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作者： Zhao, Yu Huang, Biao Su, Hongye Chu, Jian State Key Lab. of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou China University of Alberta Department of Chemical and Material Engineering Edmonton AB T6G-2G6 Canada

This paper is concerned with identification of linear parameter varying (LPV) systems in an input-output setting with Box-Jenkins (BJ) model structure. Classical linear time invariant prediction error method (PEM) is extended to the LPV PEM. Under the new LPV framework, identification of two types of input-output LPV models is considered: one is based on parameter interpolation and the other is based on model interpolation. The effectiveness of the proposed solution is validated by comparison with other existing LPV identification approaches through simulation examples and demonstrated by experiment studies. © 2011 Elsevier Ltd. All Rights Reserved.

关键词： Interpolation

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Market-driven profit optimization for tabular chemical reactors with genetic algorithms

Market-driven profit optimization for tabular chemical react...

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2012 IEEE Symposium on Robotics and Applications, ISRA 2012

作者： Qin, Hai-Peng Chen, Peng Lu, Yong-Zai Wu, Zhao-Li Chu, Jian State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China Department of Automation Shanghai Jiaotong University Shanghai 200023 China Department of Chemical and Biological Engineering Zhejiang University Hangzhou 310027 China

ISBN: (纸本)9781467322072

With the uncertainty in the prices of feedstock, energy and finished products, the profit optimization plays a critical role in making a chemical production enterprise more dynamic and flexible to adapt the changes in global marketplace. This paper presents the development of profit optimization solution with an integration of profit model and model-based genetic algorithm (GA) optimization, and its industrial case study on an ethanolamine (EA) production line. The proposed methodology and solution may also be applied to other chemical manufacturing enterprises. © 2012 IEEE.

关键词： Ethanolamines

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Plant friendly input design for system identification in closed loop

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IFAC Proceedings Volumes 2012年第16期45卷 1335-1340页

作者： Sridharakumar Narasimhan Xavier Bombois Dept. of Chemical Engineering IIT Madras Chennai India Delft Center for Systems and Control Delft University of Technology The Netherlands

Optimal experiment or input design is the scientific exercise of designing informative excitation signals for the identification of a real-life dynamic system. In the least costly input design framework, the input is designed such that the identification cost is minimized while meeting desired specifications on the quality of the identified model. Identification of real-life processes require that the identification be “plant-friendly”. These are typically imposed as constraints on experiment time, input and output amplitudes or input move sizes. This work focusses on an LMI based plant friendly input design in the least costly framework.

关键词： input design optimal experiment design identification for control convex optimization

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Human behavior model and its application on performance management in a chemical plant workshop

Human behavior model and its application on performance mana...

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2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012

作者： Han, Feng Liu, Xiwei Shang, Xiuqin Cheng, Changjian Li, Bo Fan, Dong Olefine Plant SINOPEC Qilu Petrochemical Co. Ltd. Zibo China School of Chemical Engineering Beijing University of Chemical Technology Beijing China State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Songshan Lake Dongguan China

ISBN: (纸本)9781467324007

To investigate the influences of performance management rules on human behavior in a chemical plant workshop, we construct an artificial workshop. It mainly contains human behavior model, workshop environment model including the definition of work position performance management rules and production events, and their interaction rules. The human behavior model is built based on DISC (Dominance, Influence, Steadiness, Compliance) behavior model and the Agent technology. A platform for workshop human behavior computing is finally created for computational experiments on performance management and decision-making. The platform can be used as a support for refined management in the chemical plant. © 2012 IEEE.

关键词： chemical plants

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Fault tolerant adaptive estimation of nonlinear processes using redundant measurements

Fault tolerant adaptive estimation of nonlinear processes us...

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2012 20th Mediterranean Conference on control and Automation, MED 2012

作者： Miranda, M. Edelmayer, A. Laboratory of Unit Operations Department of Chemical Engineering University of Los Andes 5101 Mérida Venezuela Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences Kende u. 13-17 H-1111 Budapest Hungary

ISBN: (纸本)9781467325318

This paper presents a dual layer approach for robust fault tolerant estimation of nonlinear processes using a combined adaptive extended Kalman filter and fault detection and filter reconfiguration. From the one hand, the filter is made robust in face of environment uncertainty using adaptive filtering. To this end, the filter identifies the measurement covariance by means of recursive estimation, upon which the adaptation relies, to suppress the effect of sporadic variations in the quality of measurements as well as compensates for incipient sensor faults. From the other hand, fault monitoring is continuously applied to the filter's innovation in an attempt to initiate filter reconfiguration when the adaptation mechanism alone is not able to overcome the failure situation. The discussion of the results is embedded in the application framework of state estimation of a batch distillation process. © 2012 IEEE.

