Multi-layer health-aware economic predictive control of a pasteurization pilot plant

Open access

Abstract

This paper proposes two different health-aware economic predictive control strategies that aim at minimizing the damage of components in a pasteurization plant. The damage is assessed with a rainflow-counting algorithm that allows estimating the components’ fatigue. By using the results obtained from this algorithm, a simplified model that characterizes the health of the system is developed and integrated into the predictive controller. The overall control objective is modified by adding an extra criterion that takes into account the accumulated damage. The first strategy is a single-layer predictive controller with an integral action to eliminate the steady-state error that appears when adding the extra criterion. In order to achieve the best minimal accumulated damage and operational costs, the single-layer approach is improved with a multi-layer control scheme, where the solution of the dynamic optimization problem is obtained from the model in two different time scales. Finally, to achieve the advisable trade-off between minimal accumulated damage and operational costs, both control strategies are compared in simulation over a utility-scale pasteurization plant.

Aadaleesan, P., Miglan, N., Sharma, R. and Saha, P. (2008). Nonlinear system identification using Wiener type Laguerre-wavelet network model, Chemical Engineering Science 63(15): 3932-3941.

Alamir, M. (2009). A framework for monitoring control updating period in real-time NMPC schemes, in L. Magni et al. (Eds.), Nonlinear Model Predictive Control, Springer, Berlin/Heidelberg, pp. 433-445.

Alastruey, C.F., De la Sen, M. and García-Sanz, M. (1999). Modelling and identification of a high temperature short time pasteurization process including delays, Proceedings of the 7th Mediterranean Conference on Control and Automation, Haifa, Israel, pp. 28-30.

Armfield (2015). Process plant trainer PTC23-MKII, Instruction manual, www.discoverarmfield.com/en/products/view/pct23/process-plant-trainer-process-control-trainer.

de Jesus Barradas-Berglind, J., Wisniewski, R. and Soltani, M. (2015). Fatigue damage estimation and data-based control for wind turbines, IET Control Theory & Applications 9(7): 1042-1050.

Downing, S.D. and Socie, D. (1982). Simple rainflow counting algorithms, International Journal of Fatigue 4(1): 31-40.

Ellis, M., Durand, H. and Christofides, P.D. (2014). A tutorial review of economic model predictive control methods, Journal of Process Control 24(8): 1156-1178.

Endo, T., Mitsunaga, K. and Nakagawa, H. (1967). Fatigue of metals subjected to varying stress-prediction of fatigue lives, Preliminary Proceedings of the Chugoku-Shikoku District Meeting, Tokyo, Japan, pp. 41-44.

Fonte, M., Anes, V., Duarte, P., Reis, L. and Freitas, M. (2015). Crankshaft failure analysis of a boxer diesel motor, Engineering Failure Analysis 56: 109-115.

González, A., Adam, E. and Marchetti, J. (2008). Conditions for offset elimination in state space receding horizon controllers: A tutorial analysis, Chemical Engineering and Processing: Process Intensification 47(12): 2184-2194.

Grosso, J.M., Ocampo-Martinez, C. and Puig, V. (2016). Reliability-based economic model predictive control for generalised flow-based networks including actuators’ health-aware capabilities, International Journal of Applied Mathematics and Computer Science 26(3): 641-654, DOI: 10.1515/amcs-2016-0044.

Hammerum, K., Brath, P. and Poulsen, N.K. (2007). A fatigue approach to wind turbine control, Journal of Physics: Conference Series 75(1): 012081.

Hrovat, D., Di Cairano, S., Tseng, H.E. and Kolmanovsky, I.V. (2012). The development of model predictive control in automotive industry: A survey, IEEE International Conference on Control Applications (CCA), Dubrovnik, Croatia, pp. 295-302.

Ibarrola, J., Guillén, J., Sandoval, J. and García-Sanz,M. (1998). odelling of a high temperature short time pasteurization process, Food Control 9(5): 267-277.

Ibarrola, J., Sandoval, J., Garcıa-Sanz, M. and Pinzolas, M. (2002). Predictive control of a high temperature-short time pasteurisation process, Control Engineering Practice 10(7): 713-725.

Karimi Pour, F., Ocampo-Martinez, C. and Puig, V. (2017a). Output-feedback model predictive control of a pasteurization pilot plant based on an LPV model, Journal of Physics: Conference Series 783: 012029.

