The online use of first-principles models in process operations: Review, current status and future needs

Abstract The online use of first-principles models (FPMs) to support process operations has been practised in the chemical and petroleum industry for over 40 years. FPMs can encapsulate a large amount of process knowledge and many companies have realized significant value from the use of these models in online model based applications (OMBAs). Such applications include real-time optimization, model predictive control, data reconciliation, virtual sensors, and process performance monitoring to name a few. The sophistication of both the FPM models and applications based on them has increased over time. At some points in the evolution certain applications were not successful due to issues related to sustainability, which includes model complexity, solvability, maintainability and tractability. Also, model development cost can be a factor in considering the type of model used in these applications. Hence many simplified and empirical model-based online applications became preferred in some domains, even though the overall prediction quality of the FPM may be superior. This paper will review the past experiences, current status and future challenges related to FPM based online modeling applications. There are many areas where the issues related to FPMs can be addressed through proper model management, better software tools and improved technical approaches and work processes. It is hoped that this paper can serve as a basis to promote an understanding of the issues for researchers, modeling software vendors, modeling engineers, and application engineers and help to stimulate improvements in this area leading to increased usage and value of FPMs in supporting process operations.

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

[2]  F. Allgöwer,et al.  Nonlinear Model Predictive Control: From Theory to Application , 2004 .

[3]  M. Matzopoulos,et al.  Improve engineering via whole-plant design optimization: New simulation methods identify cost-effective advantages early , 2010 .

[4]  T. Backx,et al.  Grade-change control using INCA model predictive controller: application on a Dow polystyrene process model , 2003, Proceedings of the 2003 American Control Conference, 2003..

[5]  C. Pantelides,et al.  A distributed memory parallel algorithm for the efficient computation of sensitivities of differential-algebraic systems , 1998 .

[6]  J.D. Hedengren,et al.  Moving Horizon Estimation and Control for an Industrial Gas Phase Polymerization Reactor , 2007, 2007 American Control Conference.

[7]  Jose A. Romagnoli,et al.  A framework for on-line full optimising control of chemical processes , 2005 .

[8]  Martin Schlegel,et al.  Optimization and Control of Polymerization Processes , 2005 .

[9]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[10]  O. A. Asbjornsen,et al.  Simultaneous optimization and solution of systems described by differential/algebraic equations , 1987 .

[11]  Siep Weiland,et al.  Identification of low order parameter varying models for large scale systems , 2009 .

[12]  Efstratios N. Pistikopoulos,et al.  Perspectives in Multiparametric Programming and Explicit Model Predictive Control , 2009 .

[13]  Wolfgang Marquardt,et al.  Industrial challenges in modeling of processes and model reduction , 2006 .

[14]  Rui Huang,et al.  Nonlinear Model Predictive Control and Dynamic Real Time Optimization for Large-scale Processes , 2010 .

[15]  Sebastian Engell.  Feedback control for optimal process operation , 2007 .

[16]  Jobert Ludlage,et al.  Model-based Optimal Control of Industrial Batch Crystallizers , 2010 .

[17]  Masoud Soroush,et al.  Multirate nonlinear state estimation with application to a polymerization reactor , 1999 .

[18]  C. Kravaris,et al.  Multivariable nonlinear control of a continuous polymerization reactor: An experimental study , 1993 .

[19]  Babatunde A. Ogunnaike.  On-line modelling and predictive control of an industrial terpolymerization reactor , 1994 .

[20]  Ton Backx,et al.  Plantwide Economical Dynamic Optimization: Application of a Borealis Borstar Process Model , 2004 .

[21]  Wolfgang Marquardt,et al.  Neighboring-extremal updates for nonlinear model-predictive control and dynamic real-time optimization , 2009 .

[22]  Sandro Macchietto,et al.  Statistical tools for optimal dynamic model building , 2000 .

[23]  James B. Rawlings,et al.  Critical Evaluation of Extended Kalman Filtering and Moving-Horizon Estimation , 2005 .

[24]  J. van den Berg,et al.  Model reduction for dynamic real-time optimization of chemical processes , 2005 .

[25]  Victor M. Zavala,et al.  The advanced-step NMPC controller: Optimality, stability and robustness , 2009, Autom..

[26]  Stefan Feuerriegel,et al.  Parallel sensitivity analysis for efficient large-scale dynamic optimization , 2011 .

[27]  Seongkyu Yoon,et al.  Statistical and causal model‐based approaches to fault detection and isolation , 2000 .

[28]  Wolfgang Marquardt,et al.  A grey-box modeling approach for the reduction of nonlinear systems , 2008 .

[29]  P. Astrid,et al.  Reduction of process simulation models : a proper orthogonal decomposition approach , 2004 .

[30]  Jay H. Lee,et al.  Model predictive control: Review of the three decades of development , 2011 .

[31]  Victor M. Zavala,et al.  Optimization-based strategies for the operation of low-density polyethylene tubular reactors: Moving horizon estimation , 2009, Comput. Chem. Eng..

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

[33]  R. Nath,et al.  Joint optimization of process units and utility system , 1986 .

[34]  Kenneth V Allsford,et al.  Experiences in the Building of Dynamic Flowsheet Models for Embedded Nonlinear Control of Polymer Processes , 2008 .

[35]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[36]  B. Froisy,et al.  Industrial Application of On-line First Principle Dynamic Models using State Estimation , 1999 .

[37]  Robert E. Young,et al.  Evolution of an Industrial Nonlinear Model Predictive Controller , 2002 .

[38]  Sk Satyajit Wattamwar,et al.  Identification of low-order parameter-varying models for large-scale systems , 2010 .

[39]  Julie F. Smith,et al.  Can simulation technology enable a paradigm shift in process control?: Modeling for the rest of us , 2006, Comput. Chem. Eng..

[40]  Costas Kravaris,et al.  Nonlinear control of a batch polymerization reactor: An experimental study , 1992 .