Skip to main content

Advertisement

SpringerLink
Log in

Neural Network Predictive Control of a Vapor Compression Cycle

  • Research Article - Mechanical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

This study investigates the Neural Network Predictive Control of a vapor compression cycle (VCC). VCC consists of four components, namely the compressor, electronic expansion valve (EEV), evaporator and condenser. Modeling of the compressor and EEV is carried out with the static relationships, while modeling of the evaporator and condenser is performed with the lumped parameter moving boundary method. The established thermodynamic model is validated against the ASPEN model with the same design specifications. The neural network is trained off-line with the input and output signal data of the established model. The solution of the optimization problem for the each time step is achieved with the metaheuristic method called Whale Optimization Algorithm in the predictive controller. Ultimately, performances of the four different controllers, namely the cooling load, first law efficiency, entropy generation and second law efficiency, are compared with each other. The results show that the entropy generation controller achieves the most favorable exergy destruction performance with 0.2% lower than the worst performer cooling load controller. It is also observed that the second law efficiency controller is the best performer in terms of the overall second law efficiency through the simulation time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Abbreviations

A :

Area (m2), aperture (%)

c p :

Specific heat (J/kg K)

C :

Discharge coefficient (–)

COP:

Coefficient of performance

h :

Enthalpy (J/kg)

k :

Ratio of the specific heats (–)

L :

Length (m)

\( \dot{m} \) :

Mass flow rate (kg/s)

m :

Mass (kg)

N :

Horizon (–)

P :

Pressure (Pa)

\( \dot{Q} \) :

Heat flux (W)

J :

Objective function (–)

s :

Specific entropy (J/kg K)

S :

Entropy (J/K)

T :

Temperature (K)

u :

System input (–)

V :

Volume (m3)

\( \dot{W} \) :

Power, rate of work transfer (W)

\( \dot{X} \) :

Exergy destruction (W)

y :

State variable (–)

α :

Convective heat transfer coefficient (W/m2 K)

β :

Objective weight (–)

δ :

Objective weight (–)

η :

Efficiency (–)

γ :

Mean void fraction (–)

ρ :

Density (kg/m3)

ω :

Rotational speed (RPM)

0:

Ambient

1:

Control volume 1

12:

Between control volumes 1 and 2

2:

Control volume 2

23:

Between control volumes 2 and 3

3:

Control volume 3

achieved:

Achieved

c:

Condenser

cs:

Condenser secondary fluid

cool:

Cooling load

d:

Destruction

desired:

Desired

e:

Evaporator

entgen:

Entropy generation

es:

Evaporator secondary fluid

firstlaw:

First law efficiency

gen:

Generation

H:

Hot reservoir

i:

Inlet

int:

Interface

is:

Isentropic

k:

Compressor

l:

Liquid phase

L:

Cold reservoir

max:

Maximum

min:

Minimum

o:

Outlet

p:

Prediction

r:

Refrigerant

ref:

Reference

rev:

Reversible

seceff:

Second law efficiency

tot, total:

Total

u:

Control input

v:

Electronic expansion valve

vap:

Vaporized phase

VCS:

Vapor compression system

wall:

Wall

II:

Second law of thermodynamics

References

  1. Perez-Lombard, L.; Ortiz, J.; Pout, C.: A review on buildings energy consumption information. Energy Build. 40, 394–398 (2008)

    Article  Google Scholar 

  2. Hepbasli, A.: Thermodynamic analysis of a ground-source heat pump system for district heating. Int. J. Energy Res. 29, 671–687 (2005)

    Article  Google Scholar 

  3. Bayrakçı, H.C.; Özgür, A.E.: Energy and exergy analysis of vapor compression refrigeration system using pure hydrocarbon refrigerants. Int. J. Energy Res. 33, 1070–1075 (2009)

    Article  Google Scholar 

  4. Ahamed, J.U.; Saidur, R.; Masjuki, H.H.: Thermodynamic performance analysis of R-600 and R-600a as refrigerant. Eng. e-Trans. 5, 11–18 (2010)

