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Dynamic CCA-Based Distributed Monitoring for Multiunit Non-Gaussian processes

IFAC-PapersOnLine 2018年第21期51卷 347-352页

作者： Jiang, Qingchao Yan, Xuefeng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

Multiunit non-Gaussian dynamic processes are more popular, and monitoring of such processes becomes important. A dynamic canonical correlation analysis (DCCA)-based distributed monitoring approach for multiunit non-Gaussian dynamic processes is proposed. First, DCCA is performed between each local unit and the coupled units, which models both the auto-correlation and cross-correlation among units. Second, two fault detection residuals are generated for each local unit, through which both the process status and the type of a detected fault are identified. The proposed DCCA monitoring scheme achieves local unit monitoring by considering the variables from both the local unit and the coupled units, and therefore exhibits superiority. Applications on the Tennessee Eastman benchmark process are provided, through which the efficiency of the DCCA monitoring method is demonstrated. © 2018

关键词： Monitoring Benchmarking Correlation methods Fault detection Gaussian distribution Gaussian noise (electronic) process control process monitoring Canonical correlation analysis Cross correlations Distributed monitoring Distributed process Dynamic process monitoring Monitoring methods Non Gaussian process Tennessee Eastman

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Corrections to “Practical Fixed-Time Consensus Tracking for Multiple Euler–Lagrange Systems With Stochastic Packet Losses and Input/Output Constraints”

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IEEE Systems Journal 2022年第1期16卷 1709-1709页

作者： He Li Cheng-Lin Liu Ya Zhang Yang-Yang Chen Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Institute of Automation Jiangnan University Wuxi China School of Automation Southeast University Nanjing China

In [1], several errors were introduced during the production process, which are corrected here.

关键词： Packet loss

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A Generalized Probabilistic Monitoring Model with Both Randomand Sequential Data

arXiv

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arXiv 2022年

作者： Yu, Wanke Wu, Min Huang, Biao Lu, Chengda The School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China The Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 2G6 Canada

Many multivariate statistical analysis methods and their corresponding probabilistic counterparts have been adopted to develop process monitoring models in recent decades. However, the insightful connections between them have rarely been studied. In this study, a generalized probabilistic monitoring model (GPMM) is developed with both random and sequential data. Since GPMM can be reduced to various probabilistic linear models under specific restrictions, it is adopted to analyze the connections between different monitoring methods. Using expectation maximization (EM) algorithm, the parameters of GPMM are estimated for both random and sequential cases. Based on the obtained model parameters, statistics are designed for monitoring different aspects of the process system. Besides, the distributions of these statistics are rigorously derived and proved, so that the control limits can be calculated accordingly. After that, contribution analysis methods are presented for identifying faulty variables once the process anomalies are detected. Finally, the equivalence between monitoring models based on classical multivariate methods and their corresponding probabilistic graphic models is further investigated. The conclusions of this study are verified using a numerical example and the Tennessee Eastman (TE) process. Experimental results illustrate that the proposed monitoring statistics are subject to their corresponding distributions, and they are equivalent to statistics in classical deterministic models under specific restrictions. Copyright © 2022, The Authors. All rights reserved.

关键词： process monitoring

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Asynchronous double consensus-based distributed optimization for sustainable industrial utility systems

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Energy 2025年 331卷

作者： Minxue Kong Xin Peng Zhi Li Feifei Shen Yurong Liu Weimin Zhong Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai 200237 China School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 China State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai 200237 China State Key Laboratory of Chemical Engineering and Low-Carbon Technology East China University of Science and Technology Shanghai 200237 China

Industrial utility systems are essential to large-scale chemical plants, providing both electrical and thermal energy to support industrial processes. However, traditional centralized optimization methods, which rely on a central node for decision-making and synchronization, suffer from inherent limitations such as computational load concentration, poor robustness, and inadequate adaptability to dynamic environments. These issues significantly constrain system efficiency and scalability. Integrating renewable energy sources presents an additional challenge, primarily due to the conflict between the intermittent nature of renewable energy and the need for stability in the utility system’s energy supply. This study introduces a distributed optimization framework based on an asynchronous double-consensus algorithm to address these issues. The framework is designed to optimize utility system operations while enhancing computational efficiency and scalability. To further improve system resilience, a dynamic asynchronous communication strategy is proposed to handle device failures and communication constraints. The effectiveness of the proposed method is demonstrated through a case study of a real industrial energy system. The results show a significant reduction in computational complexity, with an overall system cost reduction of 6.6%. The proposed algorithm reduces execution time by approximately 91%–92% compared to the conventional centralized approach. Moreover, it guarantees convergence to the optimal solution within a finite number of iterations, attaining a convergence accuracy of 2 . 651 × 1 0 − 6 .

