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第23届过程控制会议

作者： Xiang Xie Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of

For complex industrial processes with multiple operating conditions, it is important to develop effective monitoring algorithms to ensure the safety of the producing processes. This paper proposes a novel monitoring strategy based on fuzzy c-means (FCM). First, the high dimensional historical data are transferred to a low dimensional subspace space by locality preserving projection (LPP). Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operating mode. After that, the distance statistics of each cluster are integrated though the membership values into a novel BID monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman (TE) benchmark process.

关键词： Multimode process monitoring fuzzy c-means locality preserving projection integrated monitoring index Tennessee Eastman process.

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An Auto-tuning method for aeration control in activated sludge wastewater treatment processes

An Auto-tuning method for aeration control in activated slud...

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2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Nascu, Ioana Du, Wenli Nascu, Ioan Technical University of Cluj Napoca Automation Department Cluj-Napoca Romania Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China Ministry of Education East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this paper, a relay-feedback PID auto-tuning method and applications related to conventional Wastewater Treatment Plants are presented. The developed method has two steps: identification of the process model parameters and PID controller tuning. First, to identify two points on the process Nyquist curve, a relay-feedback based experiment is proposed. A linear model of second order is obtained and this model is further used for the tuning of the PID controller. The performances of the control algorithms are illustrated by simulation results. Both the reference tracking and the disturbance rejection capability have been tested and analyzed. The impact of changing a design parameter in the closed loop system behavior has also been explored. © 2022 IEEE.

关键词： Wastewater treatment

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A novel deep quality-supervised regularized autoencoder model for quality-relevant fault detection

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Science China(Information Sciences) 2022年第5期65卷 280-282页

作者： Zhichao LI Li TIAN Xuefeng YAN Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Dear editor,In the industrial processes, timely detection of key quality variables is very important for tracking the product quality, monitoring the process status, and achieving stable and reliable control. However,... 详细信息

关键词： supervised learning deep learning regularized autoencoder process monitoring quality-relevant fault detection

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Integral control on nonlinear spaces: two extensions

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IFAC-PapersOnLine 2016年第18期49卷 1000-1005页

作者： Zhang, Zhifei Ling, Zhihao Sarlette, Alain Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Department of Electronics & Information Systems Ghent University Shanghai China Key Laboratory of A.C.O.C.P. ECUST Shanghai China Department of Electronics & Information Systems Ghent University QUANTIC lab INRIA Paris France

This paper applies the recently developed framework for integral control on nonlinear spaces to two non-standard cases. First, we show that perfect target stabilization in presence of actuation bias holds also if this bias is state dependent. This might not be surprising, but in practice it provides an easy way to robustly cancel nonlinear dynamics of the uncontrolled plant. We specifically illustrate this for robust stabilization of a pendulum at arbitrary angle. Second, as previous work has been restricted to systems with as many control inputs as configuration dimensions, we here provide results for integral control of a non-holonomic system. More precisely, we design robust steering control of a rigid body under velocity bias. © 2016

关键词： Feedback linearization Lie groups Stabilization Arbitrary angles control inputs Geometric control Integral control Nonholonomic systems Robust stabilization State dependent Steering control

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Self-adaptive iterative hybrid modeling and its application in acetylene hydrogenation process

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Huagong Xuebao/CIESC Journal 2018年第11期69卷 4814-4822页

作者： Guo, Jingjing Xu, Jinjin Du, Wenli Ye, Zhencheng State Key Laboratory of Chemical Engineering Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

The reaction mechanism of chemical process is complex. There is a modeling error between the mechanism model and the actual reaction system. At the same time, there are complex slow time-varying features, such as catalyst deactivation, fuel coking, etc. Thus there will be a mismatch between the process model and the actual process system. A self-adaptive iterative hybrid model (SAIHM) is established to reflect the dynamic characteristics of the process accurately over a long period. The mechanism model and the data-driven model are effectively combined to improve the prediction accuracy of the model;the data-driven model uses the deep recurrent neural network (DRNN) to fully exploit the timing relationship between adjacent conditions;the data-driven model is automatically updated based on the evaluation indicators to resolve the contradiction between accuracy and efficiency. The simulation and comparison results of the self-adaptive iterative hybrid model and the existing mechanism model established based on the historical operation data of an acetylene hydrogenation adiabatic reactor show that the self-adaptive iterative hybrid model can more effectively track the actual system. © All Right Reserved.