    Google Scholar 

  5. Arora, A.; Kaushik, S.C.: Theoretical analysis of a vapour compression refrigeration system with R502, R404A and R507A. Int. J. Refrig. 31, 998–1005 (2008)

    Article  Google Scholar 

  6. Wright, J.A.; Hanby, V.I.: The formulation, characteristics and solution of HVAC system optimized design problems. ASHRAE Trans. 93, 2133–2145 (1987)

    Google Scholar 

  7. Wright, J.A.: HVAC optimisation studies: sizing by genetic algorithm. Build. Serv. Eng. Res. Technol. 17, 7–14 (1996)

    Article  Google Scholar 

  8. Fong, K.F.; Hanby, V.I.; Chow, T.T.: System optimization for HVAC energy management using the robust evolutionary algorithm. Appl. Therm. Eng. 29, 2327–2334 (2009)

    Article  Google Scholar 

  9. Sayyaadi, H.; Nejatolahi, M.: Multi-objective optimization of a cooling tower assisted vapor compression refrigeration system. Int. J. Refrig. 34, 243–256 (2011)

    Article  Google Scholar 

  10. Jain, V.; Sachdeva, G.; Kachhwaha, S.S.; Patel, B.: Thermo-economic and environmental analyses based multi-objective optimization of vapor compression-absorption cascaded refrigeration system using NSGA-II technique. Energy Convers. Manag. 113, 230–242 (2016)

    Article  Google Scholar 

  11. Afram, A.; Janabi-Sharifi, F.: Theory and applications of HVAC control systems: a review of model predictive control (MPC). Build. Environ. 72, 343–355 (2014)

    Article  Google Scholar 

  12. Jain, N.; Alleyne, A.: Exergy-based optimal control of a vapor compression system. Energy Convers. Manag. 92, 353–365 (2015)

    Article  Google Scholar 

  13. Yin, X.; Wang, X.; Li, S.; Cai, W.: Energy-efficiency-oriented cascade control for vapor compression refrigeration cycle systems. Energy 116, 1006–1019 (2016)

    Article  Google Scholar 

  14. Mirjalili, S.; Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  15. Rasmussen, B.P.: Dynamic modeling for vapor compression systems: part I—literature review. HVAC&R Res. 18, 934–955 (2012)

    Google Scholar 

  16. Rasmussen, B.P.; Shenoy, B.: Dynamic modeling for vapor compression systems: part II—simulation tutorial. HVAC&R Res. 18, 956–973 (2012)

    Google Scholar 

  17. McKinley, T.L.; Alleyne, A.G.: An advanced nonlinear switched heat exchanger model for vapor compression cycles using the moving-boundary method. Int. J. Refrig. 31, 1253–1264 (2008)

    Article  Google Scholar 

  18. Li, B.; Alleyne, A.G.: A dynamic model of a vapor compression cycle with shut-down and start-up operations. Int. J. Refrig. 33, 538–552 (2010)

    Article  Google Scholar 

  19. Jain, N.; Alleyne, A.G.: Transient exergy destruction analysis for a vapor compression system. In: International Refrigeration and Air Conditioning Conference, West Lafayette, USA (2014)

  20. Bell, I.H.; Wronski, J.; Quoilin, S.; Lemort, V.: Pure and pseudo-pure fluid thermophysical property evaluation and the open-source thermophysical property library coolprop. Ind. Eng. Chem. Res. 53, 2498–2508 (2014)

    Article  Google Scholar 

  21. Rasmussen, B.P.; Alleyne, A.G.: Dynamic modeling and advanced control of air conditioning and refrigeration systems. University of Illinois at Urbana-Champaign ACRC Technical Report, ACRC TR-244 (2006)

  22. Eldredge, B.: Improving the accuracy and scope of control-oriented vapor compression cycle system models. Master’s thesis, University of Illinois at Urbana-Champaign (2006)

  23. Jain, N.: Thermodynamics-based optimization and control of integrated energy systems. Ph.D. thesis, University of Illinois at Urbana-Champaign (2006)

