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Smart and Optimal Manufacturing： The key for the Transformation andDevelopment of the Process Industry

Engineering 2017年第2期3卷 151-151页

作者： Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and TechnologyShanghai 200237 China

In recent years, smart manufactur-ing has been the focus of competitionand collaboration between countriesaround the world. The major developedand developing countries are speedingup their strategic planning and layoutfor smart manufacturing. Germany, theUnited States, and China have releasedthe Industry 4.0, Smart Process Man-ufacturing, and Made in China 2025initiatives, respectively, which aim ata deep integration of information andcommunication technology and man-ufacturing technology, thus achieving seamless integration of prod-ucts, equipment, humans, and organizations.

关键词：期刊编辑工作人物介绍工程

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Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control

arXiv

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arXiv 2018年

作者： Li, Wenqing Zhao, Chunhui Huang, Biao State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G-2V4 Canada

For large-scale industrial processes under closed-loop control, process dynamics directly resulting from control action are typical characteristics and may show different behaviors between real faults and normal changes of operating conditions. However, conventional distributed monitoring approaches do not consider the closed-loop control mechanism and only explore static characteristics, which thus are incapable of distinguishing between real process faults and nominal changes of operating conditions, leading to unnecessary alarms. In this regard, this paper proposes a distributed monitoring method for closed-loop industrial processes by concurrently exploring static and dynamic characteristics. First, the large-scale closed-loop process is decomposed into several subsystems by developing a sparse slow feature analysis (SSFA) algorithm which capture changes of both static and dynamic information. Second, distributed models are developed to separately capture static and dynamic characteristics from the local and global aspects. Based on the distributed monitoring system, a two-level monitoring strategy is proposed to check different influences on process characteristics resulting from changes of the operating conditions and control action, and thus the two changes can be well distinguished from each other. Case studies are conducted based on both benchmark data and real industrial process data to illustrate the effectiveness of the proposed method. Copyright © 2018, The Authors. All rights reserved.

关键词： Process monitoring

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Modeling Method of Data-driven Model Combined with A Priori Knowledge and Its Application in Average Particle Size Estimation of Composite Colloidal Sols

Modeling Method of Data-driven Model Combined with A Priori ...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Yang Zhou Wenxi Xu Shaojun Li Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and TechnologyMinistry of Education

We present a universally applicable hybrid modeling method for nonlinear industrial processes that combine the a priori process knowledge with a data-driven *** method constructs a unified framework for the modeling process by integrating a data-driven modeling technique,sampling detection technique,constraint optimization problem,and an evolutionary *** the modeling process,a swarm intelligence algorithm is used to optimize the model parameters under the circumstances of satisfying the constraints of a priori *** adding the constraints of process a priori knowledge,we can obtain more information about the actual process and avoid the over-fitting problem to some extent,especially when modeling a system with a small quantity of *** order to show the effectiveness of the method proposed in this paper,two general data-driven models,the polynomial regression model and radial basis function network model,are used as case ***,a function simulation experiment is designed to test effectiveness,and applied to estimate average particle size of ZrO2-TiO2 composite colloidal sols.

关键词： Data-driven modeling A priori knowledge Constraint handling Intelligence algorithm Hybrid model

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Non-Gaussian dynamic batch process monitoring based on ICA-JITL-MSSVDD

Non-Gaussian dynamic batch process monitoring based on ICA-J...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Zhaomin Lv Xuefeng Yan Qingchao Jiang .Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

To solve the non-Gaussian and time-varying dynamics problems of batch process,a new monitoring method called Independent component analysis and Just-in-time learning multiple subspace support vector data description(ICA-JITL-MSSVDD) is *** offline,a new multiple variable subspaces division method used for batch process based on Independent component analysis(ICA) is *** proposed multiple subspace division method is used as a global non-Gaussian modeling *** online,Just-in-time learning multiple(JITL) is employed to extract similar samples,and multiple subspace support vector data description(MSSVDD) models are *** method can solve the time-varying dynamics problem and MSSVDD is used as a local non-Gaussian modeling *** the integration of global and local non-Gaussian modeling methods,the non-Gaussian and time-varying dynamics problems of batch process can be *** fault detection effectiveness of ICA-JITL-MSSVDD can be demonstrated by simulating the fed-batch penicillin fermentation process.

