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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： Qingchao Jiang Furong Gao Biao Huang Xuefeng Yan Department of Chemical and Biomolecular Engineering The Hong Kong University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Modern chemical processes are usually characterized by large scale,multiple operation units,and high dynamic,and monitoring of these processes is *** paper proposes a genetic algorithm(GA)-regularized dynamic canonical correlation analysis(DCCA)-based concurrent modeling and monitoring scheme for multi-unit dynamic ***,the DCCA modeling is performed between each unit and the entire process to characterize both cross-correlation and auto-correlation among ***,the GA regularization is performed for each unit to explore communicating variables and reduce the communication *** a residual-based local fault detector is established for each unit,which performs fault detection by considering both the variables of the local unit and the information provided by its related units,and therefore exhibits superior monitoring *** proposed monitoring scheme is applied on a numerical example and an industrial tail gas treatment *** results demonstrate the feasibility and efficiency of the proposed approach.

关键词： Fault detection Multi-unit dynamic processes Canonical correlation analysis Process monitoring

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Reaction mechanism of methyl nitrite dissociation during co catalytic coupling to dimethyl oxalate:A density functional theory study

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Chinese Journal of chemical Engineering 2016年第1期24卷 132-139页

作者： Chen Fan Man Luo Wende Xiao Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology (ECUST) School of Chemistry and Chemical Engineering Shanghai Jiao Tong University (SJTU)

Dissociation of methyl nitrite is the first step during CO catalytic coupling to dimethyl oxalate followed by hydrogenation to ethyl glycol in a typical coal to liquid process. In this work, the first-principle calculations based on density functional theory were performed to explore the reaction mechanism for the non-catalytic dissociation of methyl nitrite in the gas phase and the catalytic dissociation of methyl nitrite on Pd(111) surface since palladium supported on alpha-alumina is the most effective catalyst for the coupling. For the non-catalytic case, the calculated results show that the CH_3O–NO bond will break with a bond energy of 1.91 eV, and the produced CH_3O radicals easily decompose to formaldehyde, while the further dissociation of formaldehyde in the gas phase is difficult due to the strong C–H bond. On the other hand, the catalytic dissociation of methyl nitrite on Pd(111) to the adsorbed CH_3O and NO takes place with a small energy barrier of 0.03 eV. The calculated activation energies along the proposed reaction pathways indicate that(i) at low coverage, a successive dehydrogenation of the adsorbed CH_3O to CO and H is favored while(ii) at high coverage, hydrogenation of CH_3O to methanol and carbonylation of CH_3O to methyl formate are more preferred. On the basis of the proposed reaction mechanism,two meaningful ways are proposed to suppress the dissociation of methyl nitrate during the CO catalytic coupling to dimethyl oxalate.

关键词： Methyl nitrite Catalytic Non-catalytic Dissociation Density functional theory

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Research on online multiple model soft-sensor

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chemical Engineering Transactions 2017年 61卷 1795-1800页

作者： Wang, Shijie Wang, Zhenlei Wang, Xin Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai200237 China Center of Electrical and Electronic Technology Shanghai Jiao Tong University Shanghai200240 China

Offline updating is a method that most of multiple model soft-sensors used to adapt the new operating conditions. Replacing online models with offline ones is bound to affect the efficiency of soft-sensors, and it costs manpower as well as time simultaneously. It takes maintenance staffs some time to re-train complete models, which requires a lot of historical data, and then the existing models will be changed with new ones. A soft-sensor that can be added or subtracted models online is proposed in this paper. Density-based spatial clustering of applications with noise (DBSCAN) is employed for clustering analysis. Compared with traditional kernel fuzzy clustering method (KFCM), DBSCAN improves the ability of filtering out noise and enhance the ability to decide whether there is a new working condition. However, the clustering results of DBSCAN are extremely sensitive to the input parameters. In this study, kernel density estimation (KDE) is applied to determine the number of subsets and a novel method is proposed to determine the parameters. The new sub-models can be directly added to the online models after trained. The results of soft-sensor achieved by a number of models according to the switching or weighted way. The method proposed in this paper is applied to the measurement of cracking depth of ethylene cracking furnace, which proves the practicability and effectiveness. Copyright © 2017, AIDIC Servizi S.r.l.

