP(Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm(DE), genetic algorithm(GA), and particle swarm optimization algorithm(PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO(3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algorithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effectively. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms(EAs) can be improved,and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coalwater slurry gasifier shows outstanding computing results than actual industry use and other algorithms.

关键词： 3-Layer mixed cultural evolutionary framework Optimal operation Syngas production Coal-water slurry gasifier

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Soft-sensing of turbine steam flow based on CQGAPSO

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Huagong Xuebao/CIESC Journal 2010年第11期61卷 2855-2860页

作者： Tang, Qifeng Zhao, Liang Qi, Rongbin Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

Turbine steam flow is an important parameter for analyzing turbine operating efficiency. In order to solve such problems as lack of detection information, poor reliability of traditional measurement method, high cost and difficult installation of non-contact measurement method etc., a soft-sensing model based on the cooperative quantum genetic algorithm particle swarm optimization(CQGAPSO) and a well-defined part-connected neural network(NN) was proposed. It could eliminate some adverse effects of approximation ability caused by redundant structure of NN. The amplitude-based coding method and cooperation mechanism improved the learning efficiency, approximation precision and generalization of NN. The soft-sensing experimental results of turbine steam flow showed that the soft-sensing model of turbine steam flow could obtain higher prediction accuracy and greater robustness than the all-connection NN and other models. © All Rights Reserved.

关键词： Steam turbines

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Just-in-time learning for cement free lime prediction with empirical mode decomposition and database monitoring index*

Just-in-time learning for cement free lime prediction with e...

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Asian control Conference

作者： Jinquan Zheng Wenli Du Weimin Zhong Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781728102634

Free lime (f-Cao) content is the key quality indicator in the control and optimization of cement calcination process. However, the cement process has many complex characteristics including large noise, time delay, nonlinearity, and time-varying system. To cope with the above characteristics, this study proposes just-in-time (JIT) learning with empirical mode decomposition (EMD) and database monitoring index (DMI) for free lime prediction. At first, EMD and Savitzky-Golay (S-G) filter can effectively deal with process large noise. Meanwhile, weighted time series analysis and mutual information are used to estimate large time delay. Then, least squares support vector machine (LSSVM) based on JIT and DMI is employed to solve the problems of process nonlinearity and time variation. The proposed strategy is validated on practical data from a cement plant.

关键词： Calcination Kilns Delay effects Mutual information Monitoring Indexes

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Data-driven Scheduling optimization of Ethylene Cracking Furnace System

Data-driven Scheduling Optimization of Ethylene Cracking Fur...

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Chinese control and Decision Conference, CCDC

作者： Xinwei Lin Liang Zhao Wenli Du Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

The ethylene cracking furnace system convert a variety of hydrocarbon feeds into products such as ethylene and propylene in parallel. During the cracking process, the coke produced by the cracking will degrade the performance of the cracking furnace, so the furnace should be cleaned periodically. Scheduling of the cracking furnace system involves various raw materials, multiple furnaces, and multiple constraints, so it is a difficult mathematic programming problem. Previous studies have simplified the optimization model to obtain a corresponding mixed integer nonlinear programming (MINLP) model. In order to make the scheduling more suitable for the actual process, this paper develops the transfer-line exchanger (TLE) model, fuel consumption model, and super-high pressure steam (SS) model by using the actual plant data. A case study is put forward to demonstrate the effectiveness of the proposed method.

关键词： Furnaces Feeds Mathematical model Job shop scheduling Raw materials optimization Fuels

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Synchronization of Nonlinear Coupled Networks with Time-delay via Distributed Impulsive control

Synchronization of Nonlinear Coupled Networks with Time-dela...

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第三十二届中国控制会议

作者： ZHANG Hao FENG Gang YAN Huaicheng CHEN Qijun Department of Control Science and Engineering Tongji University Department of Mechanical & Biomedical Engineering City University of Hong Kong Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering East China University of Science and Technology

ISBN: (纸本)9781479900305

In this paper,the synchronization of a nonlinear network with time-varying coupling delay is investigated via distributed impulsive *** objective is to design the distributed impulsive controller with minimum coupling strength such that the nonlinear network with coupling delay is globally exponentially *** sufficient conditions have been derived based on Lyapunov-Razumikhin method in terms of matrix *** example is presented to illustrate the effectiveness of the proposed control methods.

关键词： Global exponential synchronization Distributed impulsive control Time-varying delay Coupled network

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Support vector regression based on multi-scale wavelet kernel for propylene concentration estimation and application

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Huagong Xuebao/CIESC Journal 2010年第6期61卷 1486-1494页

作者： Yu, Yanfang Du, Wenli Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

Support vector regression based on multi-scale wavelet kernel has strong robustness and good generalization ability, but it is critical for it to choose appropriate model parameters. Obviously, the multi-scale kernel method has more difficulty in model selection than the single kernel approach. This paper proposed an approach about how to develop support vector regression based on multi-scale wavelet kernel. It applied quantum clustering to data partition in order to determine the scale parameter of the multi-scale kernel, resorted to the support vector data description algorithm to calculate the kernel width of the corresponding data points, and then used cultural algorithms to optimize the kernel width and the remaining parameters. The results showed that the multi-scale kernel method outperformed the single wavelet approach and the Gaussian method. The experiments about three regression data sets-Boston housing, Bodyfat and Santa demonstrated that in contrast with the Gaussian approach, the present multi-scale wavelet support vector regression made the mean squared error of test sets decrease by 6.8%, 62.0% and 91.3%, respectively. Meanwhile, the proposed approach exhibited good generalization ability for propylene concentration estimation in the bottom byproduct of propylene fractionation tower. It not only enabled the model output of training set for propylene concentration to show little difference with the actual output, but also made the relative error of test set down to 0.211. Compared with other methods, it had the minimal prediction error. © All Rights Reserved.

