Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model

• Autorzy: Tuan T. T. , Tufa L. D. , Matalib M. I. A. , Ramli N. M.

Identyfikatory

DOI

10.24425/119076

• Języki publikacji: EN

Abstrakty

EN

In advanced control, a control target tracks the set points and tends to achieve optimal operation of a process. Model predictive control (MPC) is used to track the set points. When the set points correspond to an optimum economic trajectory that is sent from an economic layer, the process will be gradually reaching the optimal operation. This study proposes the integration of an economic layer and MPC layer to solve the problem of different time scale and unreachable set points. Both layers require dynamic models that are subject to objective functions. The prediction output of a model is not always asymptotically equal to the measured output of a process. Therefore, Kalman filter is proposed as a state feedback to the two-layer integration. The proposed controller only considers the linear empirical model and the inherent model is identified by system identification, which is assumed to be an ample representation of the process. A depropanizer process case study has been used for demonstration of the proposed technique. The result shows that the proposed controller tends to improve the profit of the process smoothly and continuously, until the process reaches an asymptotically maximum profit point.

Słowa kluczowe

EN

integrate economic optimization and MPC; Kalman filter; Matlab-Hysys Interface; integrate RTO-MPC; depropanizer

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2018
• Tom: Vol. 28, No. 1
• Strony: 35--50
• Opis fizyczny: Bibliogr. 18 poz., rys., wykr., wzory

Twórcy

autor

Tuan T. T.

• Chemical Engineering Department, Universiti Teknologi Petronas, Tronoh, Malaysia

autor

Tufa L. D.

• Chemical Engineering Department, Universiti Teknologi Petronas, Tronoh, Malaysia

autor

Matalib M. I. A.

• Chemical Engineering Department, Universiti Teknologi Petronas, Tronoh, Malaysia

autor

Ramli N. M.

• Chemical Engineering Department, Universiti Teknologi Petronas, Tronoh, Malaysia

Bibliografia

• 1] C.-M. Ying and B. Joseph: Performance and stability analysis of LP-MPC and QP-MPC cascade control systems. AIChE J., 45 (1999) 1521-1534.
• [2] M. L. Darby, M. Nikolau, J. Jones and D. Nicholson: RTO: An overview and assessment of current practice. J. Process Control, 21 (2011) 874-884.
• [3] L. A. Alvarez and D. Odloak: Robust integration of real time optimization with linear model predictive control. Comput. Chem. Eng., 34 (2010) 1937-1944.
• [4] A. G. Marchetti, A. Ferramosca and A. H. González: Steady-state target optimization designs for integrating real-time optimization and model predictive control. J. Process Control, 24 (2014) 129-145.
• [5] C. R. Cutler and B. L. Ramak: Dynamic matrix control-A computer control algorithm, p. 72, 1980.
• [6] J. Kadam, W. Marquardt, M. Schlegel, T. Backx, O. Bosgra, P. Brouwer, G. Dünnebier, D. Van Hessem, A. Tiagounov and S. De Wolf: Towards integrated dynamic real-time optimization and control of industrial processes. Proc. Found. Comput.-Aided Process Oper. FOCAPO2003, (2003) 593-596.
• [7] L. Würth, R. Hannemann and W. Marquardt: A two-layer architecture for economically optimal process control and operation. J. Process Control, 21 (2011) 311-321.
• [8] M. Ellis and P. D. Chistofides: Integrating dynamic economic optimization and model predictive control for optimal operation of nonlinear process systems. Control Eng. Pract., 22 (2014) 242–251.
• [9] V. Adetola and M. Guay: Integration of real-time optimization and model predictive control. J. Process Control, 20 (2010) 125-133.
• [10] G. De Souza, D. Odloak and A. C. Zanin: Real time optimization (RTO) with model predictive control (MPC). Comput. Chem. Eng., 34 (2010) 1999-2006.
• [11] M. Morari and J. H. Lee: Model predictive control: past, present and future. Comput. Chem. Eng., 23 (1999) 667-682.
• [12] X. Jin, B. Huang and D. S. Shook: Multiple model LPV approach to nonlinear process identification with EM algorithm. J. Process Control, 21 (2011) 182-193.
• [13] J. B. Rawlings, D. Angeli and C. N. Bates : Fundamentals of economic model predictive control. 2012 IEEE 51st IEEE Conf. Decis. Control CDC, 2012, pp. 3851-3861.
• [14] D. Simon: Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. John Wiley & Sons, 2006.
• [15] T. T. Tuan, L. D. Tufa, M. I. A. Mutalib and A. F. M. Abdallah: Control of Depropanizer in Dynamic Hysys Simulation Using MPC in Matlab-Simulink. Procedia Eng., 148 (2016) 1104-1111.
• [16] T. B. Olaf: A toolbox for using MATLAB as an activeX/COM controller for Hysys, Matlab Central., Comput. Stuff. (2008). http://www.pvv.org/~olafb/hysyslib/.
• [17] P. Van Overschee, B. De Moor: N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica, 30 (1994) 75-93.
• [18] W. Favorell, B. De Moor and P. Van Overschee: Subspace state space system identification for industrial processes. J. Process Control, 10 (2000) 149-155.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).