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Adaptive particle swarm optimization–based deep neural network for productivity enhancement of solar still

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Abstract

Water is considered one of the most superabundant resources on the earth that covers 75% of the entire earth’s surface, yet numerous countries face problem due to water shortage. Desalination is considered the most efficient process to overcome this rising clean water demand. Solar energy is considered one of the efficient and finest resources to refine brackish water. Therefore, this paper proposes a novel black widow particle swarm optimization–based deep neural network approach to enhance the water productivity from a solar still. The main intension of the proposed BWPSO–based DNN approach is to enhance the performances of DNN by employing BWPSO for optimal water production. Here, the optimal weight of the DNN is determined by utilizing the BWPSO algorithm. The solar still is incorporated with a straight tube and spiral tube solar water collector. In addition to this, the study based on solar still and their experimental analysis is carried out in Coimbatore city located in Tamil Nadu. The evaluation is conducted for various parameters, namely glass temperature, average evaporation temperature, inlet and outlet temperature, water temperature, air temperature, yield, solar intensity, wind velocity, RMSE, MAE, MRE, and ECR, to determine the effectiveness of the system. Also, comparative analysis is made and the evaluation results reveal that the proposed approach outperforms various other approaches.

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Data availability

The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Mechanical Engineering, RVS Technical Campus-Coimbatore, Coimbatore, India

    Winstor Jebakumar Suthagar Durairaj Victor & Dharmalingam Somasundaram

  2. Department of Mechanical Engineering, Jayaraj Annapackiam C.S.I Polytechnic College, Nazereth, India

    Koilraj Gnanadason

Authors
  1. Winstor Jebakumar Suthagar Durairaj Victor
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  2. Dharmalingam Somasundaram
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  3. Koilraj Gnanadason
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Contributions

VSWJ led the conception and design of this study. VSWJ, SD, and MKG were responsible for the data collection and analysis. VSWJ was in charge of interpreting the data and drafting the manuscript. VSWJ, SD, and MKG made revision from critical perspective for important intellectual content. The final version was read and adopted by all the authors.

Corresponding author

Correspondence to Winstor Jebakumar Suthagar Durairaj Victor.

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

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The authors declare no competing interests.

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Responsible Editor: Philippe Garrigues

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Highlights

• Solar energy is the efficient and finest resource to refine saline water.

• Enhancing the water productivity using BWPSO-based DNN approach.

• Training dataset evaluates the performances of the network to determine the fitness function.

• The solar still is incorporated with a straight tube and spiral tube solar water collector.

• Utilizing various performance measures to determine the effectiveness of the system.

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Victor, W.J.S.D., Somasundaram, D. & Gnanadason, K. Adaptive particle swarm optimization–based deep neural network for productivity enhancement of solar still. Environ Sci Pollut Res 29, 24802–24815 (2022). https://doi.org/10.1007/s11356-021-16840-9

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  • DOI: https://doi.org/10.1007/s11356-021-16840-9

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