关键词： Batch process monitoring Independent component analysis Just in time learning Multiple subspace Support vector data description

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A novel fault diagnosis approach using hidden Markov model for a multimode process

A novel fault diagnosis approach using hidden Markov model f...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Fan Wang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

chemical processes with multiple operational modes are common in modern process *** fault diagnosis methods meet challenges when facing multimodal complex data *** order to tackle this challenge,a new fault diagnosis approach utilizing hidden Markov model(HMM) is developed in this paper.A HMM trained by normal process data with multimodal feature has several hidden *** operating mode is represented by a hidden state which should be described via a finite Gaussian mixture *** multivariate Gaussian mixture distribution targets complex data distribution within each individual ***,for a monitored sample,the Mahalanobis distance(MD) inside each Gaussian component is constructed as monitoring *** contribution index is obtained through the differential decomposition along every variable direction to MD-based monitoring *** last,the hidden state Gaussian component probability strategy is used to integrate local monitoring index and local variable contribution index across all Gaussian components into a global monitoring indicator and a global variable contribution *** derived variable contribution index with probabilistic feature is employed to locate faulty *** to a numerical simulation and the Tennessee Eastman(TE) process display the fault detection and diagnosis performance of our proposed method.

关键词： Hidden Markov model Fault diagnosis Multimode process Fault detection

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Total Plant Performance Evaluation based on Big Data:Visualization Analysis of TE Process

Total Plant Performance Evaluation based on Big Data:Visuali...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Li Mengyao Du Wenli Qian Feng Zhong Weiming Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

The performance evaluation of the process industry,which has been a popular topic nowadays,can not only find the weakness and verify the resilience and reliability of the process,but also provide some suggestions to improve the process benefits and ***,the performance assessment principally concentrate upon some parts of the entire system at present,for example the controller *** some researches focus on the whole process,they aim at discovering the relationships between profit,society,policies and so forth,instead of relations between overall performance and some manipulated variables,that is,the total plant *** this basis,this paper propose a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the *** variables related to safety and profits are regarded as key factors to performance *** better monitor the process state and observe the performance variation trend of the process,a classification-visualization method based on kernel principal component analysis(KPCA) and self-organizing map(SOM) is *** monitoring method is applied to Tennessee Eastman *** results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.

关键词： performance evaluation structured logic rules hierarchical framework multidimensional visualization KPCA-SOM

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Early Stopping Criterion Combining Probability Density Function with Validation Error for Improving the Generalization Capability of the Backpropagation Neural Network

Early Stopping Criterion Combining Probability Density Funct...

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2017 2nd International Conference on Applied Mechanics and Mechatronics Engineering(AMME 2017)

作者： Wei WANG Xue-feng YAN The Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

A new early stopping criterion that combines the probability density function error and the validation error was designed to evaluate a training process. In this new criterion, the real status of the backpropagation neural network(BPNN) was jointly evaluated by the validation error, the probability density error of the traditional training data, and the unlabeled data that was not considered in the traditional training process to determine the appropriate stopping time. The proposed approach was tested on three classes of automatic stopping criteria. In addition, it was compared with the stopping criteria based solely on the validation error and the probability density error with the use of four different data sets selected from the UCI Machine Learning *** showed that the new early stopping criterion is beneficial for enhancing the generalization capability of the BPNN, demonstrating an improved forecasting precision by 2%-3%.

关键词： Backpropagation neural network Early stopping criterion Generalization capability Probability density function

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Lithium Metal Anodes: A Triple-Gradient Host for Long Cycling Lithium Metal Anodes at Ultrahigh Current Density (Small 30/2020)

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Small 2020年第30期16卷

作者： TrungHieu Le Qinghua Liang Ming Chen Ciqing Yang Zhihao Yu Jie Cheng Feiyu Kang Ying Yang State Key Laboratory of Control and Simulation of Power System and Generation Equipments Tsinghua University Beijing 100084 China Department of Chemical Engineering The University of Melbourne Parkville VIC 3010 Australia Zhejiang Yuyuan Energy Storage Technology Co. Ltd. Huzhou 313100 China Laboratory of Advanced Materials Department of Materials Science and Engineering Tsinghua University Beijing 100084 China

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Global diversity and biogeography of bacterial communities in wastewater treatment plants (vol 4, pg 1183, 2019)

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NATURE MICROBIOLOGY 2019年第12期4卷 2579-2579页

