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Data Driven control and Learning Systems (DDCLS)

作者： Tao Zhang Xin Wang Zhenlei Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China Center of Electrical & Electronic Technology Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538626191

The grey wolf optimization (GWO) algorithm, one of the recently proposed bio-inspired algorithms, simulates the leadership hierarchy and hunting mechanism of grey wolves in nature. The GWO has a good performance in some optimization tasks, but its search capacity decreases with the increasing search scope and dimension. This paper proposes an improved GWO (IGWO) algorithm, in which Levy flight strategy and a sine cosine operator with adaptive step are incorporated to significantly improve the performance of the algorithm. The Levy flight strategy is used to strengthen the efficiency of global search. The adaptive sine cosine operator is introduced to improve the local search ability. Experimental results based on twenty unconstrained benchmark problems show the superiority of the proposed IGWO. Furthermore, the IGWO is utilized in PID controller design. The comparison results show that the IGWO algorithm is better than, or at least comparable to, other well-established swarm intelligence algorithms.

关键词： optimization Power system stability Sociology Statistics Prediction algorithms Particle swarm optimization Genetic algorithms

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Improved Garson Algorithm based on Neural Network Model 29

Improved Garson Algorithm based on Neural Network Model

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第29届中国控制与决策会议

作者： Sun Maozhun Liu Ji Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology School of Information Science and Engineering East China University of Science and Technology

ISBN: (纸本)9781509046584

The evaluation of input factors of complex system is a hot and difficult point in the sensitivity analysis. In this paper, the Garson algorithm based on artificial intelligence is studied and the original Garson algorithm accuracy is not high. Therefore, an improved Garson algorithm is proposed and the input factors are introduced into the Garson algorithm. At the same time, the original local sensitivity analysis algorithm is improved as the global sensitivity analysis algorithm and it increases the accuracy and stability of the Garson algorithm. Through the typical benchmark test function simulation, the experimental results show that the improved Garson algorithm has higher accuracy and stability in the evaluation of sensitivity coefficient. Finally, the improved Garson algorithm is applied to evaluate the input factors of the plate-fin heat exchangers. It shows that the IGarson algorithm is more feasibility and effectiveness.

关键词： sensitivity analysis artificial intelligence neural network local sensitivity analysis global sensitivity analysis

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Initial-Parameter-Criterion based Gaussian Mixture Model Monitoring Method for Non-Gaussian Process

Initial-Parameter-Criterion based Gaussian Mixture Model Mon...

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第37届中国控制会议

作者： Ying Tian Wenli Du Shanghai Key Lab of Modern Optical System School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

Gaussian mixture model(GMM) monitoring method is commonly used for non-Gaussian process monitoring. In GMM monitoring method, the final number of Gaussian components and the statistical distribution parameters are estimated through the improved Figueiredo-Jain(F-J) algorithm in the modeling phase. However, the data dimensionality of modern industry is high, the number of initial Gaussian components is large for the accuracy of the algorithm. Under this condition, the relative small number of samplings makes the denominator in the weight estimation formula of the improved F-J algorithm become zero and the algorithm suspends. To make the algorithm work well in an acceptable modeling time, we deduce the Initial-ParameterCriterion among initial Gaussian components, data dimension, sampling number and propose the Initial-Parameter-Criterion based GMM(IPC-GMM) monitoring method. The proposed monitoring method is used for TE process monitoring, simulation results reveal the effectiveness and necessity of IPC-GMM monitoring method.

关键词： non-Gaussian process monitoring Gaussian mixture model Initial-Parameter-Criterion

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Agricultural Lignocellulose Biochar Material in Wastewater Treatment: A Critical Review and Sustainability Assessment

Environmental Functional Materials

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Environmental Functional Materials 2025年

作者： Aqib Zahoor Xiao Liu Yuxin Liu Shengyong Liu Wang Yi Shahdev Sajnani Lingyu Tai Nadeem Tahir Boré Abdoulaye Mahaveer Yuan Liu Zia ur Rahman Martina Damizia Benedetta de Caprariis International Joint Research Center for the Control and Prevention of Environmental Pollution on Tropical lslands of Hainan Province Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province School of Environmental Science and Engineering Hainan University Haikou 570228 P. R. China Collaborative Innovation Center of Biomass Energy Henan Agricultural University Zhengzhou 450002 School of Environmental Science and Engineering Tianjin University Tianjin 300350 P. R. China Faculty of Civil and Environmental Engineering Gdańsk University of Technology ul. Narutowicza 11/12 80-233 Gdańsk Poland Interdisciplinary Research Center for Refining and Advanced Chemicals King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Department of Chemical Engineering Materials and Environment Sapienza University of Rome Via Eudossiana 18 00184 Rome Italy

