An expert system-based modeling and optimization of corrugated plate solar air collector for North Eastern India

  • Suman Debnath
  • Jagannath Reddy
  • Jagadish
  • Biplab DasEmail author
Technical Paper


The present study introduces a fuzzy logic-based expert system (FLES) for evaluating the thermal performance of corrugated plate solar air collector (CPSAC) under different climatic conditions of North Eastern India. The FLES model consists of subtractive clustering with Takagi–Sugeno–Kang fuzzy logic (TSK-FL) model. Work considered mass flow rate (m) 0.0039–0.018 kg/s, collector tilt angles (θ) 30–60°, solar radiation (Q) 230–1086 W/m2, ambient temperature (T) 21–34 °C as input and efficiency (η), exergetic efficiency (\(\eta_{II}\)), temperature rise (∆T), and pressure drop (∆P) as output parameters for the study. First, 270 trails of experimentation have been carried out on CPSAC by varying the input parameter to get a historical database. Second, modeling of the historical data and optimization of CPSAC parameters have been performed. Third, the parametric analysis is also performed to study the effect of parameters on the thermal performance of CPSAC. Parametric results reveal that η increases with m, while up to a certain value of θ, Q, T. At last, the effectiveness and accuracy of the model is judged via various validation tests with experimental data, published data, and artificially generated data. It is observed that FLES model predicts accuracy results with an accuracy of ≈ 97.5% and optimal conditions are at m = 0.00785 kg/s, θ = 45°, Q = 727 W/m2, and T = 29.6 °C, and the corresponding outputs are η = 35.9%, \(\eta_{II}\) = 12.8%, ∆T = 34.7 °C and ∆P = 48.8 Pa.


Corrugated plate solar air collector (CPSAC) Energy analysis Exergy analysis Fuzzy logic-based expert system (FLES) Optimization Parametric analysis 

List of symbols


Surface area of a collector, m2


Specific heat of air at constant pressure, kJ/kg°C


Solar intensity, W/m2


Length of a collector, m

\(\mathop m\limits^{.}\)

A mass flow rate of air, kg/s


Fluid pressure, N/m2 or Pa


Energy incident on collector area, W


Specific gas constant, kJ/kg-K


Temperature, °C


Width of a collector, m


Work rate, (Watt) W

Greek letters

\(\dot{\varepsilon }\)

Energy rate, kW

\(\mathop {\varepsilon x}\limits^{.}\)

Exergy rate, kW

\(\mathop {\dot{\varepsilon}}x_{\text{p}}\)

Exergy considering pressure drop, kW

\(\dot{\varepsilon }x_{\text{dest}}\)

Exergy destruction or rate of irreversibility, kW


Energy efficiency, %


Thermo-hydraulic efficiency, %


Exergy efficiency, %






Surface of the absorber plate




Reference properties (Environment)







One of the authors sincerely acknowledges the fund received under a BASE fellowship from IUSSTF. Authors also sincerely acknowledge the fund received from DST, Govt. of India, for development of solar thermal laboratory facility at NIT Silchar, Assam, India under project NRDMS/SC/ST/15/016(c) dated 17/01/2017.


