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MODELLING, IDENTIFICATION AND SIMULATION OF A DOUBLE CLOSED-LOOP INDUSTRIAL WET GRINDING CIRCUIT

IFAC Proceedings Volumes 2012年第23期45卷 72-77页

作者： Moïse Mukepe Kahilu Michel Kinnaert Pierre Kalenga Ngoy Jean-Marie Moanda Ndeko Department of Automatic Control and Systems Analysis Université Libre de Bruxelles (ULB) 50 Av F.D. Roosevelt 1050 Brussels e-mail:{mmukepek michel.kinnaert}@ulb.ac.be Polytechnic Faculty Université de Lubumbashi (UNILU) DR Congo e-mail:kalenga.ngoy@unilu.ac.cd Polytechnic Faculty Université de Kinshasa (UNIKIN) DR Congo e-mail: jmariemoanda@unikin.cd

In this paper, a comprehensive approach for modelling, parameter estimation and global simulation of the grinding circuit of the Kolwezi Concentrator is presented. This process can be described by a nonlinear distributed parameter system. To differentiate between wet and dry grinding, the derived model takes into account the increase of the specific rate of breakage in the wet grinding process. In order to determine material transport dynamics within the mills, a new experimental method has been developed. Due to the lack of a colorant or a radioactive sensor, this procedure is based on the use of the G41 foaming as tracer. Both steady-state and dynamic simulation results are discussed and are shown to be in agreement with the experimental data.

关键词： Concentrators distributed parameter systems dynamic modelling multi-input/multi-output systems nonlinear models process parameter estimation process simulators

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A parameter Adjustment Method for Physics-based Control Models with Statistical Regression

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IFAC Proceedings Volumes 2012年第23期45卷 290-291页

作者： Akira Morita Satoru Ishihara Technical Development Bureau Nippon Steel Corporation Futtsu Chiba Japan (e-mail: morita.akira@nsc.co.jp) Nagoya Works Nippon Steel Corporation Tokai Aichi Japan (e-mail: ishihara.satoru@nsc.co.jp)

Physics-based models are commonly used for process controls in steel industry. However, parameter adjustments of these models are often difficult due to a large number of parameters and complexity of the models. In this paper, a parameter adjustment method for such physics-based control models is proposed. The adjustment of each parameter is estimated systematically from the process data, avoiding co-linearity problem with partial least squares regression. An application for a hot rolling mill is presented to show its effectiveness.

关键词： process parameter estimation modeling regression statistical analysis

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A stochastic approach for the prediction of PSD in crystallization processes: Analytical solution for the asymptotic behavior and parameter estimation

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COMPUTERS & CHEMICAL ENGINEERING 2011年第11期35卷 2318-2325页

作者： Tronci, Stefania Grosso, Massimiliano Baratti, Roberto Romagnoli, Jose A. Univ Cagliari Dipartimento Ingn Chim & Mat I-09123 Cagliari Italy Louisiana State Univ Dept Chem Engn Baton Rouge LA 70803 USA

Recently, a novel stochastic formulation based on the Fokker-Planck equation (FPE) for the description of anti-solvent mediated crystal growth process was proposed. Here, we further expand these results by analyzing the asymptotic (end of the batch) solution of the FPE for the CSD. In this regard, the analytical solution of the stationary FPE is exploited for predicting the end of the batch CSD as function of the model parameters. Furthermore, the availability of such analytical solution is used to simplify and diminish the computational burden of the parameter estimation problem. Two alternative approaches for parameter estimation are discussed based on the use of the analytical solution of the FPE and of the dynamic of the logistic equation (deterministic component of the FPE approach). Validations against experimental data for the NaCl-water-ethanol anti-solvent crystallization system are presented. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Particle size measurement Brownian motion process parameter estimation Probabilistic models Stochastic modeling

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Modelling and Control of Pressurized Molten Metal in Press Casting

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IFAC Proceedings Volumes 2011年第1期44卷 11726-11731页

作者： Ryosuke Tasaki Yoshiyuki Noda Kazuhiro Terashima Kunihiro Hashimoto Department of Electronic and Information Engineering Toyohashi University of Technology Hibarigaoka 1-1 Tempaku-cho Toyohashi 441-8580 Japan Department of Mechanical Engineering Toyohashi University of Technology Sintokogio Ltd. Honohara 3-1 Toyokawa 442-8505 Japan

Abstract This paper presents a modeling and control of molten metal's pressure in a pressing process using an innovative iron casting developed by our group. In this method, molten metal is directly poured into the lower mold, and then pressed to fill the cavity by the upper mold being lowered down. For the complex liquid flow during pressing, the liquid's pressure change inside vertical path with various contraction and expansion geometries is newly modeled via the unstationary Bernoulli equation. The mathematical model is derived for a control design of pressing. To conduct the pressing velocity design algorithm, an unknown parameter of proposed model considering viscous flow is identified by using CFD: Computational Fluid Dynamics model with heat flow calculation. Control performance using a multi-switching velocity pattern is confirmed as an effective control design using the pressure model, because the pressure fluctuation has discontinuous variation points. Substituting detailed information for mold shape, poured volume and initial temperature into a developed control input generator, an optimum pressing velocity design and a robust design for defect-free production are proposed by the design algorithm based on the construction of an inverse system comprised of the sequential switching from higher to lower speed. Consequently, the effectiveness of the pressing control with reasonable pressure suppression has been demonstrated through the CFD simulation.

关键词： Pressure control Physical models Metals Viscous friction process parameter estimation Sequential switching Industrial production systems

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A Stochastic Formulation for the Prediction of PSD in Crystallization processes: Comparative Assessment of Alternative Model Formulations

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IFAC Proceedings Volumes 2010年第5期43卷 409-414页

作者： M. Grosso R. Baratti J.A. Romagnoli Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari Cagliari Italy On leave from the Department of Chemical Engineering Louisiana State University Baton Rouge LA

A stochastic formulation for the description of antisolvent mediated crystal growth processes is discussed. In the proposed approach the crystal size growth dynamics is driven by a deterministic growth factor coupled to a stochastic component. The evolution in time of the particle size distribution is then described in terms of a Fokker-Planck equation. In this formulation the specific form of the stochastic model leads to different shapes for the probability density function. I this work we investigate and assess comparatively the performance of the FPE approach to model the crystal size distribution based on different expressions for the stochastic component. In particular, we consider the Langevin equation with a multiplicative noise term that depends on the crystal size (time and space). It is shown and corroborated via experimentation that the best stochastic model is given by the Geometric Brownian Motion (GBM). Excellent quantitative agreement between experiments and the predictions from the FPE-GBM model were obtained for a range of conditions. Validations against experimental data are presented for the NaCl-water-ethanol anti-solvent crystallization system.

关键词： particle size measurement Brownian motion process parameter estimation probabilistic models stochastic modeling

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Conceptual modelling and optimization of jacketed tubular reactors for the production of LDPE

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IFAC Proceedings Volumes 2010年第5期43卷 433-438页

作者： Peter M.M. Van Erdeghem Filip Logist Michiel Heughebaert Christoph Dittrich Jan Van Impe BioTeC Department of Chemical Engineering K.U. Leuven W. de Croylaan 46 B-3001 Leuven Belgium. (Tel: +32-16-32.26.75) SABIC Technology Center Geleen P.O. Box 319 6160 AH Geleen The Netherlands

This paper deals with the model based optimization of tubular reactors for the production of LDPE. Due to the high complexity, solving an optimization problem of an industrial application is not straightforward. Often researchers seek the shortest way to reach their final goal by going directly to the development of a high-complexity model and optimize this with respect to a certain objective. Although this approach seems the fastest way to success, it can be a bumpy road with a lot of dead ends. Therefore, a divide and conquer strategy is adopted, i.e., first develop a conceptual low-complexity model, set up the optimization problem and then use the obtained knowledge during the optimization of more complex models. The aim of this paper is to give the results of the three steps which have to be accomplished in order to achieve this subgoal. First, the multizone process of LDPE production is modelled as a sequence of conceptual modules which simulate the steady-state characteristics of one reaction and cooling zone. Then, this model is fitted to industrial data such that it quantitatively describes the real process. Finally, a multiple objective design optimization problem is formulated, i.e., where along the reactor and which amount of initiator has to be injected to maximize the profit at different economic situations.

关键词： Chemical industry multiobjective optimizations process parameter estimation plastics industry multiple-criterion optimization differential equations

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estimation of Damage Behaviour for Model-Based Prognostic

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IFAC Proceedings Volumes 2009年第8期42卷 1444-1449页

作者： David Gucik-Derigny Rachid Outbib Mustapha Ouladsine Laboratory of Information Science and Systems LSIS UMR CNRS 6168 Université Paul Cézanne Aix-Marseille III Domaine universitaire de St Jérôme 13397 Marseille Cedex 20 France

In this paper, preliminary definitions on damage and damage prognosis will be given. A damage prognosis definition will be translated into a mathematical definition. The main contribution is to introduce a model-based prognosis methodology for reconstructing dynamics of cumulative damage state with only inputs and outputs measurements. The methodology is based essentially on the fact that damage will be lead back to an unknown input. The state will be reconstructed exactly in a predefined finite-time using a finite time unknown input observer. An expression of unknown input containing cumulative damage state information will be estimated. Then damage state estimation becomes an inverse problem. Finally, an identification process is designed to identify reconstructed dynamics of cumulative damage state with a class of nonlinear damage models. Simulation results on an academic automotive suspension system will be given to illustrate the pertinence of the approach.

关键词： damage predictive methods process identification process parameter estimation observers

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LOW COMPLEXITY MODELLING AND parameter estimation IN COPPER BIOLEACHING processES

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IFAC Proceedings Volumes 2006年第1期39卷 690-695页

作者： Boris I. Godoy Julio H. Braslavsky Kaushik Mahata Centre for Complex Dynamic Systems and Control The University of Newcastle Callaghan NSW 2308 Australia

In heap bioleaching, metal is dissolved from ore by a percolating solution, catalysed by naturally occurring bacteria. Increased interest in the technology motivated the development of accurate models of such process in recent years. In contrast, the utilisation of such models for control has received limited attention, given their high mathematical complexity. This paper presents a low complexity model aimed as the basis for control design. The model parameters are estimated using least squares on data generated by a high complexity, experimentally validated model provided by BHP Billiton Innovation. Simulation results indicate significant potential of the approach.

关键词： process models Reduced-order models Control oriented models process parameter estimation Recursive least squares

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INFERENCE OF OIL CONTENT IN PETROLEUM WAXES BY ARTIFICIAL NEURAL NETWORKS

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IFAC Proceedings Volumes 2006年第2期39卷 759-764页

作者： Anie D.M. Lima Danilo do C.S. Silva Valéria S. Silva De Souza B. Maurício Research And Development Center (CENPES) Lubricants and Special Products PETROBRAS Rio de Janeiro Brazil School of Chemistry Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil

The reduction of the time required to determine oil content is important in the production of petroleum waxes. Here, it is aimed to generate a model whose output (the inferred oil content) is obtained from inputs given by other characterization parameters (needle penetration, viscosity, density and refractive index) that are obtained from simpler experiments. Laboratory experiment data together with industrial data were employed in the modeling. These ‘real’ data were compared with predictions made by the linear models and artificial neural networks. The networks outperform the linear models, as they generate smaller residuals in the whole operational range considered.

关键词： Nonlinear analysis system Artificial intelligence process parameter estimation Product quality Neural-network models Backpropagation algorithms

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MGP: A TOOL FOR WIDE RANGE TEMPERATURE MODELING

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IFAC Proceedings Volumes 2006年第7期39卷 315-320页

作者： Angel Fernando Kuri Morales Luis Vázquez Seisdedos Instituto Tecnológico Autónomo de México Río Hondo No.1 México 01000 D.F. Departamento de Control Automatico Universidad de Oriente. Ave. Las Americas S/N. Esq. Casero. 90400. Santiago de Cuba Cuba

Superheated steam temperature dynamic is statically non linear. The steam enthalpy exhibits nonlinear dependence on both steam pressure and temperature. Also, the heat transfer process on superheaters and attemperators is strongly non linear and it was, reportedly, very difficult to find a synthetic expression (model). The so-called G enetic M ultivariate P olynomials (GMP) solve this problem by finding the coefficients of a multivariate polynomial for an arbitrary set of data. Although this regression problem has been tackled with success using neural networks (NN) the ‘black box’ characteristic of such models is frequently cited as a major drawback. Despite the restrictions of a polynomial basis, GPMs compete favorably with the NNs without the mentioned limitation. Therefore, a practical tool is proposed for temperature modeling on a wide range real plant operation and its static parameter estimations. Based on advanced simulation tools, the polynomial expression of enthalpy (on a wide range) and the empirical heat transfer equations in superheaters allow us to turn the static parameter estimation from a distributed to a lumped parameter system.

关键词： Mathematical models dynamic modeling process parameter estimation thermal properties lumped parameters system temperature control

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