作者： Wu, Linwei Ning, Daliang Zhang, Bing Li, Yong Zhang, Ping Shan, Xiaoyu Zhang, Qiuting Brown, Mathew Robert Li, Zhenxin Van Nostrand, Joy D. Ling, Fangqiong Xiao, Naijia Zhang, Ya Vierheilig, Julia Wells, George F. Yang, Yunfeng Deng, Ye Tu, Qichao Wang, Aijie Zhang, Tong He, Zhili Keller, Jurg Nielsen, Per H. Alvarez, Pedro J. J. Criddle, Craig S. Wagner, Michael Tiedje, James M. He, Qiang Curtis, Thomas P. Stahl, David A. Alvarez-Cohen, Lisa Rittmann, Bruce E. Wen, Xianghua Zhou, Jizhong State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing China Institute for Environmental Genomics Department of Microbiology and Plant Biology and School of Civil Engineering and Environmental Sciences University of Oklahoma Norman OK USA Consolidated Core Laboratory University of Oklahoma Norman OK USA Environmental Biotechnology Laboratory The University of Hong Kong Hong Kong China School of Environment Northeastern Normal University Changchun China School of Engineering Newcastle University Newcastle upon Tyne UK Department of Energy Environmental and Chemical Engineering Washington University in St Louis St Louis MO USA Department of Microbiology and Ecosystem Science Division of Microbial Ecology Research Network ''Chemistry meets Microbiology'' University of Vienna Vienna Austria Karl Landsteiner University of Health Sciences Division of Water Quality and Health Krems Austria and Interuniversity Cooperation Centre for Water and Health Krems Austria Department of Civil and Environmental Engineering Northwestern University Evanston IL USA Institute for Marine Science and Technology Shandong University Qingdao China Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing China Department of Civil and Environmental Engineering The University of Tennessee Knoxville TN USA. Institute for a Secure and Sustainable Environment The University of Tennessee Knoxville TN US Advanced Water Management Centre The University of Queensland Brisbane Queensland Australia Department of Chemistry and Bioscience Center for Microbial Communities Aalborg University Aalborg Denmark Department of Civil and Environmental Engineering Rice University Houston TX USA Department of Civil and Environmental Engineering Stanford University Stanford CA USA Center for Microbial Ecology Michigan State University East Lansing MI USA School of Engineering Newcastle University

Microorganisms in wastewater treatment plants (WWTPs) are essential for water purification to protect public and environmental health. However, the diversity of microorganisms and the factors that control it are poorly understood. Using a systematic global-sampling effort, we analysed the 16S ribosomal RNA gene sequences from ~1,200 activated sludge samples taken from 269 WWTPs in 23 countries on 6 continents. Our analyses revealed that the global activated sludge bacterial communities contain ~1 billion bacterial phylotypes with a Poisson lognormal diversity distribution. Despite this high diversity, activated sludge has a small, global core bacterial community (n = 28 operational taxonomic units) that is strongly linked to activated sludge performance. Meta-analyses with global datasets associate the activated sludge microbiomes most closely to freshwater populations. In contrast to macroorganism diversity, activated sludge bacterial communities show no latitudinal gradient. Furthermore, their spatial turnover is scale-dependent and appears to be largely driven by stochastic processes (dispersal and drift), although deterministic factors (temperature and organic input) are also important. Our findings enhance our mechanistic understanding of the global diversity and biogeography of activated sludge bacterial communities within a theoretical ecology framework and have important implications for microbial ecology and wastewater treatment processes.

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Fuzzy-Model-Based Leader-Follower Consensus of Nonlinear Multi-Agent Systems with Input Saturation

Fuzzy-Model-Based Leader-Follower Consensus of Nonlinear Mul...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Xiaolu Liu Duxin Chen Yan-Wu Wang Huaicheng Yan School of Automation Huazhong University of Science and Technology Wuhan P. R. China School of Mathematics Southeast University Nanjing P. R. China Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology Shanghai P. R. China

This paper presents a fuzzy-model-based method for solving the consensus problem of a class of nonlinear multi-agent systems (MASs) with input saturation. Since each agent has nonlinear dynamics, the system is not asymptotically null controllable with bounded controls (ANCBC). Therefore, the widely-used low-gain feedback method for designing consensus protocols of MASs with input saturation can no longer work. To this end, the Takagi-Sugeno (T-S) fuzzy model is adopted to formulate the error dynamics of those nonlinear follower agents with input saturation as well as a leader with time-varying states. Accordingly, by using the properties of convex hull, a set of invariance condition in the format of linear matrix inequality (LMI) is designed. Furthermore, by enlarging the shape reference set, the estimation of the attraction domain can be obtained. Simultaneously, by viewing the control gain as an extra free parameter in the LMI optimization procedure, the leader-follower consensus algorithm is proposed, which guarantees that all followers with input saturation can track the leader, and they can asymptotically reach consensus. Finally, numerical experiments validate the effectiveness of the proposed anti-saturation consensus algorithm.

关键词： Automation Nonlinear dynamical systems STEM Actuators Symmetric matrices

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