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DEVELOPMENT ISSUES ON THE LIFE-CYCLE MANAGEMENT OF MINERAL GRINDING processES AND EQUIPMENT

IFAC Proceedings Volumes 2005年第1期38卷 91-96页

作者： A. Remes V. Suontaka K. Saloheimo S-L. Jämsä-Jounela Helsinki University of Technology Department of Chemical Technology Laboratory of Process Control and Automation P.O.Box 6100 FIN-02015 HUT Finland Outokumpu Technology P.O.Box 84 FIN-02201 Espoo Finland

The requirements set on the performance of industrial equipment during their life-time are increasing due to the growing demand to minimize expenses and maximize production as a result of tightening global competition. The demand to optimize the equipment performance is making the ability to utilize different services during the equipment more important. Equipment performance optimization includes integration of the process and maintenance information. In this paper the life-cycle management concepts are reviewed and a general framework and a case study in the mineral processing field is presented and discussed.

关键词： Equipment life-cycle Condition based maintenance Standardization Embedded automation Mineral processing Particle size analyzer

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Nonlinear process identification and predictive control by multi-parameter models 6

Nonlinear process identification and predictive control by m...

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6th IFAC Symposium on Nonlinear control Systems, NOLCOS 2004

作者： Schmttz, U. Haber, R. Bars, R. Department of Plant and Chemical Process Engineering Laboratory of Process Control University of Applied Science Cologne Betzdorfer Str. 2 KölnD-50679 Germany Department of Automation and Applied Informatics Budapest University of Technology and Economics Goldmann Gy. ter 3 BudapestH-1111 Hungary

According to the multi-model approach a nonlinear dynamical process is approximated in different working points by local valid linear models. The global valid model output is calculated as the weighted sum of the sub-model outputs. The parameters of the Gaussian weighting function can be chosen by optimization. The computation time can be reduced if instead of the model outputs the parameters (for example static gain and time constant) of the local valid models are merged. The global valid nonlinear model can be used e.g., for model based predictive control. The new, multi-parameter method is illustrated by a heat exchanger example. Copyright © 2004 IEAC.

关键词： Heat exchangers

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Performance assessment of model predictive control in industrial distillation column

Performance assessment of model predictive control in indust...

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WCICA 2004 - Fifth World Congress on Intelligent control and automation, Conference Proceedings

作者： Yang, Maying Jin, Xiaoming Chen, Fei Automation Department Information Engneering College Zhejiang University of Technology Hangzhou 310032 China Natl. Key Lab. Indust. Contr. T. Inst. of Advanced Process Control Zhejiang University Hangzhou 310027 China

A synthetical method of multivariable control system performance assessment is proposed in, this paper, which uses multivariable Minimum Variance control (MVC) benchmark to determine the stochastic performance, and Normalized Multivariate Impulse Response (NMIR) curve as an alternative measure of performance to test the dynamic performance, and with the help of Auto-Correlation Function (ACF) and Cross-Correlation Function (CCF) to analyse if there are oscillations exist. The method is applied to assess performance of multivariable predictive control system of industrial distillation column.

关键词： Distillation columns

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Temperature prediction control based on least squares support vector machines

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control Theory and technology 2004年第4期2卷 365-370页

作者： Bin LIU, Hongye SU, Weihua HUANG, Jian CHU (National Laboratory of Industrial control technology, Institute of Advanced process control, Zhejiang University, Yuquan Campus,Hangzhou Zhejiang 310027,China department of automation, College of Information Science and Engineering, Wuhan University of Saence and technology, Wuhan Hubei 430081, China) National Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Yuquan Campus Hangzhou Zhejiang China Department of Automation College of Information Science and Engineering Wuhan University of Science and Technology Wuhan Hubei China

A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled *** obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time *** results of the experiment verify the effectiveness and merit of the algorithm.

关键词： Predictive control Least squares support vector machines RBF kernel function Generalized prediction control

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Predicting the product yield profile and cracking degrees in an industrial ethylene pyrolysis furnace

Predicting the product yield profile and cracking degrees in...

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International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Qiang Zhou Jie Zhang Yongmao Xu Institute of Process Control Department of Automation Tsinghua University Beijing Beijing China Centre for process Analytics and Control Technology School of Chemical Engineering & Advanced Materials University of Newcastle Newcastle-upon-Tyne UK

ISBN: (纸本)0780386531

This paper presents two soft-sensing models for predicting the product yields profile and the cracking degree of an ethylene pyrolysis furnace. The model based on single neural network with only one hidden layer trained by Levenberg-Marquardt algorithm with regularisation was first developed. It was found that the single neural network lack generalisation capability in that they can give undesirable performance when applied to unseen data. To improve the generalisation capability of the soft-sensing model, multi-model soft-sensors based on bootstrap aggregated neural networks with sequential training are used. In the sequential training of bootstrap aggregated networks, the first network is trained to minimise its prediction error whereas the rest of the networks are trained not only to minimise their prediction errors but also minimise the correlation among the trained networks. The overall output is obtained by combining all the individual networks. Application results show that the multi-model soft-sensors possess good generalisation capability in that they give good performance when applied to unseen data.

