Abstract
Problem formulations and algorithms are considered for optimization problems with differential-algebraic equation (DAE) models. In particular, we provide an overview of direct methods, based on nonlinear programming (NLP), and indirect, or variational, methods. We further classify each method and tailor it to the appropriate applications. For direct methods, we briefly describe current approaches including the sequential approach (or single shooting), multiple shooting method, and the simultaneous collocation (or direct transcription) approach. In parallel to these strategies we discuss NLP algorithms for these methods and discuss optimality conditions and convergence properties. In particular, we present the simultaneous collocation approach, where both the state and control variable profiles are discretized. This approach allows a transparent handling of inequality constraints and unstable systems. Here, large scale nonlinear programming strategies are essential and a novel barrier method, called IPOPT, is described. This NLP algorithm incorporates a number of features for handling large-scale systems and improving global convergence. The overall approach is Newton-based with analytic second derivatives and this leads to fast convergence rates. Moreover, it allows us to consider the extension of these optimization formulations to deal with nonlinear model predictive control and real-time optimization. To illustrate these topics we consider a case study of a low density polyethylene (LDPE) reactor. This large-scale optimization problem allows us to apply off-line parameter estimation and on-line strategies that include state estimation, nonlinear model predictive control and dynamic real-time optimization.
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References
Pontryagin, V.V., Boltyanskii, Y., Gamkrelidze, R., and Mishchenko, E., The Mathematical Theory of Optimal Processes, New York: Interscience, 1962.
Bryson, A.E. and Ho, Y.C., Applied Optimal Control, New York: Hemisphere, 1975.
Cervantes, A.M. and Biegler, L.T., Optimization strategies for dynamic systems, in Encyclopedia of Optimization, Floudas, C.A. and Pardalos, P.M., Eds., Dordrecht: Kluwer, 2001.
Vassiliadis, V.S., Sargent, R.W.H., and Pantelides, C.C., Solution of a class of multistage dynamic optimization problems. Part I. Algorithmic framework, Ind. Eng. Chem. Res, 1994, vol. 33, pp. 2111–2122.
Vassiliadis, V.S., Sargent, R.W.H., and Pantelides, C.C., Solution of a class of multistage dynamic optimization problems. Part II. Problems with path constraints, Ind. Eng. Chem. Res., 1994, vol. 33, pp. 2123–2133.
Barton, P.I., Allgor, R.J., Feehery, W.F., and Galan, S., Dynamic optimization in a discontinuous world, Ind. Eng. Chem. Res., 1998, vol. 37, pp. 966–981.
Ascher, U.M. and Petzold, L.R., Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations, Philadelphia, Pa.: SIAM, 1998.
Flores-Tlacuahuac, A., Biegler, L.T., and Saldivar-Guerra, E., Dynamic optimization of HIPS open-loop unstable polymerization reactors, Ind. Eng. Chem. Res., 2005, vol. 44, pp. 2659–2679.
Bock, H.G., Recent advances in parameter identification techniques for O.D.E., in Numerical Treatment of Inverse Problem in Differential and Integral Equations, Heidelberg: Springer, 1983, pp. 95–121.
Bock, H.G. and Plitt, K.J., A multiple shooting algorithm for direct solution of optimal control problems, Proc. 9th IFAC World Congr., Budapest, 1984.
Leineweber, D.B., Efficient Reduced SQP Methods for the Optimization of Chemical Processes Described by Large Sparse DAE Models, Heidelberg: Univ. of Heidelberg, 1999.
Betts, J.T. and Huffman, W.P., Application of sparse nonlinear programming to trajectory optimization, J. Guid. Control Dyn., 1992, vol. 15, pp. 198–206.
Biegler, L.T., Nonlinear Programming: Concepts, Algorithms and Applications to Chemical Processes, Philadelphia, Pa.: SIAM, 2010.