  24. Aminyavari, M.; Najafi, B.; Shirazi, A.; Rinaldi, F.: Exergetic, economic and environmental (3E) analyses, and multi-objective optimization of a CO2/NH3 cascade refrigeration system. Appl. Therm. Eng. 65, 42–50 (2014)

    Article  Google Scholar 

  25. Moran, M.J.; Shapiro, H.N.; Boettner, D.D.; Bailey, M.B.: Fundamentals of Engineering Thermodynamics, 8th edn. Wiley, Hoboken (2014)

    Google Scholar 

  26. Shah, M.M.: A general correlation for heat transfer during film condensation in pipes. Int. J. Heat. Mass. Transf. 22, 547–556 (1979)

    Article  Google Scholar 

  27. Fauske, H.K.: Some ideas about the mechanism causing two-phase critical flow. Appl. Sci. Res. 13, 149–160 (1964)

    Article  Google Scholar 

  28. Gungor, K.E.; Winterton, R.H.S.: A general correlation for flow boiling in tubes and annuli. Int. J. Heat. Mass. Transf. 29, 351–358 (1985)

    Article  Google Scholar 

  29. Aspen Plus, Aspen Technology Inc., USA (2013)

  30. Lawrynczuk, M.: Neural networks in model predictive control. In: Nguyen, N.T., Szczerbicki, E. (eds.) Intelligent System for Knowledge Management, pp. 31–63. Springer, New York (2009)

    Chapter  Google Scholar 

  31. Grüne, L.; Pannek, J.: Nonlinear Model Predictive Control: Theory and Algorithms, 1st edn. Springer, London (2011)

    Book  Google Scholar 

  32. Hornik, K.; Stinchcombe, M.; White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989)

    Article  Google Scholar 

  33. Vasickaninova, A.; Bakosova, M.; Meszaros, A.; Klemes, J.J.: Neural network predictive control of a heat exchanger. Appl. Therm. Eng. 31, 2094–2100 (2011)

    Article  Google Scholar 

  34. Vasickaninova, A.; Bakosova, M.: Control of a heat exchanger using neural network predictive controller combined with auxiliary fuzzy controller. Appl. Therm. Eng. 89, 1046–1053 (2015)

    Article  Google Scholar 

  35. Afram, A.; Janabi-Sharifi, F.; Fung, A.S.; Raahemifar, K.: Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: a state of the art review and case study of a residential HVAC system. Energy Build. 141, 96–113 (2017)

    Article  Google Scholar 

  36. Eberhart, R.; Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan (1995)

  37. Storn, R.; Price, K.: Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  38. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Erciyes University Technical Report TR-06 (2005)

  39. Nair, V.; Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel (2010)

  40. Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G.S.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Goodfellow, I.; Harp, A.; Irving, G.; Isard, M.; Jozefowicz, R.; Jia, Y.; Kaiser, L.; Kudlur, M.; Levenberg, J.; Mané, D.; Schuster, M.; Monga, R.; Moore, S.; Murray, D.; Olah, C.; Shlens, J.; Steiner, B.; Sutskever, I.; Talwar, K.; Tucker, P.; Vanhoucke, V.; Vasudevan, V.; Viégas, F.; Vinyals, O.; Warden, P.; Wattenberg, P.; Wicke, M.; Yu, Y.; Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems. Software. www.tensorflow.org (2015)

  41. Kingma, D.P.; Ba, L.J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR 2015), San Diego, USA (2015)

  42. Kotas, T.J.: The Exergy Method of Thermal Plant Analysis, 1st edn. Butterworths, London (1985)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mechanical Engineering Department, Faculty of Engineering, Ege University, 35040, Bornova, Izmir, Turkey

    Mert Sinan Turgut & Mustafa Turhan Çoban

Authors
  1. Mert Sinan Turgut
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mustafa Turhan Çoban
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mert Sinan Turgut.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Turgut, M.S., Çoban, M.T. Neural Network Predictive Control of a Vapor Compression Cycle. Arab J Sci Eng 45, 779–796 (2020). https://doi.org/10.1007/s13369-019-04149-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-019-04149-2

Keywords

Search

Navigation