关键词： Furnaces Neural networks process control Predictive models Petrochemicals Chemical industry automation Postal services Chemical analysis Automatic control

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Performance assessment of model predictive control in industrial distillation column

Performance assessment of model predictive control in indust...

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World Congress on Intelligent control and automation (WCICA)

作者： Maying Yang Xiaoming Jin Fei Chen Automation Department Information Engineering College Zhejiang University of Technology Hangzhou China National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control University of Zhejiang Hangzhou China

ISBN: (纸本)0780382730

A synthetical method of multivariable control system performance assessment is proposed in this paper, which uses multivariable minimum variance control (MVC) benchmark to determine the stochastic performance, and normalized multivariate impulse response (NMIR) curve as an alternative measure of performance to test the dynamic performance, and with the help of auto-correlation function (ACF) and cross-correlation function (CCF) to analyse if there are oscillations exist. The method is applied to assess the performance of multivariable predictive control system of industrial distillation column.

关键词： Predictive models Predictive control Industrial control Distillation equipment Electrical equipment industry control systems System performance Benchmark testing Stochastic systems Impulse testing

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Operation Cycle Optimisation of the Larox Pressure Filter

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IFAC Proceedings Volumes 2004年第5期37卷 55-60页

作者： S-L. Jämsä-Jounela M. Vermasvuori J. Kämpe K. Koskela Department of Chemical Technology Laboratory of Process Control and Automation Kemistintie I. FIN-02150 Espoo Finland Larox Corporation P.O. Box 29 53101 Lappeenranta FINLAND

Artificial intelligence methods such as expert systems, fuzzy systems, neural networks and combinations of these, have become invaluable tools in helping operators to monitor and control processes. These methods can also be used to run processes in a more economically effective way and, in the case of equipment malfunction, they can propose appropriate corrective measures. In this paper a system for operation cycle optimisation of the Larox pressure filter is presented and some test results are discussed.

关键词： Expert systems Optimisation problem Fault detection Computer applications Models

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Particle Size Analyzer – A Case Project of an Extended Product in Mineral processing

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IFAC Proceedings Volumes 2004年第15期37卷 413-418页

作者： A. Remes M. Kongas K. Saloheimo S.-L. Jämsä-Jounela Helsinki University of Technology Department of Chemical Technology Laboratory of Process Control and Automation P.O.Box 6100 FIN-02015 HUT Finland Outokumpu Technology P.O.Box 84 FIN-02201 Espooo Finland

process, measurement and on-line analysis equipment are becoming technically more detailed and sophisticated. In many of the solutions information and computer technology are highly integrated in the product’s operating and maintenance procedures. This means new possibilities for the suppliers to provide services and to utilize the concept of an extended product. An embedded automation solution case is presented, and some service concepts for a particle size analyzer are considered.

关键词： Extended product Embedded systems Remote services Particle size measurement

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Nonlinear process identification and predictive control by multiparameter models

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IFAC Proceedings Volumes 2004年第13期37卷 1319-1324页

作者： U. Schmitz R. Haber R. Bars Department of Plant and Chemical Process Engineering Laboratory of Process Control University of Applied Science Cologne D-50679 Koln Betzdorfer Str. 2. Germany fax: +49-22J-8275-2836 Department of Automation and Applied Injormatics Budapest University of Technology and Economics H-1111 Budapest Goldmann Gy. tér 3. Hungary fax: +36-1-463-2871

关键词： process identification nonlinear models multi-model predictive control heat exchanger

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Long-range optimal model and multi-step-ahead prediction identification for predictive control 13

Long-range optimal model and multi-step-ahead prediction ide...

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13th IFAC Symposium on System Identification, SYSID 2003

作者： Haber, R. Schmitz, U. Bars, R. Department of Plant and Process Engineering Laboratory of Process Control University of Applied Science Cologne Betzdorfer Str. 2 KölnD-50679 Germany Department of Automation and Applied Informatics Control Research Group Hungarian Academy of Sciences Budapest University of Technology and Economics Hungary

Long-range optimal prediction algorithms use the predicted output for several steps ahead. The prediction based on traditionally estimated model parameters does not result in an optimal prediction if the measurements are noisy or/and model structure differs from real process structure. In this paper two different identification schemes are presented and compared: long-range predictive single-model identification and simultaneous multi-step-ahead prediction identification. It is shown that the first method is easier to realize but the second one leads to more accurate results. Both methods are derived for a first-order model in details. Simulation runs and a level control example illustrate the algorithms presented. © 2003 International Federation of Automatic control.

关键词： Parameter estimation

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