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Multi-objective dispatch of integrated renewable power systems leveraging robust optimization in deep reinforcement learning

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Computers & chemical Engineering 2025年 201卷

作者： Aoqun Ma Zhi Li Feifei Shen Xin Peng Yurong Liu Weimin Zhong Feng Qian State Key Laboratory of Chemical Engineering and Low-Carbon Technology East China University of Science and Technology Shanghai 200237 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai 200237 China School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 China State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai 200237 China

The time-varying nature of wind speed and solar radiation introduces significant intermittency and uncertainty to the grid integration of renewable energy. We propose a robust optimization method with a dynamic framework based on reinforcement learning to address this challenge. Our approach considers economic cost and carbon emissions as objective functions in a multi-objective robust optimization model. Day-ahead scheduling results and real-time renewable energy forecasting are used to dynamically adjust the uncertainty set in dispatch, employing support vector clustering and Deep Q-Network. The dynamic framework aims to achieve feasible and cost-effective dispatch by accounting for real-time prediction errors and penalty costs associated with energy spillage and load curtailment risks. A practical industrial system case study shows that the proposed algorithm significantly reduces average day-ahead costs by 21.43% compared to static solutions and exhibits better environmental performance compared to the counterparts.

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Nanocomposite Substrate-Supported Nanofiltration Membrane for the Efficient Treatment of Rare Earth Wastewater

SSRN

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SSRN 2022年

作者： Wang, Yiwen Jiang, Qinliang Li, Yi Fan, Hua Gui, Shuanglin Xiong, Jihai Luo, Jianquan Institute of Energy Conversion Jiangxi Academy of Sciences Nanchang330096 China Key Laboratory of Poyang Lake Environment and Resource Utilization Ministry of Education School of Resources Environmental & Chemical Engineering Nanchang University Nanchang330031 China Advanced Membranes & Porous Materials Center King Abdullah University of Science and Technology Division of Physical Sciences and Engineering Thuwal 23955-6900 Saudi Arabia State Key Laboratory of Biochemical Engineering Institute of Process Engineering University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100190 China

Rare earth wastewater is an emerging wastewater featuring high salinity, strong acidity and a complex background with multiple contaminants. A nanofiltration (NF) membrane with better performance is desired to tackle the challenge of rare earth wastewater to reduce its negative impact on the environment. Herein, a novel nanocomposite substrate-supported thin film composite (TFC) NF membrane with good stability and superior separation performance was reported. The inorganic two-dimensional O-MoS2 nanosheets were embedded into the PSf substrate via a nonsolvent-induced phase inversion method. The PSf/O-MoS2 nanocomposite was used as the support for the interfacial polymerization. the pore structure of the substrate shifted from a larger irregular bubble pore structure into a finger-like porous structure when the content of O-MoS2 was 0.06 wt%. As a result, the nanocomposite substrate-supported TFC membrane had a 1.7 times higher water flux and improved salt rejections compared to that of the control membrane. The obtained NF membrane showed excellent separation performance with a high salinity removal efficiency of 95% in treating real rare earth wastewater (diluted 50 times, pH: 2.46). This work also highlights the modification of the substrate by functional nanomaterials could provide versatile ways to improve the separation performance and stability of TFC membranes. © 2022, The Authors. All rights reserved.

