Steady-state real-time optimization using transient measurements

Abstract Real-time optimization (RTO) is an established technology, where the process economics are optimized using rigourous steady-state models. However, a fundamental limiting factor of current static RTO implementation is the steady-state wait time. We propose a “hybrid” approach where the model adaptation is done using dynamic models and transient measurements and the optimization is performed using static models. Using an oil production network optimization as case study, we show that the Hybrid RTO can provide similar performance to dynamic optimization in terms of convergence rate to the optimal point, at computation times similar to static RTO. The paper also provides some discussions on static versus dynamic optimization problem formulations.

[1]  Qin Li,et al.  Robust Extremum Seeking Control with application to Gas Lifted Oil Wells , 2016 .

[2]  Eric C. Kerrigan,et al.  Noise covariance identification for nonlinear systems using expectation maximization and moving horizon estimation , 2017, Autom..

[3]  L. Lasdon,et al.  Efficient data reconciliation and estimation for dynamic processes using nonlinear programming techniques , 1992 .

[4]  R. Bhushan Gopaluni,et al.  Model Predictive Control in Industry: Challenges and Opportunities , 2015 .

[5]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[6]  Ignacio E. Grossmann,et al.  Review of Mixed‐Integer Nonlinear and Generalized Disjunctive Programming Methods , 2014 .

[7]  Dominique Bonvin,et al.  Modifier-adaptation methodology for real-time optimization , 2009 .

[8]  K. Deergha Rao,et al.  An Approach for a Faster GPS Tracking Extended Kalman Filter , 1995 .

[9]  Sigurd Skogestad,et al.  Plantwide controlA review and a new design procedure ” , 2013 .

[10]  Bjarne Grimstad,et al.  Petroleum production optimization - A static or dynamic problem? , 2017, Comput. Chem. Eng..

[11]  Sigurd Skogestad,et al.  Control structure design for complete chemical plants , 2004, Comput. Chem. Eng..

[12]  Dominique Bonvin,et al.  Dynamic optimization of batch processes: II. Role of measurements in handling uncertainty , 2003, Comput. Chem. Eng..

[13]  M. Xiong,et al.  Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks , 2008, PloS one.

[14]  Fabio dos Santos Liporace,et al.  Challenges and problems with advanced control and optimization technologies , 2009 .

[15]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[16]  José Carlos Pinto,et al.  Common vulnerabilities of RTO implementations in real chemical processes , 2013 .

[17]  Dominique Bonvin,et al.  Use of transient measurements for the optimization of steady-state performance via modifier adaptation , 2014 .

[18]  Bjarne A. Foss,et al.  Production Optimization under Uncertainty - Applied to Petroleum Production , 2015 .

[19]  Dominique Bonvin,et al.  Comparison of Gradient Estimation Methods for Real-time Optimization , 2011 .

[20]  Michael Nikolaou,et al.  RTO: An overview and assessment of current practice , 2011 .

[21]  James B. Rawlings,et al.  Achieving state estimation equivalence for misassigned disturbances in offset‐free model predictive control , 2009 .

[22]  Thomas F. Edgar,et al.  Process Dynamics and Control , 1989 .

[23]  José Carlos Pinto,et al.  Performance Evaluation of Real Industrial RTO Systems , 2016 .

[24]  Frank Allgöwer,et al.  Computational Delay in Nonlinear Model Predictive Control , 2004 .

[25]  Helen Durand,et al.  A tutorial review of economic model predictive control methods , 2014 .

[26]  S. Palanki,et al.  Dynamic Optimization of Batch Processes : II . Handling Uncertainty Using Measurements , 2001 .

[27]  Joel Andersson,et al.  A General-Purpose Software Framework for Dynamic Optimization (Een algemene softwareomgeving voor dynamische optimalisatie) , 2013 .

[28]  Gade Pandu Rangaiah,et al.  Plantwide control : recent developments and applications , 2012 .

[29]  Babu Joseph,et al.  On-line optimization using a two-phase approach: an application study , 1987 .

[30]  R. R. Rhinehart,et al.  An efficient method for on-line identification of steady state , 1995 .

[31]  Eduardo Camponogara,et al.  Robust formulations for production optimization of satellite oil wells , 2017 .

[32]  Roger G. E. Franks,et al.  Mathematical modeling in chemical engineering , 1967 .

[33]  Josef Kallrath,et al.  Mixed Integer Optimization in the Chemical Process Industry: Experience, Potential and Future Perspectives , 2000 .

[34]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[35]  Ole Morten Aamo,et al.  Anti-Slug control of gas-lift wells - Experimental results , 2004 .

[36]  John R. Beaumont,et al.  Control and Coordination in Hierarchical Systems , 1981 .

[37]  John D. Hedengren,et al.  Overview of estimation methods for industrial dynamic systems , 2017 .

[38]  Kartik B. Ariyur,et al.  Real-Time Optimization by Extremum-Seeking Control , 2003 .

[39]  Sigurd Skogestad,et al.  Real-Time Optimization under Uncertainty Applied to a Gas Lifted Well Network , 2016 .

[40]  Dominique Bonvin,et al.  Adaptation strategies for real-time optimization , 2009, Comput. Chem. Eng..

[41]  Bjarne A. Foss,et al.  Oil production optimization - A piecewise linear model, solved with two decomposition strategies , 2010, Comput. Chem. Eng..

[42]  Dominique Bonvin,et al.  Use of measurements for enforcing the necessary conditions of optimality in the presence of constraints and uncertainty , 2005 .

[43]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[44]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[45]  Sigurd Skogestad.  Plantwide control: the search for the self-optimizing control structure , 2000 .

[46]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[47]  Daniel Sarabia,et al.  Modifier Adaptation methodology based on transient and static measurements for RTO to cope with structural uncertainty , 2017, Comput. Chem. Eng..

[48]  J. Maciejowski,et al.  Soft constraints and exact penalty functions in model predictive control , 2000 .

[49]  Bjarne Foss,et al.  Gas Lift Optimization under Uncertainty , 2017 .

[50]  Carol S. Woodward,et al.  Enabling New Flexibility in the SUNDIALS Suite of Nonlinear and Differential/Algebraic Equation Solvers , 2020, ACM Trans. Math. Softw..

[51]  Wolfgang Marquardt,et al.  Towards integrated dynamic real-time optimization and control of industrial processes , 2003 .