Globally, roughly 380 trillion liters of wastewater are generated annually, with a significant portion remaining untreated due to expensive treatment and lack of expertise, consequently, leading severe environmental and public health problems. This research aims to identify the most environmentally sustainable and eco-friendly approach to wastewater treatment. Results shows that, agricultural lignocellulose-based biomass (ALCB) biochar is highly effective for wastewater treatment due to its abundance, cost-effectiveness, and excellent adsorption properties. The ability of sustaining high carbon content makes it a valuable carbon sink beneficial for the carbon reduction. Annually, ALCB is widely available as a byproduct in developing countries like China 716 (million tons) Mt, India 605 Mt, Brazil 451 Mt, Pakistan 285 Mt as well as in developed regions U.S.A 682 Mt, and Europe 580 Mt. Pyrolysis, gasification, and torrefaction are mostly used methods for ALCB biochar production. Physicochemical properties including surface area, porosity, functional groups, pH, and elemental composition notably affected by production methods, feedstock types and reactor designs determine the adsorption performance and environmental safety of ALCB biochar. The adsorption mechanisms of the organic and inorganic pollutant on biochar are ion exchange, complexation, electrostatic attraction, pore-filings, and physical adsorption, etc. LCA assessment revealed that the utilization of ALCB biochar on wastewater treatment offers several economic, environmental, and educational benefits through the aspect of waste management, revenue generation, job creations, pollution control, trading and skill developments.

关键词： Agricultural lignocellulose-based biochar Pyrolysis Pollutant adsorption Sustainable water treatment Environmental and economic benefits

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DE based economic control chart design and application for a typical petrochemical process

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Frontiers of Engineering Management 2017年第3期4卷 348-356页

作者： Zhi LI Feng QIAN Wenli DU Weimin ZHONG Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

Petrochemical industry plays an important role in the development of the national economy. Purified terephthalic acid(PTA) is one of the most important intermediate raw materials in the petrochemical and chemical fiber industries. PTA production has two parts:p-xylene(PX) oxidation process and crude terephthalic acid(CTA) hydropurification process. The CTA hydropurification process is used to reduce impurities, such as 4-carboxybenzaldehyde, which is produced by a side reaction in the PX oxidation process and is harmful to the polyester industry. From the safety and economic viewpoints, monitoring this process is necessary. Four main faults of this process are analyzed in this study. The common process monitoring methods always use T^2 and SPE statistic as control limits. However, the traditional methods do not fully consider the economic viewpoint. In this study, a new economic control chart design method based on the differential evolution(DE) algorithm is developed. The DE algorithm transforms the economic control chart design problem to an optimization problem and is an excellent solution to such problem. Case studies of the main faults of the hydropurification process indicate that the proposed method can achieve minimum profit *** method is useful in economic control chart design and can provide guidance for the petrochemical industry.

关键词： petrochemical PTA economic control chart design process monitoring DE algorithm

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Variable population memetic search: A case study on the critical node problem

arXiv

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arXiv 2019年

作者： Zhou, Yangming Hao, Jin-Kao Fu, Zhang-Hua Wang, Zhe Lai, Xiangjing School of Information Science and Engineering East China University of Science and Technology 130 Meilong Road Shanghai200237 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Department of Computer Science LERIA Université d'Angers 2 Boulevard Lavoisier Angers49045 France Institut Universitaire de France 1 rue Descartes Paris75231 France Robotics Laboratory for Logistics Service Institute of Robotics and Intelligent Manufacturing Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China

Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems. Often, a population of fixed size was used in such algorithms to record some best solutions sampled during the search. However, given the particular features of the problem instance under consideration, a population of variable size would be more suitable to ensure the best search performance possible. In this work, we propose variable population memetic search (VPMS), where a strategic population sizing mechanism is used to dynamically adjust the population size during the memetic search process. Our VPMS approach starts its search from a small population of only two solutions to focus on exploitation, and then adapts the population size according to the search status to continuously influence the balancing between exploitation and exploration. We illustrate an application of the VPMS approach to solve the challenging critical node problem (CNP). We show that the VPMS algorithm integrating a variable population, an effective local optimization procedure (called diversified late acceptance search) and a backbone-based crossover operator performs very well compared to state-of-the-art CNP algorithms. The algorithm is able to discover new upper bounds for 13 instances out of the 42 popular benchmark instances, while matching 23 previous best-known upper bounds. Copyright © 2019, The Authors. All rights reserved.

