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第三十四届中国控制会议

作者： Yuan HU Yugang NIU Bei Chen Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education (East China University of Science and Technology) Central academy Shanghai Electric Group Co. Ltd.

ISBN: (纸本)9781467374439

Clustering is an energy efficient routing protocol for wireless sensor networks. Traditional clustering methods can prolong the network lifetime and achieve scalable performance, but they do not consider the event development. In many applications, the scalability and occurrence region of events often change. A dynamic clustering method with overlaps(DCMO) is proposed in this paper. Due to the 2-logical-coverage overlaps of the proposed clustering method, the clusters can be migrated with the changing tendency of events. As a result, the sensed data can be transmitted at a lower price. Simulations show that the proposed DCMO method has lower energy consumption, compared with LEACH protocol.

关键词： Clustering,event development,2-logical-coverage overlaps,event detection

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Simulation of distillation unit based on artificial immune network multi-agent algorithm

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Huagong Xuebao/CIESC Journal 2010年第12期61卷 3186-3192页

作者： Shi, Xuhua 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 Research Institute of Electric Automatic Control Ningbo University Zhejiang Ningbo 315211 China

In petrochemical field, the process simulation for distillation is an important task. The key parameter in the distillation process simulation is the tray efficiency, which can not be obtained easily. Thus the determination of appropriate tray efficiency is an important issue. In this study, artificial immune network multi-agent optimization strategy(Maopt-aiNet), which combines immune mechanics and multi-agent technology, is used to determine the Murphree efficiency. The main search operators of Maopt-aiNet include neighborhood clonal selection, neighborhood competition, self-confidence motivation, self-confidence neighborhood learning, and neighborhood collaborative operators. Based on the process and analysis data of the distillation unit, Maopt-aiNet is applied to determine the Murphree efficiency for each stage and to minimize the square summation of model's analog relative error of the stage temperature. The experimental results show that with the tray efficiency determined by Maopt-aiNet the model fits the actual distillation unit fairly well. The method can be used to guide the operation of the distillation process. © All Rights Reserved.

关键词： Distillation

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Identification of Abnormal Operating Conditions in Fused Magnesium Furnaces Using a Deep Multiview TSK Fuzzy System

Identification of Abnormal Operating Conditions in Fused Mag...

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2024 China Automation Congress, CAC 2024

作者： Xi, Chao Wu, Gaochang Fan, Zizhu Xu, Feng Wang, Hui Tang, Xiaokun School of Electrical and Automation Engineering East China Jiaotong University Nanchang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang China

ISBN: (纸本)9798350368604

The smelting process of the fused magnesium furnace(FMF) is prone to semi-molten abnormal conditions, which may lead to low product quality and production efficiency, and is also prone to safety accidents. At present, the operating conditions of FMF mainly rely on manual observation of the furnace shell features for diagnosis, and it is hard to timely and effectively identify abnormalities in the smelting process. Given the difficulty in identifying abnormal operating conditions of FMF, this paper proposes a deep multiview fuzzy classification method(DMFC) that can automatically identify abnormal operating conditions. First, based on the three-phase current data, CNN and Transformer are used to extract features as multiple views. Secondly, the Takagi-Sugeno-Kang(TSK) fuzzy model is used as a single-view model, and the evidence theory is used for dynamic fusion of multiple views. This method can effectively fuse multiple deep features, and has strong scalability and a certain degree of interpretability. Through verification of the real historical data of multiple FMFs, our model has relatively high accuracy, false detection rate(FDR), and missed detection rate(MDR). © 2024 IEEE.

关键词： Smelting

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Predictive control for Continuous Stirred Tank Reactors

Predictive Control for Continuous Stirred Tank Reactors

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2019 International Conference on Informatics, control and Robotics 2019信息学、控制和机器人学国际会议(ICICR2019)

作者： Ioana NASCU Ioan NASCU Wen-li DU Sai GU Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Department of Chemical and Process Engineering University of Surrey Department of Automation Technical University of Cluj-Napoca

The paper focuses on the implementation of a model based predictive control(MBPC) method, for Continuous Stirred Tank Reactors. First, the modelling problem of a single irreversible exothermic reaction, taking place in a perfectly mixed continuously stirred tank reactor(CSTR) is presented. The dynamic model consists of differential material and energy balance equations. The control strategy is investigated and evaluated by performing simulations and analyzing the results. The disturbance rejection capacity of the control system(regulatory control performances) have been tested and compared with those obtained using a classical Proportional Integral Derivative(PID) based controller. The results show that this control strategy has good performances and can be efficiently used to control the CSTR.

关键词： Predictive control PID control chemical reactor process model

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Brain control: Human-computer Integration control Based on Brain-computer Interface Approach

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Acta Automatica Sinica 2013年第3期39卷 208-221页

作者： Xing-Yu WANG Jing JIN Yu ZHANG Bei WANG Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education School of Information science and Technology East China University of Science and Technology Shanghai 200237 China

Recently, a new system called brain control system has been developed rapidly. Brain control system is a human-computer integration control system based on brain-computer interface (BCI), which relies on human's ideas and thinking. Brain control system has been successfully applied in wide fields, assisting disabled patients daily life, training patients with stroke or limb injury, monitoring the state of human operator, as well as entertainment and smart house etc. In this paper, the background, basic principle, system structure and developments are firstly introduced briefly. The current research status focusing on the problems of electroencephalograph (EEG) signal pattern, control signal transfer algorithm and system application is summarized and analyzed in detail. The further research direction and problems are discussed. Finally, the future development of brain control is analyzed and prospects are given.

