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Low-‎computational adaptive MPC algorithmization strategy for over ‎and ‎undershoots ‎instantaneous ‎water ‎heaters stability

Yıl 2023, Cilt: 3 Sayı: 1, 19 - 24, 22.06.2023
https://doi.org/10.14744/seatific.2023.0003

Öz

Tankless gas Hot Water users' ‎comforting perception ‎is severely affected by ‎sudden ‎changes ‎in temperature ‎apart from ‎the ‎desired temperature. The instability of the ‎water ‎temperature ‎with ‎overshoots and ‎undershoots is the ‎most common disadvantage ‎that ‎appears ‎mainly ‎because of the sudden changes in ‎the water flow demanded by ‎users ‎and ‎the ‎response delays inherent to ‎the heating system. ‎Classical ‎controllers for heat ‎cells have ‎difficulties in ‎responding ‎to ‎temperature ‎instability in a timely manner because ‎they do not ‎have the ‎capacity to anticipate ‎the ‎effects ‎of sudden variations in water flow ‎rate. ‎The ‎model ‎predictive control with adaptive function strategy reported ‎the ‎best ‎response ‎in ‎the stabilization of ‎temperature ‎in previous work. Its ‎performance is a ‎result of ‎the ‎predictive nature that allows ‎anticipating and ‎correcting the negative ‎influences ‎of ‎sudden ‎variations in the flow rate in ‎the ‎temperature. The present study aims to employ ‎this ‎strategy a low-computational ‎algorithm that can be embedded in low-cost ‎hardware ‎with ‎the limitation of computational and ‎memory resources. ‎The ‎study’s ‎motivation is to ‎meet ‎the ‎opening of manufacturers by ‎implementing low-cost and optimal-‎performance ‎microcontrollers ‎for ‎water heaters. The algorithm predictions ‎are ‎showing ‎good ‎agreement ‎responses in temperature stabilization.‎

Kaynakça

  • Aliskan, I. (2018). Adaptive model predictive control for wiener nonlinear systems. Iranian Journal of Science and Technology Transactions of Electrical Engineering, 43, 361–377.
  • Astrom, K. J., & Wittenmark, B. (1997). Computer controlled systems: Theory and design (3rd ed.). Prentice Hall. Bourke, G., Bansal, P., & Raine, R. (2014). Performance of gas tankless (instantaneous) water heaters under various international standards. Applied Energy, 131, 468–478.
  • Costa, V., Ferreira, J., & Guilherme, D. (2016). Modeling and simulation of tankless gas water heaters to reduce temperature overshoots and undershoots. 12th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics (HEFAT 2016), 1404–1409.
  • Ehtiwesh, I. A. S., & Durović, Ž. (2009). Comparative analysis of different control strategies for electro-hydraulic servo systems. World Academy of Science, Engineering and Technology, 32(8), 906–909.
  • Ehtiwesh, I. A. S., Quintã, A. F., & Ferreira, J. A. F. (2021). Predictive control strategies for optimizing temperature stability in instantaneous hot water systems. Science and Technology for the Built Environment, 27(5), 679–690.
  • Haissig, C. M., & Woessner, M. (2000). Adaptive fuzzy algorithm for domestic hot water temperature control of a combi-boiler. HVAC&R Research, 6(2), 117–134.
  • Henze, G. P., Yuill, D. P., & Coward, A. H. (2009). Development of a model predictive controller for tankless water heaters. HVAC & R Research, 15(1), 3–23.
  • Laurencio-Molina, J.C. & Salazar-Garcia, C., 2018. Design of an artificial neural network controller for a tankless water heater by using a low-profile embedded system. In J. L. Crespo-Mariño (Eds.), 2018 IEEE International work conference on bioinspired intelligence, IWOBI 2018 – Proceedings (pp. 1–8). IEEE.
  • Li, P., Vrabie, D., Li, D., Sorin, C., Bengea, S., O’Neill, Z. D., & Mijanovic, S. (2015). Simulation and experimental demonstration of model predictive control in a building HVAC system. Science and Technology for the Built Environment, 21(6), 721–732.
  • MathWorks. (2021). fmincon function. Available from: https://www.mathworks.com/help/optim/ug/ fmincon.html#:~:text=example-,x %3D fmincon(fun %2C x0 %2C A %2C b %2C,lb ≤ x ≤ ub .&text=If x(i) is unbounded,ub(i) %3D Inf.
  • Quintã, A. F., Ehtiwesh, I. A. S., Martins, N., & Ferreira, J. A. F. (2022). Gain scheduling model predictive controller design for tankless gas water heaters with time-varying delay. Applied Thermal Engineering, 213, Article 118669.
  • Quinta, A. F., Oliveira, J. D., Ferreira, J. A. F., Costa, V. A. F., & Martins, N. (2022). Virtual test bench for the design of control strategies for water heaters. Journal of Thermal Science and Engineering Applications, 14(5), 1–11.
  • Santos, T. L. M., Limon, D., Normey-Rico, J. E., & Alamo, T. (2012). On the explicit dead-time compensation for robust model predictive control. Journal of Process Control, 22(1), 236–246.
  • Takács, B., Števek, J., Valo, R., & Kvasnica, M. (2016). Python code generation for explicit MPC in MPT. In: 2016 European control conference (ECC) (pp. 1328–1333). IEEE.
  • Wang, L., Zang, H., & Ning, Y. (2011). The gas water heater control system design based on fuzzy control. 2011 International conference on electric information and control engineering, ICEICE 2011 - Proceedings (pp. 840–843). IEEE.
  • Xu, K., Qiu, X., Li, X., & Xu, Y. (2008). A dynamic neuro-fuzzy controller for gas-fired water heater. In M. Guo (Ed.), Proceedings - 4th International Conference on Natural Computation (pp. 240–244). IEEE.
  • Yuill, D. P., Coward, A. H., & Henze, G. P. (2010). Performance comparison of control methods for tankless water heaters. HVAC & R Research, 16(5), 677–690.
Yıl 2023, Cilt: 3 Sayı: 1, 19 - 24, 22.06.2023
https://doi.org/10.14744/seatific.2023.0003

