THERMAL SCIENCE

International Scientific Journal

ENHANCED CONTROL OF RADIATOR HEATING SYSTEM

ABSTRACT
In this paper a radiator heating system of a building is considered. For the purpose of the heating system optimization, a mathematical model of the system is developed. The linear quadratic algorithm with integral action is proposed and analyzed. This solution has proven to be expensive. Further analysis of the model is done and a reduction of the order of the system is proposed. An inverse-based controller design approach for minimum-phase first order system is used to provide realizable controller in the form of proportional integral controller. Optimal parameters of the control algorithm parameters have been chosen by integral of time absolute error criterion, and also by metaheuristic optimization. According to the real heating demand, a simulation of the plant is performed. Proposed controllers were tested by numerical simulation for a typical winter day for geographical region of the building. It is shown that advanced performance can be achieved with optimized control systems, and that by controller optimization a significant reduction of the energy consumption is obtained without losing the in-door comfort. This has also proved to be more economical solution. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. TR 33047, Grant no. TR35005 and Grant no. TR 35016]
KEYWORDS
PAPER SUBMITTED: 2018-03-23
PAPER REVISED: 2018-07-06
PAPER ACCEPTED: 2018-07-09
PUBLISHED ONLINE: 2019-01-19
DOI REFERENCE: https://doi.org/10.2298/TSCI18S5337R
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2018, VOLUME 22, ISSUE Supplement 5, PAGES [S1337 - S1348]
REFERENCES
  1. Todorović, M., Ristanović, M., Efficient Energy Use in Buildings (in Serbian) University of Belgrade, Belgrade, 2015
  2. Laković, M., et al., Numerical Computational and Prediction of Electricity Consumption in Tobacco In-dustry, Facta Universitatis Series: Mechanical Engineering, 15 (2017), 3, pp. 457-465
  3. Skogestad, S., Postlethwaite I., Multivariable Feedback Control: Analysis and Design, Vol. 2., John Wiley and Sons, New York, USA, 2007
  4. Ogata, K., Modern Control Engineering, Prentice Hall, Englewood Cliffs, N. J., USA, 1997
  5. Astrom, K. J., Murray, R. M., Feedback Systems: an Introduction for Scientists and Engineers, Prince-ton University Press, Oxford, UK, 2010
  6. Franklin, G. F., et al., Feedback Control of Dynamic Systems, Addison-Wesley, Reading, Mass., USA, 1994
  7. Brown, R. G., Patrick, Y. C. H., Introduction to Random Signals and Applied Kalman Filtering: with MATLAB Exercises and Solutions, John Wiley & Sons, New York, USA, 1997
  8. Astrom, K. J., Hagglund, T., PID Controllers: Theory, Design and Tuning, Instrument Society of Amer-ica, Research Triangle Park, N. C., USA, 1995.
  9. Dučić, N., et al., Optimization of the Gating System for Sand Casting Using Genetic Algorithm, Interna-tional Journal of Metalcasting, 11 (2017), 2, pp. 255-265
  10. Precup, R.-E., et al., Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems with Re-duced Parametric Sensitivity, IEEE Transactions on Industrial Electronics, 64 (2017), 1, pp. 527-534
  11. Manić, M., Amarasinghe, K., et al., Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting, IEEE Industrial Electronics Magazine, 10 (2016), 4, pp. 32-49
  12. Mataušek, M. R., Kvaščev, G. S., A Unified Step Response Procedure for Autotuning of PI Controller and Smith Predictor for Stable Processes, Journal of Process Control 13, (2003), 8, pp. 787-800

© 2019 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence