Industrial challenges in modeling of processes and model reduction

Abstract Currently a shift of focus towards 6-sigma quality and market responsive operation has been initiated in the chemical processing industries. The fast evolution of products and processes enforced by fierce global competition and by tightening legislation are major forces for new application development approaches and for new technologies. The need of high performance non-linear model based control, optimization, monitoring and soft sensing applications and the cost driven necessity of reuse of models and results of earlier engineering effort will be explained to be the drivers for the current and future industrial challenges in (hybrid) modelling and system identification. These market developments require more extensive application of (nonlinear) rigorous models extended with empirical model components to achieve the model accuracy requirements, the coverage of wide process operating ranges and minimization of engineering costs, which cannot be attained by application of pure black box modelling approaches. Besides the techniques applied for hybrid modelling and parameter estimation, the paper also discusses the techniques needed for model reduction, model tracking and state estimation to make the high performance model based applications work properly. An overview with some results is given of the techniques applied and tested in our current R&D industrial pilot projects.

[1]  Edriss S. Titi,et al.  Efficient methods using high accuracy approximate inertial manifolds , 2001, Numerische Mathematik.

[2]  Jan C. Willems,et al.  From time series to linear system - Part II. Exact modelling , 1986, Autom..

[3]  Β. L. HO,et al.  Editorial: Effective construction of linear state-variable models from input/output functions , 1966 .

[4]  Karl Johan Åström,et al.  BOOK REVIEW SYSTEM IDENTIFICATION , 1994, Econometric Theory.

[5]  Prodromos Daoutidis,et al.  Nonlinear model reduction of chemical reaction systems , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[6]  P. Van Overschee,et al.  Subspace algorithms for the stochastic identification problem , 1991 .

[7]  B. Moore.  Principal component analysis in linear systems: Controllability, observability, and model reduction , 1981 .

[8]  Emanuele Carpanzano.  Order Reduction of General Nonlinear DAE Systems by Automatic Tearing , 2000 .

[9]  I. Jaimoukha.  A general minimal residual Krylov subspace method for large-scale model reduction , 1997, IEEE Trans. Autom. Control..

[10]  BaiZhaojun.  Krylov subspace techniques for reduced-order modeling of large-scale dynamical systems , 2002 .

[11]  P. V. D. Hof,et al.  Analysis of closed-loop identification with a tailor-made parametrization , 1997 .

[12]  P. Overschee.  Subspace Identification: Theory, Implementation, Application , 1995 .

[13]  Patrick Dewilde,et al.  Subspace model identification Part 1. The output-error state-space model identification class of algorithms , 1992 .

[14]  Yucai Zhu,et al.  Identification of Multivariable Industrial Processes: for Simulation, Diagnosis and Control , 1993 .

[15]  George Stephanopoulos,et al.  Integrating Information, Management and Control in Process Industries , 1995 .

[16]  József Bokor,et al.  Identification with generalized orthonormal basis functions - statistical analysis and error bounds , 1994 .

[17]  D. K. De Vries,et al.  Identification of model uncertainty for control design , 1994 .

[18]  Ioannis P. Androulakis,et al.  Kinetic mechanism reduction based on an integer programming approach , 2000 .

[19]  W. H. Weinberg,et al.  Combinatorial materials science: Paradigm shift in materials discovery and optimization , 1999 .

[20]  J. Marsden,et al.  A subspace approach to balanced truncation for model reduction of nonlinear control systems , 2002 .

[21]  L. Silverman,et al.  Model reduction via balanced state space representations , 1982 .

[22]  Ton Backx,et al.  TOWARDS INTENTIONAL DYNAMICS IN SUPPLY CHAIN CONSCIOUS PROCESS OPERATIONS , 1998 .

[23]  A.C.P.M. Backx,et al.  Identification for the control of MIMO industrial processes , 1991 .

[24]  Juergen Hahn,et al.  Reduction of stable differential–algebraic equation systems via projections and system identification , 2005 .

[25]  Constantinos C. Pantelides,et al.  PROCESS MODELLING TECHNOLOGY : A CRITICAL REVIEW OF RECENT DEVELOPMENTS , 2004 .

[26]  Z. Bai.  Krylov subspace techniques for reduced-order modeling of large-scale dynamical systems , 2002 .

[27]  R. B. Newell,et al.  Control relevant model reduction: a reduced order model for ‘model IV’ fluid catalytic cracking units , 1994 .

[28]  Boris Lohmann,et al.  Order reduction and determination of dominant state variables of nonlinear systems , 1995 .

[29]  Stefan Volkwein,et al.  Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics , 2002, SIAM J. Numer. Anal..

[30]  P. V. D. Hof,et al.  Frequency domain identification with generalized orthonormal basis functions , 1995, IEEE Trans. Autom. Control..

[31]  O. Bosgra,et al.  Approximate system identification using system based orthonormal functions , 1990, 29th IEEE Conference on Decision and Control.

[32]  L. Petzold,et al.  Model reduction for chemical kinetics: an optimization approach , 1999 .

[33]  J. Richalet,et al.  Model predictive heuristic control: Applications to industrial processes , 1978, Autom..

[34]  Edriss S. Titi,et al.  An approximate inertial manifolds approach to postprocessing the Galerkin method for the Navier-Stokes equations , 1999, Math. Comput..

[35]  Wolfgang Marquardt,et al.  Integration of Model Predictive Control and Optimization of Processes: Enabling Technology for Market Driven Process Operation , 2000 .

[36]  Bjarne A. Foss,et al.  A field study of the industrial modeling process , 1998 .

[37]  Tom T. Hartley,et al.  A fundamental theorem for the model reduction of nonlinear systems , 1992 .

[38]  Jan C. Willems,et al.  From time series to linear system - Part I. Finite dimensional linear time invariant systems , 1986, Autom..

[39]  R. G. Hakvoort,et al.  System identification for rubust process control. Nominal models and error bounds , 1994 .

[40]  Hm Heinz Falkus,et al.  Parametric uncertainty in system identification , 1994 .

[41]  Yucai Zhu.  New Developments in Industrial MPC Identification , 2003 .

[42]  Ulrich Maas,et al.  Simplifying chemical kinetics: Intrinsic low-dimensional manifolds in composition space , 1992 .

[43]  Antonios Armaou,et al.  Computation of empirical eigenfunctions and order reduction for nonlinear parabolic PDE systems with time-dependent spatial domains , 2001 .

[44]  Lennart Ljung,et al.  A projection method for closed-loop identification , 2000, IEEE Trans. Autom. Control..

[45]  Muruhan Rathinam,et al.  A New Look at Proper Orthogonal Decomposition , 2003, SIAM J. Numer. Anal..

[46]  Acpm Ton Backx,et al.  Identification of an industrial process : a Markov parameter approach , 1987 .

[47]  George N. Saridis,et al.  A model reduction technique for nonlinear systems , 1980, Autom..

[48]  Lennart Ljung,et al.  Comparing different approaches to model error modeling in robust identification , 2002, Autom..

[49]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[50]  H. Zeiger,et al.  Approximate linear realizations of given dimension via Ho's algorithm , 1974 .

[51]  Lucia Russo,et al.  Construction of approximate inertial manifold by decimation of collocation equations of distributed parameter systems , 2002 .

[52]  Prodromos Daoutidis,et al.  Singular perturbation modeling of nonlinear processes with nonexplicit time-scale multiplicity , 1998 .

[53]  Vladimir Mahalec,et al.  MODELING AND SIMULATION IN 2004: AN INDUSTRIAL PERSPECTIVE , 2004 .

[54]  Wolfgang Marquardt,et al.  Order reduction of non-linear differential-algebraic process models , 1991 .

[55]  Athanasios C. Antoulas,et al.  An overview of approximation methods for large-scale dynamical systems , 2005, Annu. Rev. Control..

[56]  K. Lee,et al.  A control-relevant model reduction technique for nonlinear systems , 2000 .

[57]  Thomas F. Edgar,et al.  Order reduction of large scale DAE models , 2004, Comput. Chem. Eng..

[58]  J.L.A. Koolen,et al.  Plant operation in the future , 1994 .

[59]  PJ Pieter Heres,et al.  Robust and Efficient Krylov Subspace Methods for Model Order Reduction , 2005 .

[60]  Athanasios C. Antoulas,et al.  Approximation of Large-Scale Dynamical Systems , 2005, Advances in Design and Control.

[61]  Wolfgang Marquardt,et al.  Adaptive model reduction and simulation of thermal cracking of multicomponent hydrocarbon mixtures , 2000 .

[62]  Saibal Ganguly,et al.  A semi-rigorous dynamic distillation model for on-line optimization and nonlinear control , 1993 .

[63]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[64]  Constantinos Theodoropoulos,et al.  Order reduction for nonlinear dynamic models of distributed reacting systems , 2000 .

[65]  P. Eykhoff.  System Identification Parameter and State Estimation , 1974 .

[66]  W. Marquardt,et al.  Towards a Process Modeling Methodolgy , 1995 .

[67]  Jan C. Willems,et al.  From time series to linear system - Part III: Approximate modelling , 1987, Autom..

[68]  Michael Baldea,et al.  Model Reduction and Control in Reactor-Heat Exchanger Networks , 2004 .

[69]  Bjarne A. Foss,et al.  A field study of the industrial modeling process , 1997 .

[70]  Wolfgang Marquardt,et al.  Nonlinear Model Reduction for Optimization Based Control of Transient Chemical Processes , 2002 .