Karimi Pour, F., Puig, V. and Ocampo-Martinez, C. (2017b). Health-aware model predictive control of pasteurization plant, Journal of Physics: Conference Series 783(1): 012030.

Khadir, M.T. (2011). Enthalpy predictive functional control of a pasteurisation plant based on a plate heat exchanger, International Journal of Modelling, Identification and Control 13(1-2): 78-87.

Körber, A. (2014). Extreme and Fatigue Load Reducing Control for Wind Turbines: A Model Predictive Control Approach Using Robust State Constraints, PhD thesis, Technische Universität Berlin, Berlin.

Kwakernaak, H. and Sivan, R. (1972). Linear Optimal Control Systems, Vol. 1, Wiley, New York, NY.

Lee, Y.L. (2005). Fatigue Testing and Analysis: Theory and Practice, Vol. 13, Butterworth-Heinemann, Oxford.

Łobos, E. and Momot, M. (2002). Reliability design of complex systems by minimizing the lifetime variance, International Journal of Applied Mathematics and Computer Science 12(4): 553-557.

Marin, J.C., Barroso, A., Paris, F. and Canas, J. (2008). Study of damage and repair of blades of a 300 kW wind turbine, Energy 33(7): 1068-1083.

Miner, M.A. (1945). Cumulative damage in fatigue, Journal of Applied Mechanics 12(3): 159-164.

Mokhtar, W., Taip, F.S., Aziz, N. and Noor, S. (2012). Process control of pink guava puree pasteurization process: Simulation and validation by experiment, International Journal on Advanced Science, Engineering and Information Technology 2(4): 302-305.

Montes de Oca, S., Puig, V. and Blesa, J. (2012). Robust fault detection based on adaptive threshold generation using interval LPV observers, International Journal of Adaptive Control and Signal Processing 26(3): 258-283.

Musallam, M. and Johnson, C.M. (2012). An efficient implementation of the rainflow counting algorithm for life consumption estimation, IEEE Transactions on Reliability 61(4): 978-986.

Muske, K.R. and Badgwell, T.A. (2002). Disturbance modeling for offset-free linear model predictive control, Journal of Process Control 12(5): 617-632.

Niamsuwan, S., Kittisupakorn, P. and Mujtaba, I.M. (2014). Control of milk pasteurization process using model predictive approach, Computers & Chemical Engineering 66: 2-11.

Niesłony, A. (2009). Determination of fragments of multiaxial service loading strongly influencing the fatigue of machine components, Mechanical Systems and Signal Processing 23(8): 2712-2721.

Ocampo, R. (2008). Fatigue failures in pumps. Part 1, World Pumps 2008(500): 42-45.

Odgaaard, P.F., Knudsen, T., Overgaard, A., Steffensen, H. and Jørgensen, M. (2015). Importance of dynamic inflow in model predictive control of wind turbines, IFACPapersOnLine 48(30): 90-95.

Osgood, C.C. (2013). Fatigue Design, International Series on the Strength and Fracture ofMaterials and Structures, Elsevier, Cranbury, NJ.

Pannocchia, G. and Rawlings, J.B. (2003). Disturbance models for offset-free model-predictive control, AIChE Journal 49(2): 426-437.

Rosich, A. and Ocampo-Martinez, C. (2015). Real-time experimental implementation of predictive control schemes in a small-scale pasteurization plant, in S. Olaru et al. (Eds.), Developments in Model-Based Optimization and Control, Springer, Cham, pp. 255-273.

Rychlik, I. (1987). A new definition of the rainflow cycle counting method, International Journal of Fatigue 9(2): 119-121.

Sanchez, H., Escobet, T., Puig, V. and Odgaard, P.F. (2015). Health-aware model predictive control of wind turbines using fatigue prognosis, IFAC-PapersOnLine 48(21): 1363-1368.

Yetendje, A., Seron, M. and De Doná, J. (2012). Robust multisensor fault tolerant model-following MPC design for constrained systems, International Journal of Applied Mathematics and Computer Science 22(1): 211-223, DOI: 10.2478/v10006-012-0016-7.

International Journal of Applied Mathematics and Computer Science

Journal of the University of Zielona Góra

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