关键词： Dynamics

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Feature extraction of vigilance level based on Heart Rate Variability of Electrocardiogram

Feature extraction of vigilance level based on Heart Rate Va...

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2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011

作者： Yu, Shanshan Wang, Bei Wang, Yujue Wang, Xingyu Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education No. 130 Meilong St. Shanghai 200237 China

ISBN: (纸本)9781424493524

This paper presented a simple method of detecting the peak of R wave in Electrocardiogram (ECG) signal and computing the Heart Rate Variability (HRV). Features were extracted from the obtained HRV to analyze the vigilance level during day time short nap sleep. Firstly, the second derivative of ECG signal was calculated and clustered in order to detect the peak of R wave. Secondly, HRV was calculated from the subtraction of two adjoining R waves. Finally, several features were extracted based on HRV in time domain and frequency domain. The variation of vigilance level during the day time short nap was analyzed based on the extracted features. The result showed that the accuracy of peak detection of R wave in ECG signal was about 99.5%. The extracted features can reflect the changing of vigilance level and be useful for sleep stage analysis. © 2011 IEEE.

关键词： Electrocardiograms

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A new particle swarm optimization and the application in the soft sensor modeling

A new particle swarm optimization and the application in the...

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2010 8th IEEE International Conference on control and Automation, ICCA 2010

作者： Dang, Mingmei Wang, Zhenlei Qian, Feng East China University of Science and Technology China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781424451951

Aiming at the disadvantages of the standard Particle Swarm Optimization (PSO), a new particle swarm optimization algorithm based on dual mutation(DDPSO) is proposed. By comparing and analyzing the results of several Benchmark functions, the excellent performance of PSO is proved. The improved PSO is applied to optimize the structure and parameters in artificial neural network(ANN). The availability of algorithm optimizing neural network is proved by applying ANN in soft sensor modeling of propylene concentration measurement. © 2010 IEEE.

关键词： Neural networks

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Novel control Vector Parameterization Method with Differential Evolution Algorithm and Its Application in Dynamic Optimization of chemical processes

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Chinese Journal of chemical Engineering 2013年第1期21卷 64-71页

作者：孙帆钟伟民程辉钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes (Ministry of Education) East China University of Science and Technology Department of Automation East China University of Science and Technology

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 parameteri- zation （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 oroposed methods.

关键词： control vector pararneterization differential evolution algorithm dynamic optimization chemical processes

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Three Stage Equilibrium Model for Coal Gasification in Entrained Flow Gasifiers Based on Aspen Plus

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Chinese Journal of chemical Engineering 2013年第1期21卷 79-84页

作者：孔祥东钟伟民杜文莉钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes (Ministry of Education) East China University of Science and Technology Automation Institute East China University of Science and Technology

A three stage equilibrium model is developed for coal gasification in the Texaco type coal gasifiersbased on Aspen Plus to calculate the composition of product gas, carbon conversion, and gasification teml^erature. The model is divided into three stages including pyrolysis and combustion stage, char gas reaction stage, and gas *** reaction stage. Part of the water produced in thepyrolysis and combust!on stag.e is assumed to be involved inthe second stage to react with the unburned carbon. Carbon conversion is then estimated in the second stage by steam participation ratio expressed as a function of temperature. And the gas product compositions are calculated from gas phase reactions in the third stage. The simulation results are consistent with published experimental data.

关键词： coal gasification Texaco gasifier equilibrium model carbon conversion product gas composition

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Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation

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Chinese Journal of chemical Engineering 2012年第6期20卷 1047-1052页

作者：陶莉莉孔祥东钟伟民钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology Automation Institute East China University of Science and Technology

In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.

关键词： self-adaptive immune genetic algorithm artificial neural network measurement p-xylene oxidation process

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