关键词： Nanofiltration membranes

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Design,modeling and experiments of broadband tristable galloping piezoelectric energy harvester

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Acta Mechanica Sinica 2020年第3期36卷 592-605页

作者： Junlei Wang Linfeng Geng Shengxi Zhou Zhien Zhang Zhihui Lai Daniil Yurchenko Engineering Research Center of Energy Saving Technology and Equipment of Thermal Energy System Ministry of EducationZhengzhou 450000China School of Aeronautics Northwestern Polytechnical UniversityXi'an 710072China William G.Lowrie Department of Chemical and Biomolecular Engineering The Ohio State UniversityColumbusOH 43210USA Guangdong Provincial Key Laboratory of Micro/Nano Optomechatronics Engineering College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen 518060China Institute of Mechanical Process&Energy EngineeringHeriot-Watt UniversityEdinburgh EH 144ASUK

Galloping based piezoelectric energy harvester is a kind of micro-environmental energy harvesting device based on flowinduced vibrations.A novel tristable galloping-based piezoelectric energy harvester is constructed by introducing a nonlinear magnetic force on the traditional galloping-based piezoelectric energy *** on Euler-Bernoulli beam theory and Kirchhoff’s law,the corresponding aero-electromechanical model is proposed and validated by a series of wind tunnel *** parametric study is performed to analyse the response of the tristable galloping-based piezoelectric energy *** results show that comparing with the galloping-based piezoelectric energy harvester,the mechanism of the tristable galloping-based piezoelectric energy harvester is more *** the increase of a wind speed,the vibration of the bluff body passes through three branches:intra-well oscillations,chaotic oscillations,and inter-well *** threshold wind speed of the presented harvester for efficiently harvesting energy is 1.0 m/s,which is decreased by 33% compared with the galloping-based piezoelectric energy *** maximum output power of the presented harvester is 0.73 mW at 7.0 m/s wind speed,which is increased by 35.3%.Compared with the traditional galloping-based piezoelectric energy harvester,the presented tristable galloping-based piezoelectric energy harvester has a better energy harvesting performance from flow-induced vibrations.

关键词： Energy harvesting Galloping Tristable Flow induced vibrations

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Fuzzy Event-triggered Disturbance Rejection control of Nonlinear Systems

Fuzzy Event-triggered Disturbance Rejection Control of Nonli...

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第31届中国过程控制会议（CPCC 2020）

作者： Peng Cheng Yanyan Yin Yanqing Liu Feng Pan Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things Jiangnan University School of Electrical Engineering Computing and Mathematical SciencesCurtin University

In this paper, we describe a novel method for constructing fuzzy based event-triggered disturbance rejection control for nonlinear systems. A new fuzzy event based anti-disturbance controller is designed and a reduced order disturbance observer is constructed. Sufficient conditions for the closed loop system to be asymptotically stable under an H∞ performance index are derived. Based on these conditions, the design of a fuzzy event-triggered state feedback controller is formulated and *** results are presented to demonstrate the correctness and effectiveness of our theoretical findings.

关键词： Fuzzy system Nonlinear systems Event trigger Disturbance rejection Asymptotically stable

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Bipartite Consensus Problem of Harmonic Oscillators Based on Sampled-data

Bipartite Consensus Problem of Harmonic Oscillators Based on...

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第32届中国控制与决策会议

作者： Hang Tian Cheng-Lin Liu Guang-Ye Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University Shanghai Keliang Information Tech.& Eng.Co. Ltd

This paper is concerned with the bipartite consensus problem of sampled-data coupled harmonic oscillators by predictor factor under the fixed *** with the existing distributed protocol for synchronization in networks of coupled harmonic oscillators based on the sampled-data measurement,we propose a distributed bipartite consensus control protocol in which positive and negative interaction weights *** structurally balanced networks,we improve the convergence rate of the network via predictor *** and sufficient conditions are obtained for the well designed protocol to achieve bipartite consensus by using frequency-domain analysis and matrix ***,examples are shown to validate the convergence conditions.

关键词： Bipartite consensus Sampled-data coupled harmonic oscillators Predictor factor

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Smart process Manufacturing Systems: Deep Integration of Arti cialIntelligence and process Manufacturing

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Engineering 2019年第6期5卷 981-981页

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

In recent years, smart process manufacturing has attracted increasing attention from both academia and industry around the world. Together with academia and industry, various governments have released their strategic plans for smart manufacturing, which has the key aim of realizing the upgrading and transformation of the process industry by means of knowledge-integrated planning, scheduling, and process optimization based on industrial internet of things. However, in order to achieve intelligent, autonomous,and regulated control in process manufacturing systems, the industry must confront many problems—including multi-scale coupling, human–cyber–physical interaction, and multi-objective optimization with strong constraints—from the varying perspectives of intelligent perception, autonomous control, and smart decision-making.

关键词： autonomous smart Together

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