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Experimental Investigation and Neural network based parametric prediction in a multistage reciprocating humidifier

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  • Salins, Sampath Suranjan
  • Kota Reddy, S.V.
  • Shiva Kumar,

Abstract

Cooling of the buildings is very much mandatory in summer and to meet this, considerable energy will be spent for cooling purpose across the world. Present work focuses on the multistage evaporative cooling pads where four different packing are used to analyze the different humidification output parameters. Cam shaft which is powered by the motor gives reciprocating motion to the cooling pads which is made to dip inside the stagnant water. Input operating parameters such as air velocity, cam shaft speed and the number of cooling pads are varied and the output parameters like pressure drop, cooling effect, coefficient of performance, relative humidity drop and energy consumption rate are determined. Results indicated that, there is an increase in COP, pressure drop and the energy consumption rate with the rise in the air velocity. Artificial neural network has been used for predicting the performance parameters of the experimental results. 3-15-4 structured MLP based network is considered and is trained by using trainscg, trainlm and using trainbr networks. Results indicated a good prediction capability of ANN techniques with MRE of test data lying below 12%. Trainbr outperformed the other two networks as the correlation coefficient was much higher and MRE was lower for both training as well as test data.

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  • Salins, Sampath Suranjan & Kota Reddy, S.V. & Shiva Kumar,, 2021. "Experimental Investigation and Neural network based parametric prediction in a multistage reciprocating humidifier," Applied Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:appene:v:293:y:2021:i:c:s0306261921004347
    DOI: 10.1016/j.apenergy.2021.116958
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    References listed on IDEAS

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    1. Yang, Yifan & Cui, Gary & Lan, Christopher Q., 2019. "Developments in evaporative cooling and enhanced evaporative cooling - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    2. Li, Yang & Huang, Xin & Peng, Hao & Ling, Xiang & Tu, ShanDong, 2018. "Simulation and optimization of humidification-dehumidification evaporation system," Energy, Elsevier, vol. 145(C), pages 128-140.
    3. Antonio Franco & Diego L. Valera & Araceli Peña, 2014. "Energy Efficiency in Greenhouse Evaporative Cooling Techniques: Cooling Boxes versus Cellulose Pads," Energies, MDPI, vol. 7(3), pages 1-21, March.
    4. Pandelidis, Demis & Anisimov, Sergey & Rajski, Krzysztof & Brychcy, Ewa & Sidorczyk, Marek, 2017. "Performance comparison of the advanced indirect evaporative air coolers," Energy, Elsevier, vol. 135(C), pages 138-152.
    5. Scapino, Luca & Zondag, Herbert A. & Diriken, Jan & Rindt, Camilo C.M. & Van Bael, Johan & Sciacovelli, Adriano, 2019. "Modeling the performance of a sorption thermal energy storage reactor using artificial neural networks," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
    7. Babu, D. & Thangarasu, Vinoth & Ramanathan, Anand, 2020. "Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel," Applied Energy, Elsevier, vol. 263(C).
    8. Kabeel, A.E. & Hamed, Mofreh H. & Omara, Z.M. & Sharshir, S.W., 2014. "Experimental study of a humidification-dehumidification solar technique by natural and forced air circulation," Energy, Elsevier, vol. 68(C), pages 218-228.
    9. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
    10. Shivakumar & Srinivasa Pai, P. & Shrinivasa Rao, B.R., 2011. "Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings," Applied Energy, Elsevier, vol. 88(7), pages 2344-2354, July.
    11. Mujahid Rafique, M. & Gandhidasan, P. & Rehman, Shafiqur & Al-Hadhrami, Luai M., 2015. "A review on desiccant based evaporative cooling systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 145-159.
    12. Abohorlu Doğramacı, Pervin & Riffat, Saffa & Gan, Guohui & Aydın, Devrim, 2019. "Experimental study of the potential of eucalyptus fibres for evaporative cooling," Renewable Energy, Elsevier, vol. 131(C), pages 250-260.
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    Cited by:

    1. Huang, Xin & Chen, Hu & Ling, Xiang & Liu, Lin & Huhe, Taoli, 2022. "Investigation of heat and mass transfer and gas–liquid thermodynamic process paths in a humidifier," Energy, Elsevier, vol. 261(PA).
    2. Salins, Sampath Suranjan & Reddy, S.V. Kota & Kumar, Shiva, 2022. "Modelling of a multistage reciprocating humidifier and performance analysis for various packing configurations," Energy, Elsevier, vol. 241(C).
    3. Yan, Weichao & Meng, Xiangzhao & Cui, Xin & Liu, Yilin & Chen, Qian & Jin, Liwen, 2022. "Evaporative cooling performance prediction and multi-objective optimization for hollow fiber membrane module using response surface methodology," Applied Energy, Elsevier, vol. 325(C).
    4. Cui, Xin & Yang, Chuanjun & Yan, Weichao & Zhang, Lianying & Wan, Yangda & Chua, Kian Jon, 2023. "Experimental study on a moisture-conducting fiber-assisted tubular indirect evaporative cooler," Energy, Elsevier, vol. 278(PB).
    5. Yan, Weichao & Cui, Xin & Meng, Xiangzhao & Yang, Chuanjun & Liu, Yilin & An, Hui & Jin, Liwen, 2023. "Effects of membrane characteristics on the evaporative cooling performance for hollow fiber membrane modules," Energy, Elsevier, vol. 270(C).

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