关键词： Ethylene

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A valve stiction and time delay control method based on fuzzy smith internal model principle

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chemical Engineering Transactions 2017年 61卷 577-582页

作者： Zhang, Hao Wang, Xin Wang, Zhenlei Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai200237 China Center of Electrical and Electronic Technology Shanghai Jiao Tong University Shanghai200240 China

In the process of chemical production, the phenomenon of control valve's stiction is very common. This phenomenon causes control loop oscillation and has a negative effect on the quality and cost of the product. Time delay exists widely in industrial process, which leads to the variation of the dynamic response of the system. In this paper, taking the stiction model proposed by Kano as an example, then puts forward a method of smith internal model control based on fuzzy theory. This paper try to solve the stiction problem by controller designed. This control method can realize the parameter self-tuning and overcome the stiction. Simulation results show that the method proposed can effectively overcome the loop oscillation and improve the performance of the system. Copyright © 2017, AIDIC Servizi S.r.l.

关键词： Stiction

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Data-driven online operating performance assessment for multi-datasets multivariable industrial processes

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chemical Engineering Transactions 2017年 61卷 1729-1734页

作者： Du, Yupeng Wang, Zhenlei Wang, Xin Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai200237 China Center of Electrical and Electronic Technology Shanghai Jiao Tong University Shanghai200240 China

In this study, a novel online operating performance assessment method based on multi-sets two-step basis vector extraction artificial neural networks (MTBVE-ANN) strategy is proposed for industrial applications. The MTBVE-ANN method focuses on finding common and specific information involved in multi-datasets, which improves the accuracy of data nonlinear characterization with artificial neural networks introduced. The optimality related variations are extracted from each operating performance grade by analysing the common and unique variations over online steady performance grades. The online operating performance assessment method is performed based on the similarities between the optimality related variations of the test data and that of historical training data. Previously, total projection to latent structures (T-PLS) operating performance assessment method must be performed based on the availability of both input and output data. The proposed method in this paper takes the artificial neural network to assess the operating performance grade of the online test data without output. The validity and precision of the proposed operating performance assessment method is illustrated with the industrial data of multi-datasets multivariable industrial processes. Copyright © 2017, AIDIC Servizi S.r.l.

关键词： Neural networks

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Nonlinear adaptive switching control for a class of non-affine nonlinear systems

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Chinese Journal of chemical Engineering 2016年第9期24卷 1243-1251页

作者： Miao Huang Xin Wang Zhenlei Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology School of Optical–Electrical and Computer Engineering University of Shanghai for Science and Technology Center of Electrical & Electronic Technology Shanghai Jiao Tong University

An improved nonlinear adaptive switching control method is presented to relax the assumption on the higher order nonlinear terms of a class of discrete-time non-affine nonlinear systems. The proposed control strategy is composed of a linear adaptive controller, a neural network(NN) based nonlinear adaptive controller and a switching mechanism. An incremental model is derived to represent the considered system and an improved robust adaptive law is chosen to update the parameters of the linear adaptive controller. A new performance criterion of the switching mechanism is designed to select the proper controller. Using this control scheme, all the signals in the system are proved to be bounded. Numerical examples verify the effectiveness of the proposed algorithm.

关键词： Non-affine nonlinear systems Nonlinear adaptive switching control Incremental model

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Real-time optimization and control of an industrial ethylbenzene dehydrogenation process

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chemical Engineering Transactions 2017年 61卷 331-336页

作者： Wang, Xiaoqiang Mahalec, Vladimir Li, Zhi Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai200237 China Department of Chemical Engineering McMaster University HamiltonONL8S 4L7 Canada

Styrene is one of the most important monomers in the petrochemical industry. In this study, a novel methodology for tight integration of Real-Time Optimization (RTO) and Model Predictive control (MPC) is applied to an industrial ethylbenzene dehydrogenation process for producing styrene. This optimization-control framework is able to improve the automation level of ethylbenzene dehydrogenation process under various unfavourable conditions, especially the plant-model mismatch and disturbances. In the RTO layer, a constraint-adaptation strategy is adopted, which makes it easier to update the steady-state model. In the MPC layer, an MPC with Nonlinear Successive Linearization (MPC-NSL) is employed to deal with the nonlinear dynamic characteristics of the process and to match the steady-state gain at the steady-state point. In addition, an economic Steady-State Target Optimization (SSTO) layer is inserted between RTO and MPC layers to update continuously the set-points for MPC. The two-layered integration framework is easy to maintain because the models adopted in the three layers originate from the same nonlinear dynamic model. The comprehensive dynamic model of the styrene process is developed based on two large core fixed-bed axial-radial flow reactors described by Partial Differential Algebraic Equations (PDAE). The model parameters are corrected, and accuracy is validated using actual industrial data before it is used for simulation. Case study shows that the proposed optimization-control framework significantly improves the operation level. Copyright © 2017, AIDIC Servizi S.r.l.