Öz

Kaynakça

  • Aliskan, I. (2018). Adaptive model predictive control for wiener nonlinear systems. Iranian Journal of Science and Technology Transactions of Electrical Engineering, 43, 361–377.
  • Astrom, K. J., & Wittenmark, B. (1997). Computer controlled systems: Theory and design (3rd ed.). Prentice Hall. Bourke, G., Bansal, P., & Raine, R. (2014). Performance of gas tankless (instantaneous) water heaters under various international standards. Applied Energy, 131, 468–478.
  • Costa, V., Ferreira, J., & Guilherme, D. (2016). Modeling and simulation of tankless gas water heaters to reduce temperature overshoots and undershoots. 12th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics (HEFAT 2016), 1404–1409.
  • Ehtiwesh, I. A. S., & Durović, Ž. (2009). Comparative analysis of different control strategies for electro-hydraulic servo systems. World Academy of Science, Engineering and Technology, 32(8), 906–909.
  • Ehtiwesh, I. A. S., Quintã, A. F., & Ferreira, J. A. F. (2021). Predictive control strategies for optimizing temperature stability in instantaneous hot water systems. Science and Technology for the Built Environment, 27(5), 679–690.
  • Haissig, C. M., & Woessner, M. (2000). Adaptive fuzzy algorithm for domestic hot water temperature control of a combi-boiler. HVAC&R Research, 6(2), 117–134.
  • Henze, G. P., Yuill, D. P., & Coward, A. H. (2009). Development of a model predictive controller for tankless water heaters. HVAC & R Research, 15(1), 3–23.
  • Laurencio-Molina, J.C. & Salazar-Garcia, C., 2018. Design of an artificial neural network controller for a tankless water heater by using a low-profile embedded system. In J. L. Crespo-Mariño (Eds.), 2018 IEEE International work conference on bioinspired intelligence, IWOBI 2018 – Proceedings (pp. 1–8). IEEE.
  • Li, P., Vrabie, D., Li, D., Sorin, C., Bengea, S., O’Neill, Z. D., & Mijanovic, S. (2015). Simulation and experimental demonstration of model predictive control in a building HVAC system. Science and Technology for the Built Environment, 21(6), 721–732.
  • MathWorks. (2021). fmincon function. Available from: https://www.mathworks.com/help/optim/ug/ fmincon.html#:~:text=example-,x %3D fmincon(fun %2C x0 %2C A %2C b %2C,lb ≤ x ≤ ub .&text=If x(i) is unbounded,ub(i) %3D Inf.
  • Quintã, A. F., Ehtiwesh, I. A. S., Martins, N., & Ferreira, J. A. F. (2022). Gain scheduling model predictive controller design for tankless gas water heaters with time-varying delay. Applied Thermal Engineering, 213, Article 118669.
  • Quinta, A. F., Oliveira, J. D., Ferreira, J. A. F., Costa, V. A. F., & Martins, N. (2022). Virtual test bench for the design of control strategies for water heaters. Journal of Thermal Science and Engineering Applications, 14(5), 1–11.
  • Santos, T. L. M., Limon, D., Normey-Rico, J. E., & Alamo, T. (2012). On the explicit dead-time compensation for robust model predictive control. Journal of Process Control, 22(1), 236–246.
  • Takács, B., Števek, J., Valo, R., & Kvasnica, M. (2016). Python code generation for explicit MPC in MPT. In: 2016 European control conference (ECC) (pp. 1328–1333). IEEE.
  • Wang, L., Zang, H., & Ning, Y. (2011). The gas water heater control system design based on fuzzy control. 2011 International conference on electric information and control engineering, ICEICE 2011 - Proceedings (pp. 840–843). IEEE.
  • Xu, K., Qiu, X., Li, X., & Xu, Y. (2008). A dynamic neuro-fuzzy controller for gas-fired water heater. In M. Guo (Ed.), Proceedings - 4th International Conference on Natural Computation (pp. 240–244). IEEE.
  • Yuill, D. P., Coward, A. H., & Henze, G. P. (2010). Performance comparison of control methods for tankless water heaters. HVAC & R Research, 16(5), 677–690.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Ismael EHTİWESH

Yayımlanma Tarihi 22 Haziran 2023
Gönderilme Tarihi 3 Nisan 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 3 Sayı: 1

Kaynak Göster

APA EHTİWESH, I. (2023). Low-‎computational adaptive MPC algorithmization strategy for over ‎and ‎undershoots ‎instantaneous ‎water ‎heaters stability. Seatific Journal, 3(1), 19-24. https://doi.org/10.14744/seatific.2023.0003

Seatific Journal