关键词： Population statistics

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Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

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Chinese Journal of chemical Engineering 2016年第7期24卷 856-860页

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

Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively,this paper proposes a novel process monitoring scheme based on orthogonal nonnegative matrix factorization(ONMF) and hidden Markov model(HMM). The new clustering technique ONMF is employed to separate data from different process modes. The multiple HMMs for various operating modes lead to higher modeling *** proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can be well interpreted through the hidden Markov estimation. The HMM-based monitoring indication named negative log likelihood probability is utilized for fault detection. In order to assess the proposed monitoring strategy, a numerical example and the Tennessee Eastman process are used. The results demonstrate that this method provides efficient fault detection performance.

关键词： Multimode processFault detectionHidden Markov modelOrthogonal nonnegative matrix factorization

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FRDPC subspace construction integrated with Bayesian inference for efficient monitoring of dynamic chemical processes 36

FRDPC subspace construction integrated with Bayesian inferen...

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第36届中国控制会议

作者： Yang Wang Qingchao Jiang Jingqi Fu School of Mechatronic Engineering and Automation Shanghai University School of Electric Engineering Shanghai Dianji University Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

ISBN: (纸本)9781538629185

Modern chemical processes are usually characterized by large-scale,complex correlation,and strong dynamics,and monitoring of such processes is *** paper proposes a performance-driven fault-relevant dynamic principal component(FRDPC) subspace construction integrated with Bayesian inference method to achieve efficient monitoring for dynamic chemical ***,dynamic principal component analysis is employed to deal with both auto-correlation and cross-correlation among ***,considering fault information has no definite mapping to a certain dynamic principal component(DPC) and the existence of non-beneficial DPCs may cause redundancy in the monitoring,an FRDPC subspace is constructed for each fault through the performance-driven DPC *** new process measurements are examined in each FRDPC subspace as well as the residual *** monitoring results in all subspaces are fused to a comprehensive index through Bayesian inference to provide an intuitive indication of the process *** studies on a numerical example and the Tennessee Eastman benchmark process indicate the efficiency.

关键词： fault detection dynamic principal component analysis Bayesian inference dynamic chemical processes

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Cooperative game based cost analysis of multi-plant heat exchanger network integration in chemical Industry Park

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chemical Engineering Transactions 2017年 61卷 1219-1224页

作者： Jin, Yuhui Li, Shaojun Jiang, Da Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai200237 China

Industrial energy saving is essential to the plant in the chemical industry park. Heat exchanger network of multi-plant can recover more potential energy. To ensure the network construction successfully, co-construct cost must be allocated reasonably. Cooperative game-theory based approach combines "Core" and "Riskbased Shapley value" with the network structure is put forward to give a contribute-equal cost imputation. A case study which includes three chemical plants is used to describe the approach and shows its practice value. The case study used Risk-based Shapley value illustrated plant which is confronted with high risk-loss will participate in the co-construction with lower cost, which means high risk and high yielded side by side. Copyright © 2017, AIDIC Servizi S.r.l.

关键词： Energy conservation

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Adaptive Neural Output Feedback control for Nonstrict-Feedback Nonlinear Systems with Quantized Input 36

Adaptive Neural Output Feedback Control for Nonstrict-Feedba...

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第36届中国控制会议

作者： Yan Dong Zhaoxu Yu Fangfei Li Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology Department of Mathematics East China University of Science and Technology

ISBN: (纸本)9781538629185

This paper focuses on the problem of adaptive output feedback tracking control for a class of nonstrict-feedback nonlinear systems with unknown control coefficients and quantized input. The difficulty from the unknown control direction is solved by using the linear state transformation and the Nussbaum gain function（NGF） approach. Based on the combination of input-driven observer, backstepping technique, neural network（NN） parametrization and variable separation method, a novel adaptive output feedback quantized control scheme involving only one adaptive parameter is developed for such systems. The designed quantized controller ensures that all signals of closed-loop systems are semi-globally uniformly ultimately bounded（SGUUB）, and the tracking error converges to an adjustable neighborhood of the origin. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed control design.

关键词： Nonstrict-feedback systems quantized input signal output feedback adaptive control unknown control direction

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