  1. 1.
    Öztürk H, Demirel Y (2004) Exergy-based performance analysis of packed-bed solar air heaters. Int J Energy Resour 28:423–432CrossRefGoogle Scholar
  2. 2.
    Gupta MKA, Kaushik SC (2008) Exergetic performance evaluation and parametric studies of solar air heater. Energy 33:1691–1702CrossRefGoogle Scholar
  3. 3.
    Esen H (2008) Experimental energy and exergy analysis of a double-flow solar air heater having different obstacles on absorber plates. Build Environ 43:1046–1054CrossRefGoogle Scholar
  4. 4.
    Alta D, Bilgili E, Ertekin C, Yaldiz O (2010) Experimental investigation of three different solar air heaters: energy and exergy analyses. Appl Energy 87:2953–2973CrossRefGoogle Scholar
  5. 5.
    Saidur R, Boroumandjazi G, Mekhlif S, Jameel M (2012) Exergy analysis of solar energy applications. Renew Sustain Energy Rev 16:350–356CrossRefGoogle Scholar
  6. 6.
    Jafarkazemi F, Ahmadifard E (2013) Energetic and exergetic evaluation of flat plate solar collectors. Renew Energy 56:55–63CrossRefGoogle Scholar
  7. 7.
    Kalaivanan PVR (2015) Energy and exergy analysis of solar air heaters with varied geometries. Arab J Sci Eng 40:1173–1186CrossRefGoogle Scholar
  8. 8.
    Suzuki AA (2016) Fundamental equation for exergy balance on solar collectors. J Solar Energy Eng 110:102–106CrossRefGoogle Scholar
  9. 9.
    Kalogirou SA (2006) Prediction of flat-plate collector performance parameters using artificial neural networks. Sol Energy 80:248–259CrossRefGoogle Scholar
  10. 10.
    Esen H, Ozgen F, Esen M, Sengur A (2009) Artificial neural network and wavelet neural network approaches for modeling of a solar air heater. Expert Syst Appl 36:11240–11248CrossRefGoogle Scholar
  11. 11.
    Caner M, Gedik E, Keçebas A (2011) Investigation on thermal performance calculation of two type solar air collectors using the artificial neural network. Expert Syst Appl 38:1668–1674CrossRefGoogle Scholar
  12. 12.
    Benli H (2013) Determination of thermal performance calculation of two different types of solar air collectors with the use of artificial neural networks. Int J Heat Mass Transf 60:1–7CrossRefGoogle Scholar
  13. 13.
    Ghritlahre HK, Prasad RK (2017) Prediction of thermal performance of unidirectional flow porous bed solar air heater with optimal training function using the artificial neural network. Energy Proced 109:369–376CrossRefGoogle Scholar
  14. 14.
    Kamthania D, Tiwari GN (2012) Performance analysis of a hybrid photovoltaic thermal double pass air collector using ANN. Appl Solar Energy 48:186–192CrossRefGoogle Scholar
  15. 15.
    Varun, Siddhartha (2010) Thermal performance optimization of a flat plate solar air heater using a genetic algorithm. Appl Energy 87:1793–1799CrossRefGoogle Scholar
  16. 16.
    Sharma N, Bhat IK, Grover D (2011) Optimization of a smooth flat plate solar air heater using stochastic iterative perturbation technique. Sol Energy 85:2331–2337CrossRefGoogle Scholar
  17. 17.
    Siddhartha, Sharma N, Varun (2012) A particle swarm optimization algorithm for optimization of the thermal performance of a smooth flat plate solar air heater. Energy 38:406–413CrossRefGoogle Scholar
  18. 18.
    Vafaei LE, Sah M (2017) Predicting the efficiency of the flat-plate solar collector using a fuzzy inference system. Proced Comput Sci 120:221–228CrossRefGoogle Scholar
  19. 19.
    Mohanty S, Rout A, Patra PK, Sahoo SS (2017) ANFIS based solar radiation data forecasting for energy and economic Study of solar water heaters in eastern India. Int J Control Theory Appl 10:179–190Google Scholar
  20. 20.
    Erenturk S, Erenturk K (2018) Comparisons of novel modeling techniques to analyze thermal performance of unglazed transpired solar collectors. Measurement 116:412–421CrossRefGoogle Scholar
  21. 21.
    Caydas U, Hascalik A (2008) A study on surface roughness in abrasive water jet machining process using artificial neural networks and regression analysis method. J Mater Process Technol 202:574–582CrossRefGoogle Scholar
  22. 22.
    Vundavilli PR, Parappagoudar MB, Kodali SP, Benguluri S (2012) Fuzzy logic- based expert system for prediction of depth of cut in abrasive water jet machining process. Knowl Base Syst 27:456–464CrossRefGoogle Scholar
  23. 23.
    Sugeno M, Kang G (1986) Fuzzy modeling and control of multilayer incinerator. Fuzzy Sets System 18:329–346CrossRefGoogle Scholar
  24. 24.
    Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybernet 15:116–132CrossRefGoogle Scholar
  25. 25.
    Haman A, Geogranas ND (2008) Comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of hapto-audio-visual applications. In: IEEE international workshop on haptic audio visual environments and their applications, Ottawa, CanadaGoogle Scholar
  26. 26.
    Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278Google Scholar
  27. 27.
    Sharma SK, Kalamkar VR (2015) Thermo-hydraulic performance analysis of solar air heaters having artificial roughness–a review. Renew Sustain Energy Rev 41:413–435CrossRefGoogle Scholar
  28. 28.
    Bahrehmand D, Ameri M, Gholampour M (2015) Energy and exergy analysis of different solar air collector system with forced convection. Renew Energy 83:1119–1130CrossRefGoogle Scholar
  29. 29.
    Jung H, Kwon WT (2010) Optimization of EDM process for multiple performance characteristics using Taguchi method and grey relational analysis. J Mech Sci Technol 24:1083–1090CrossRefGoogle Scholar
  30. 30.
    Shabgarda MR, Badamchizadehb MA, Ranjbarya G, Amini K (2013) Fuzzy approach to select machining parameters in electrical discharge machining (EDM) and ultrasonic-assisted EDM processes. J Manuf Syst 32:32–39CrossRefGoogle Scholar
  31. 31.
    Ray A (2014) Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA). Int J Adv Manuf Technol 87(2014):1299–1311Google Scholar
  32. 32.
    Bhowmik S, Ray A (2015) Prediction and optimization of process parameters of green composites in AWJM process using response surface methodology. Int J Adv Manuf Technol 87:1359–1370Google Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringNational Institute of Technology SilcharSilcharIndia
  2. 2.Department of Mechanical EngineeringNational Institute of Technology RaipurRaipurIndia

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