关键词： Fault detection

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Optimal experimental design for mechanistic nonlinear dynamic models using multisine inputs: application to a Diesel engine.

Optimal experimental design for mechanistic nonlinear dynami...

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American control Conference

作者： I. Stamati D. Telen F. Logist E. Van Derlinden M. Hirsch T. Passenbrunner J. Van Impe Katholieke Universiteit Leuven - Department of Chemical Engineering BioTeC - Chemical and Biochemical Process Technology and Control Leuven Belgium Institute for Design and Control of Mechatronical Systems Johannes Kepler University Linz Austria

ISBN: (纸本)9781457710957

For many applications first-principles nonlinear dynamic models are preferred by practitioners. Parameter estimation for these models is often a non-trivial and time consuming task. The use of optimally designed dynamic inputs can reduce the experimental burden and increase the accuracy of the estimated parameters. Traditionally, piecewise polynomial input sequences are exploited for this purpose. In contrast, this paper proposes optimal experiment design with the use of random phase multisine inputs, which are typically used for black box model identification. The main motivations are (i) the practical requirement that the inputs have to be concentrated around an operating point, and (ii) the fact that fast dynamics have to be included in the input profile without introducing a large number of discretization parameters. Moreover, multisines can be designed to excite exclusively a specific frequency band of interest. As an illustration, optimal inputs are designed and validated experimentally for estimating the parameters important for the dynamical behaviour of a Diesel engine air path model.

关键词： Mathematical model Optimization Atmospheric modeling Parameter estimation White noise Diesel engines Sensitivity

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Adaptive control of State Constrained Nonlinear systems in Strict Feedback Form

Adaptive Control of State Constrained Nonlinear Systems in S...

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American control Conference

作者： Martin Guay Mathias Burger Department of Chemical Engineering Queens' University Kingston ON Canada Institute for Systems Theory and Automatic Control University of Stuttgart 70550 Stuttgart Germany

ISBN: (纸本)9781457710957

In this paper, we propose a systematic approach for the constructive design of adaptive control systems for nonlinear uncertain systems subject to state constraints. The analysis is restricted to a class of nonlinear systems in strict feedback form with respect to the state constraints. A novel adaptive estimation routine is employed to estimate both the unknown parameters and an approximate uncertainty sets, which is decreasing over time. The approximate uncertainty set is utilized to define a sequence of robustly controlled invariant sets which are strictly contained in the feasible set.

关键词： Uncertainty Adaptive control Nonlinear systems Robustness control systems Lyapunov methods Heuristic algorithms

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Explicit-Model Predictive control: A simulation based scalability study

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IFAC Proceedings Volumes 2012年第15期45卷 204-209页

作者： Arun Gupta Sharad Bhartiya P.S.V. Nataraj ABB-GISL Research Bangalore India 560048 (Tel: +91-***) Department of Chemical Engineering IIT Bombay Mumbai India (Tel: +91-22-25767225) Systems and Control Engineering IIT Bombay Mumbai India (Tel: +91-22-25767887)

The main impediment in the applicability of explicit model predictive control (e-MPC) is that the number of regions in the parametric space increases dramatically with an increase in the dimension of the parameter and decision spaces as well as with an increase in the number of constraints. This work explores the scalability issues by simulation in which e-MPC is used for control of quadruple tank system. Explicit-MPC problem formulation from a standard MPC (for finite and infinite prediction horizon) using multi parametric quadratic programming (mp-QP) is presented. The results confirm that while the number of critical regions increases exponentially with the control horizon, the online calculations are computationally inexpensive.

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Fault tolerant adaptive estimation of nonlinear processes using redundant measurements

Fault tolerant adaptive estimation of nonlinear processes us...

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Mediterranean Conference on control and Automation (MED)

作者： M. Miranda A. Edelmayer Laboratory of Unit Operations Department of Chemical Engineering Faculty of Engineering University of Los Andes Merida Venezuela Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

关键词： Temperature measurement Mathematical model Estimation Technological innovation Noise Fault tolerance Fault tolerant systems

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Bottleneck Prediction Method Based on Improved Adaptive Network-based Fuzzy Inference System (ANFIS) in Semiconductor Manufacturing System

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Chinese Journal of chemical engineering 2012年第6期20卷 1081-1088页

作者：曹政才邓积杰刘民王永吉 College of Information Science and Technology Beijing University of Chemical Technology Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast University Tsinghua National Laboratory for Information Science and Technology State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.

关键词： semiconductor manufacturing system bottleneck prediction adaptive network-based fuzzy inference system

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