关键词： Brain control brain-computer interface (BCI) human-computer integration control electroencephalogram (EEG)

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Robust fault detection and diagnosis for multiple-model systems with uncertainties 9

Robust fault detection and diagnosis for multiple-model syst...

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9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical processes, SAFEprocess 2015

作者： Zhao, Shunyi Huang, Biao Luan, Xiaoli Yin, Yanyan Liu, Fei Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 2G6 Canada

In this paper, a robust fault detection and diagnosis (FDD) method is proposed for multiple-model systems with modeling uncertainties. A compensation step is introduced to modify the mixed states and their variances obtained through the interacting multiple model (IMM) approximation and to solve the uncertainty problem. The degree of compensation is governed by a modification parameter determined by the orthogonality principle, which means that the estimation error calculated in the sub-filter using the true system models should be orthogonal to the residual error vector. To avoid over compensation in the unmatched models, a minimization procedure is used to derive the overall modification parameter. When the modification parameter is equal to one, the proposed method reduces to the IMM algorithm. An experiment is conducted through the ball and tube system to demonstrate the effectiveness of the proposed method. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Fault detection

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Observer-based controller design for networked systems with multiple packet losses and mixed delay

Observer-based controller design for networked systems with ...

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第27届中国控制与决策会议

作者： Fengfeng Qian Huaicheng Yan Hongbo Shi Hao Zhang Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education School of Information Science and Engineering East China University of Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

The paper focuses on a stabilizing controller design problem for networked systems with quantization,mixed delays,and a series of packet losses due to the signal transmission through the unreliable communication *** of both sensor-to-controller and controller-to-actuator are taken into account for the networked systems,and the distributed time delay is also considered in the network *** conditions for designing the controller as well as the system parameters can be obtained by solving certain linear matrix ***,the effectiveness of the designed method is proved by a numerical example.

关键词： quantization,mixed delays,multiple packet losses

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Detection and Diagnosis of Multiple Faults with Uncertain Modeling Parameters

Detection and Diagnosis of Multiple Faults with Uncertain Mo...

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作者： Zhao, Shunyi Huang, Biao Liu, Fei Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G2G6 Canada Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi214122 China

In this brief, a fault-detection and diagnosis method is proposed for the stochastic hybrid system with the consideration of model parameter uncertainty. To negate the effect of model uncertainty, a compensation step is introduced, which uses a compensation parameter to adjust the degree of dependence of the filtering on the model or the measurements. To determine the compensator parameter governing the degree of dependence, an orthogonality principle between the estimation error and the residual is applied. Numerical simulations with a second-order tracking system and a pilot-scale experiment study on a quadruple water tank system are conducted to demonstrate the effectiveness of the proposed approach. © 2017 IEEE.

关键词： Water tanks

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Plant-Wide process Incipient Fault Detection via Double-Layer Subspace Weighted Moving Window Reconstruction ICA

Plant-Wide Process Incipient Fault Detection via Double-Laye...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Bing Song YiMeng Song Hongbo Shi QingChao Jiang Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

With the widespread use of distributed systems, multi-subspace whole-flow industrial monitoring methods are evolving. However, due to the lack of distinctive features, incipient faults in plant-wide processes are more difficult to detect. To improve the detection rate of incipient faults in plant-wide processes while maintaining the generality of the algorithm, a novel double-layer subspace weighted moving window reconstruction independent component analysis (DS-WRICA) method is proposed. In DS-WRICA, process variables are first divided into different subspaces based on process knowledge and data-driven partitioning methods. Secondly, a weighted moving window is used to increase the offset of incipient faults, and monitoring statistics are constructed by combining optimized reconstructed independent component analysis (RICA) and local outlier factor (LOF) in each subspace. Then, the monitoring statistics in each subspace are fused with information by Bayesian inference fusion method to obtain distributed monitoring results. Finally, the effectiveness and superiority of the DS-WRICA method are verified by industrial examples.

关键词： Sensitivity Mechatronics Statistical analysis Fault detection Random access memory Independent component analysis Robot sensing systems

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A data-approach for industrial utility systems optimization under uncertainty

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Energy 2019年 182卷 559-569页

作者： Zhao, Liang You, Fengqi Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell University IthacaNY14853 United States

Energy optimization of utility system helps to reduce the operating cost and save energy for the industrial plants. Widespread uncertainties such as device efficiency and process demand pose new challenges for this issue. A hybrid modeling framework is presented by introducing the operating data into mechanism model to adapt the changes of device efficiency and operating conditions. Mathematical models of boilers, steam turbines, and letdown valves are then developed in the framework. Based on the process historical data of a real-world plant, a Dirichlet process mixture model is used to capture the support information of uncertain parameters. Bridging data-driven robust optimization (DDRO) and utility system optimization under uncertainty, a robust mixed-integer nonlinear programming (MINLP) model is developed by utilizing the derived uncertainty set. The robust counterpart of the developed model can be reformulated as a tractable MINLP problem including conic quadratic constraints that could be solved efficiently. A real-world case study is carried out to demonstrate the effectiveness of the proposed approach in protecting against uncertainties and achieving a good trade-off between optimality and robustness of the operational decisions for industrial utility systems. © 2019 Elsevier Ltd

关键词： Boilers

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