关键词： Errors

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Surrogate model of aromatic isomerization process based on adaptive sampling algorithm

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Huagong Xuebao/CIESC Journal 2020年第2期71卷 688-697页

作者： Xie, Yuheng Li, Zhi Yang, Minglei Du, Wenli State Key Laborotory of Chemical Engineering Key Laboratory of Advanced Control and Optimization for Chemical Processed of Ministry of Education East China University of Science and Technology Shanghai200237 China

Isomerization is an important part in the production of aromatics. Improving the modeling and optimization efficiency of isomerization is of great significance to industrial production. However, the optimization process using the mechanism model directly takes a long time and the optimization efficiency is low. The surrogate model can effectively approximate the mechanism model and greatly reduce the computation time. The surrogate model sampling method has a great influence on the accuracy of the model. A new adaptive sampling algorithm based on sparsity and nearest neighbor expectation is proposed in this paper, which can balance global search and local search. By solving the optimization problem, we can find new sampling points that reflect the key information of the object function, and then add the new point to the sample set, so that the accuracy of the surrogate model is continuously improved. The results of multiple test functions show that compared with other adaptive sampling algorithms, the algorithm can effectively improve the accuracy and modeling efficiency of the surrogate model. The algorithm is also validated in the aromatic isomerization surrogate model. Compared with other algorithms, our algorithm model error is reduced by more than 5%, and the modeling time is reduced by more than 30%. © All Right Reserved.

关键词： Isomerization

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Solution evaporation processed high quality perovskite films

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Science Bulletin 2018年第23期63卷 1591-1596页

作者： Aqiang Liu Kang Liu Huimin Zhou Hongmei Li Xiaoqing Qiu Yang Yi Min Liu School of Physics and Electronics and Key Laboratory of Low-dimensional Quantum Structures and Quantum Control of the Ministry of Education Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Institute of Super-microstructure and Ultrafast Process in Advanced Materials School of Physics and Electronics Central South University School of Chemistry and Chemical Engineering Central South University State Key Laboratory of Powder Metallurgy Central South University

Organic-inorganic hybrid perovskite solar cells(PSCs), due to their high power conversion efficiency(PCE) and low cost, have been considered as one of the most promising photovoltaic devices. Welldistributed large-grain perovskite crystals play an important role for light adsorption and charge ***, high quality perovskite crystals closely relate to the preparation method. Generally, the preparation of perovskite films requires a nitrogen atmosphere and toxic anti-solvents, which hinder their practical applications. Here, we reported a novel and simple solution evaporation process followed with a methylammonium iodide(MAI) solution immersion to prepare high quality perovskite films. Porous lead iodide(PbI_2) films were firstly formed by evaporation of PbI_2 precursor solution. The obtained porous PbI_2 films were completely transformed into well-distributed large-grain perovskite films after a quick MAI solution immersion. As a result, the corresponding PCE was enhanced by 30% compared to that prepared with a regular sequential deposition. This solution evaporation method provides a convenient and practical way for preparing high-quality perovskite films.

关键词： Perovskite Solution evaporation High-quality films Power conversion efficiency

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Gas Leakage Segmentation in Industrial Plants

Gas Leakage Segmentation in Industrial Plants

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Chinese Automation Congress (CAC)

作者： Haoqi Lin Xiaojing Gu Jingyu Hu Xingsheng Gu Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Industrial gas leakage in chemical plants presents a high risk to health and safety, also makes a huge waste of production materials. In this paper, we propose an automatic gas segmentation method for optical gas imaging (OGI) videos, which can not only indicate the existence of leakage in frames but also show the precise locations and shapes of gas plumes. Specifically, we propose a novel video segmentation network that stacks 2D spatial convolutions, 1D temporal convolutions and 3D spatial-temporal convolutions together within an encoder-decoder structure, termed 2.5D-Unet. Stacking mixed convolutions increases the representation ability of network for leakage's appearance and motion. More importantly, stacking mixed convolutions facilitates pre-training of model using still images of smoke, which is especially useful when lacking of labeled videos of leaked gas. Experimental results show, for OGI video-based gas leakage segmentation, our method outperforms existing video segmentation methods.

关键词： Videos Feature extraction Image segmentation chemicals Data mining Three-dimensional displays Task analysis

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Evolutionary algorithm based on the evolution of Pareto archive and individual migration

Evolutionary algorithm based on the evolution of Pareto arch...

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作者： Qi, Rongbin Ma, Tianyi Sun, Fan Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China Shanghai Business School Shanghai China

An evolutionary algorithm based on the parallel evolution of multiple single objective populations and Pareto archive population is proposed, which is not only suitable for solving multi-objective optimization, but also effective for multimodal function. For each single objective population, single objective evolutionary algorithm is applied to optimize separately each of multi-objective functions, where individuals generated by tournament selection from the union of single objective and Pareto Archive population form the single objective population of next generation. Especially, individuals in Pareto archive population also join evolutionary operations. Simulations manifest that the proposed method can realize the search from multiple directions to obtain the non-dominated solutions scattered more uniformly over the Pareto frontier with better convergence metric compared to well-known NSGA-II algorithm. Individual migration from Pareto archive population by tournament selection is also proved to have the advantage in improving the converging speed and converging precision. Moreover, for multimodal single objective function, simulations also show that ideal optimizing solution can be obtained by properly separating single objective function into multi-objective function and applying the above method. © 2011 Zhejiang University.

关键词： Evolutionary algorithms

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