Cervantes, A.M., Wachter, A., Tutuncu, R., and Biegler, L.T., A reduced space interior point strategy for optimization of differential algebraic systems, Compt. Chem. Eng., 2000, vol. 24, pp. 39–51.
Reddien, G.W., Collocation at Gauss points as a discretization in optimal control, SIAM J. Control Optim., 1979, vol. 17, pp. 298–306.
Cuthrell, J.E. and Biegler, L.T., Simultaneous optimization and solution methods for batch reactor control profiles, Comput. Chem. Eng., 1989, vol. 13, pp. 49–62.
Hager, W.W., Runge-Kutta methods in optimal control and the transformed adjoint system, Numer. Math., 2000, vol. 87, pp. 247–282.
Kameswaran, S. and Biegler, L.T., Convergence rates for direct transcription of optimal control problems using collocation at Radau points, Comput. Optim. Appl., 2008, vol. 41, pp. 81–126.
Betts, J.T. and Campbell, S.L., Discretize Then Optimize, M and CT-TECH-03—01 Technical Report, Boeing Company, 2003.
Wachter, A. and Biegler, L.T., On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming, Math. Program., 2006, vol. 106, pp. 25–57.
Fiacco, A. and McCormick, G., Nonlinear Programming: Sequential Unconstrained Minimization Techniques, New York: Wiley, 1968.
Duff, I.S., MA57—a code for the solution of sparse symmetric definite and indefinite systems, ACM Trans. Math. Software, 2004, vol. 30, pp. 118–144.
Kiparissides, C., Seferlis, P., Mourikas, G., and Morris, A.J., Online optimizing control of molecular weight properties in batch free-radical polymerization reactors, Ind. Eng. Chem. Res., 2002, vol. 41, pp. 6120–6131.
Zavala, V.M. and Biegler, L.T., Large-scale parameter estimation in low-density polyethylene tubular reactors, Ind. Eng. Chem. Res., 2006, vol. 45, pp. 7867–7881.
Zavala, V.M., Laird, C.D., and Biegler, L.T., Interiorpoint decomposition approaches for parallel solution of large-scale nonlinear parameter estimation problems, Chem. Eng. Sci., 2008, vol. 63, p. 4834–4845.
Zavala, V.M., Laird, C.D., and Biegler, L.T., A moving horizon estimation algorithm based on nonlinear programming sensitivity, J. Process Control, 2008, vol. 18, pp. 876–884.
Zavala, V.M. and Biegler, L.T., The advanced step NMPC controller: stability, optimality and robustness, Automatica, 2009, vol. 45, pp. 86–93.
Zavala, V.M. and Biegler, L.T., Nonlinear programming strategies for state estimation and model predictive control, Proc. Int. Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control, Pavia, Italy, 2008.
Buchelli, A., Call, M.L., Brown, A.L., Bird, A., Hearn, S., and Hannon, J., Modeling fouling effects in LDPE tubular polymerization reactors. 1. Fouling thickness determination, Ind. Eng. Chem. Res., 2005, vol. 44, pp. 1474–1479.
Engell, S., Feedback control for optimal process operation, J. Process Control, 2007, vol. 17, pp. 203–219.
Biegler, L.T. and Zavala, V.M., Large-scale nonlinear programming using IPOPT: an integrating framework for enterprise-wide dynamic optimization, Comput. Chem. Eng., 2009, vol. 33, pp. 575–582.
Zavala, V.M. and Biegler, L.T., Optimization-based strategies for the operation of low-density polyethylene tubular reactors: nonlinear model predictive control, Comput. Chem. Eng., 2009, vol. 33, pp. 1735–1746.
Biegler, L.T., An overview of simultaneous dynamic optimization strategies, Chem. Eng. Process., 2007, vol. 46, pp. 1043–1063.
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Biegler, L.T. Nonlinear programming strategies for dynamic chemical process optimization. Theor Found Chem Eng 48, 541–554 (2014). https://doi.org/10.1134/S0040579514050157
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DOI: https://doi.org/10.1134/S0040579514050157