关键词： Model predictive control

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流程工业智能优化制造的基础理论与关键技术

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Engineering 2017年第2期 14-27页

作者：钱锋钟伟民杜文莉 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

本文围绕流程工业转型升级的重大需求,剖析了当前流程工业在经营决策层面、生产运行层面、能效安环层面和信息集成层面存在的主要问题,提出了利用现代信息技术从生产、管理以及营销全过程优化出发,推进以高效化、绿色化和智能化制造为... 详细信息

本文围绕流程工业转型升级的重大需求,剖析了当前流程工业在经营决策层面、生产运行层面、能效安环层面和信息集成层面存在的主要问题,提出了利用现代信息技术从生产、管理以及营销全过程优化出发,推进以高效化、绿色化和智能化制造为目标的流程工业智能优化制造的愿景目标。围绕制造过程从装备智能化到制造流程、操作模式、供应链管理等自适应智能优化,以需求驱动、应用导向为主,提出了当前亟须研究的核心工程科学问题,即:(1)生产与经营全过程信息智能感知与集成;(2)知识驱动的生产过程计划调度与管理决策;(3)人机物自然交互的生产过程全流程协同控制与优化;(4)全生命周期安全环境足迹监控与风险溯源分析及控制。并围绕核心科学问题进一步提出了拟突破的流程工业智能优化制造的基础理论与关键技术。

关键词：流程工业智能优化制造绿色制造高端制造最优性评价

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Multiple Model Predictive control with Undisturbed Switch for Wind Turbines System

Multiple Model Predictive Control with Undisturbed Switch fo...

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Asian control Conference

作者： Xianda Huang Tianhong Pan Zhengming Li Shihong Ding Tan Shuai School of Electrical & Information Engineering Jiangsu University Zhenjiang Jiangsu China Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai PR China

ISBN: (纸本)9781509015740;9781509015733

The variable-speed, variable-pitch speed wind turbines are widely used in the wind-energy system. However, the fluctuation of power generation is serious because of the erratic speed of wind and the nonlinearity of the energy conversion systems (ECS). How to keep the ECS work well during the whole operating region is a challenge issue. In this paper, a multiple model predictive control (MMPC) using the state estimation is present to solve this issue. The whole operating region is divided into several sub-regions according to the wind's speed, in which several linear models and corresponding MPCs are constructed. The state variables of each local model are estimated by using the Kalman filter. A new switching criterion without perturbation is present to keep the MMPC works well at the different operating region switching. Considering the operating constraints, a quadratic programming procedure is used to calculate the optimal value of MMPC. Comparing the conventional PI controller, the proposed control achieves a good performance and reduces the fluctuation of power generation. Simulation results show the excellent performance of the present method.

关键词： Wind speed Generators Wind turbines Torque Switches Predictive control Wind energy

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智能优化制造:流程工业转型发展的关键

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Engineering 2017年第2期 7-8页

作者：钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

近年来,智能制造成为世界各国竞争和合作的焦点,主要发达和发展中国家纷纷加快战略规划和布局。德国、美国、中国相继发布了"工业4.0"战略、"智能过程制造"以及《中国制造2025》战略,旨在将信息、计算与通信技术与... 详细信息

近年来,智能制造成为世界各国竞争和合作的焦点,主要发达和发展中国家纷纷加快战略规划和布局。德国、美国、中国相继发布了"工业4.0"战略、"智能过程制造"以及《中国制造2025》战略,旨在将信息、计算与通信技术与生产制造技术深度融合,实现产品、设备、人和组织之间的无缝集成与合作。流程工业主要包括石油、化工、钢铁、有色、建材等基础原材料工业,不仅是中国、也是美国等世界制造业大国的国民经济支柱和基础产业。经过数十年发展,

关键词：流程工业智能优化智能制造

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