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Simulation-Based Model Optimization for a Steam-Filled Chamber. Part I: Open-Loop Identification

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This paper presents a study of simulation-based model optimization for an industrial application of a steam-filled chamber under different operating conditions. Open-loop input/output process data are used to optimize selected linear models of different complexity in the form of continuous transfer functions. The optimization is performed using the MATLAB computing system and its toolboxes for simulation and optimization. More specifically, the nonlinear programming solver fmincon has been fruitfully utilized in this study for the task. Based on the suggested criteria, a suitable model in the form of a second-order astatic system with time-delay has been chosen as a trade-off between its simplicity and fidelity, which is confirmed by experimental comparison. As the modelled process is non-linear in nature, the resultant model parameters vary for different process conditions. The results of this work are further usable for both building a simulation testing model of the system and for the subsequent step – control system design and tuning purposes. The suggested solution enables to obtain a relatively simple but control-relevant model of the investigated process, linking directly controlled and control input variables, which is advantageous from the control system design point of view.
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Experimental Identification; MATLAB; Model Optimization; Steam Pressure Control

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C. Brusilov, and B. Joseph, Techniques of Model-Based Control (Prentice Hall, 2002).

W. Wu, Model-Based Design for Effective Control System Development (IGI Global, 2017).

K.J. Åström, and B. Wittenmark, Adaptive Control (Addison-Wesley, 1995).

M. Morari, and E. Zafirou, Robust Process Control (Prentice Hall, 1989).

P.E. Wellstead, Introduction to Physical System Modelling (Academic Press, 2000).

L. Ljung, System Identification: Theory for the User (Prentice Hall, 1999).

P Venkataraman, Applied Optimization with MATLAB Programming (Wiley, 2009).

X. Yan, A Numerical Modeling of Dynamic Curing Process of Tire by Finite Element, Polymer Journal, Vol. 39(Issue 10):1001-1010, August 2007.

M. Rafei, M.H.R. Ghoreishy, and G. Naderi, Development of an Advanced Computer Simulation Technique for the Modeling of Rubber Curing Process, Computational Materials Science, Vol. 47(Issue 2):539-547, December 2009.

M. H. R. Ghoreishy, A State-of-the-Art Review on the Mathematical Modeling and Computer Simulation of Rubber Vulcanization Process, Iranian Polymer Journal, Vol. 25:89-109, January 2016.

Y. Wang, B. Su, and J. Wu, Simulation and Optimization of Giant Radial Tire Vulcanization Process, Procedia Engineering, Vol. 31:723-726, 2012.

S. Bosselmann, T. Frank, M. Wielitzka, and T. Ortmaier, A Optimization of Process Parameters for Rubber Curing in Relation to Vulcanization Requirements and Energy Consumption, IEEE/ASME Int. Conf. Advanced Intelligent Mechatronics (AIM), pp. 804-809, Auckland, NZ, July 2018.

T. Frank, H. Zeipel, M. Wielitzka, S. Bosselmann, and T. Ortmaier, A Real-Time Prediction of Curing Processes using Model Order Reduction, IFAC-PapersOnLine, Vol. 53(Issue 2):11132-11137, 2020.

T. Berger, and M. Kaliske, A Thermo-mechanical Material Model for Rubber Curing and Tire Manufacturing Simulation, Computational Mechanics, Vol. 66(Issue 2):513-535, September 2020.

Z. Zhou, B. Smith, and G. Yadigaroglu, A Mathematical Model and its Analytical Solution for Slow Depressurization of a Gas-Filled Vessel, Journal of Engineering Mathematics, Vol. 31(Issue 1):43-57, January 1997.

P. He, B. Zhang, C. Zhu, Z. Ji, and C.-H. Lin, Dynamic Process Modeling on Depressurization by Cooling-Controlled Condensation in a Closed Chamber, International Journal of Heat and Mass Transfer, Vol. 78:330-340, November 2014.

Z. Zargar and S.M. Farouq Ali, Analytical Modelling of Steam Chamber Rise Stage of Steam-Assisted Gravity Drainage (SAGD) Process, Fuel, Vol. 233:732-742, December 2018.

M. Trojan, Modeling of a Steam Boiler Operation Using the Boiler Nonlinear Mathematical Model, Energy, Vol. 175:1194-1208, May 2019.

F. Li, J. Yuan, J. Chen and K. Zhang, Standard Heat Consumption Modelling Calculation and Operation Optimization of Boiler Steam Temperature System, IOP Conference Series: Earth and Environmental Science, Vol. 772, 012041, Tianjin, China, May 2021.

J.C. Lagarias, J.A. Reeds, M.H. Wright, and P.E. Wright, Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions, SIAM Journal of Optimization, Vol. 9(Issue 1):112-147, 1998.

S.P. Han, A Globally Convergent Method for Nonlinear Programming, Journal of Optimization Theory and Applications, Vol. 22(Issue 3):297-309, July 1977.

M.J.D. Powell, A Fast Algorithm for Nonlinearly Constrained Optimization Calculations, In: G.A. Watson (eds) Numerical Analysis, Lecture Notes in Mathematics, Vol. 630, Springer-Verlag, 1978.

P.E. Gill, W. Murray, and M.H. Wright, Practical Optimization (Academic Press, 1981).


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