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A data-driven model for a liquid desiccant regenerator equipped with an evacuated tube solar collector: Random forest regression, support vector regression and artificial neural network

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  • Daghigh, Roonak
  • Arshad, Siamand Azizi
  • Ensafjoee, Koosha
  • Hajialigol, Najmeh

Abstract

The application of a solar-assisted liquid desiccant air-conditioning system equipped with evacuated tubes, focusing on the assessment and comparison of various artificial intelligence (AI) models is investigated. Specifically, Support Vector Regression (SVR), Multi-Layer Perceptron Artificial Neural Network (MLP-ANN), and Random Forest Regression (RFR) models are assessed for predicting key performance indicators: mass removal rate, efficiency, and effectiveness. Additionally, different optimizers within the Artificial Neural Network (ANN) framework—such as Adam, Stochastic Gradient Descent (SGD), and RMSprop—are systematically examined and tuned. The study encompasses the selection of the most suitable AI model for each target variable, considering parameters such as ambient temperature, solar radiation, timestamp, airflow rate, and initial solution concentration as influential factors in the modeling process. It is found that the best predictor model for effectiveness is SVR with RBF kernel. For MRR and efficiency, it is MLP-ANN with respectively AdamW and NAdam optimizers. The disparity of prediction of the MRR, efficiency and effectiveness target are respectively 0.72%, 1.07% and 0.5%, on average, indicating a precise prediction. Furthermore, including timestamps as model inputs significantly boosts accuracy, an aspect often neglected in prior research, leading to a noticeable minimum 5% rise in the R2 score.

Suggested Citation

  • Daghigh, Roonak & Arshad, Siamand Azizi & Ensafjoee, Koosha & Hajialigol, Najmeh, 2024. "A data-driven model for a liquid desiccant regenerator equipped with an evacuated tube solar collector: Random forest regression, support vector regression and artificial neural network," Energy, Elsevier, vol. 295(C).
    • Handle: RePEc:eee:energy:v:295:y:2024:i:c:s0360544224007047
    DOI: 10.1016/j.energy.2024.130932
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    References listed on IDEAS

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    1. Gandhidasan, P. & Mohandes, M.A., 2011. "Artificial neural network analysis of liquid desiccant dehumidification system," Energy, Elsevier, vol. 36(2), pages 1180-1186.
    2. Alosaimy, A.S. & Hamed, Ahmed M., 2011. "Theoretical and experimental investigation on the application of solar water heater coupled with air humidifier for regeneration of liquid desiccant," Energy, Elsevier, vol. 36(7), pages 3992-4001.
    3. Ibrahim, Nur Atirah & Wan Alwi, Sharifah Rafidah & Abd Manan, Zainuddin & Mustaffa, Azizul Azri & Kidam, Kamarizan, 2024. "Climate change impact on solar system in Malaysia: Techno-economic analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    4. Giampieri, A. & Ma, Z. & Ling-Chin, J. & Roskilly, A.P. & Smallbone, A.J., 2022. "An overview of solutions for airborne viral transmission reduction related to HVAC systems including liquid desiccant air-scrubbing," Energy, Elsevier, vol. 244(PA).
    5. Chen, Guansheng & Liu, Chongchong & Li, Nanshuo & Li, Feng, 2017. "A study on heat absorbing and vapor generating characteristics of H2O/LiBr mixture in an evacuated tube," Applied Energy, Elsevier, vol. 185(P1), pages 294-299.
    6. Kabeel, A.E., 2010. "Dehumidification and humidification process of desiccant solution by air injection," Energy, Elsevier, vol. 35(12), pages 5192-5201.
    7. Wang, Xinli & Cai, Wenjian & Lu, Jiangang & Sun, Youxian & Zhao, Lei, 2015. "Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm," Energy, Elsevier, vol. 82(C), pages 939-948.
    8. Saeed Salah & Husain R. Alsamamra & Jawad H. Shoqeir, 2022. "Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms," Energies, MDPI, vol. 15(7), pages 1-16, April.
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