Performance enhancement of a solar-driven DCMD system using an air-cooled condenser and oil: Experimental and machine learning investigations

Aug 5, 2026

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Solar-driven direct contact membrane distillation (DCMD) systems face limited freshwater productivity and low gain-output-ratio (GOR), reducing overall viability. This work targets improved system performance and accurate, practical modeling through machine learning. A novel solar DCMD setup integrates oil-filled heat pipe evacuated tube collectors and an air-cooled condenser, evaluated across eight energy-and-economics scenarios. ANN, SVR, and RF models are tested for prediction of permeate flux and GOR, showing substantial efficiency and cost gains.

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Edith Cowan University Research Online Research outputs 2022 to 2026 4-6-2024 Performance enhancement of a solar-driven DCMD system using an air-cooled condenser and oil: Experimental and machine learning investigations Pooria Behnam Edith Cowan University Abdellah Shafieian Edith Cowan University Masoumeh Zargar Edith Cowan University Mehdi Khiadani Edith Cowan University Follow this and additional works at: [https://ro.ecu.edu.au/ecuworks2022-2026](https://ro.ecu.edu.au/ecuworks2022-2026) Part of the Civil and Environmental Engineering Commons 10.1016/j.desal.2023.117255 Behnam, P., Shafieian, A., Zargar, M., & Khiadani, M. (2024) . Performance enhancement of a solar-driven DCMD system using an air-cooled condenser and oil: Experimental and machine learning investigations. Desalination, 574, article 117255. [https://doi.org/10.1016/j.desal.2023.117255](https://doi.org/10.1016/j.desal.2023.117255) [This Journal Article is posted at Research Online.](This Journal Article is posted at Research Online.)[ ](This Journal Article is posted at Research Online.)[https://ro.ecu.edu.au/ecuworks2022-2026/3632](https://ro.ecu.edu.au/ecuworks2022-2026/3632) Desalination 574 (2024) 117255 Contents lists available at ScienceDirect Desalination journal [homepage:](homepage: www.elsevier.com/locate/desal)[ www.elsevier.com/locate/desal](homepage: www.elsevier.com/locate/desal) | Performance enhancement of a solar-driven DCMD system using an air-cooled condenser and oil: Experimental and machine
learning investigations | | | | | | | --- | --- | --- | --- | --- | --- | | Pooria Behnam , Abdellah Shafieian , Masoumeh Zargar , Mehdi
School of Engineering, Edith Cowan University, 270 Joondalup Drive, Joondalup, Perth, WA 6027, Australia | | | Khiadani | * | | | H I G H L I G H T S | | | | | | | • A novel solar DCMD with oil-filled collectors and an air-cooled condenser was proposed.
• Machine learning models were used to analyze the performance of the improved system.
• Grid search optimization and K-fold cross-validation methods were coupled.
• Freshwater productivity and GOR increased by almost 37 % and 31 %, respectively.
• The ANN and SVR models provided high accuracy in under two seconds of computation. | | | | | | | A R T I C L E I N F O | | A B S T R A C T | | | | | Keywords:
Direct contact membrane distillation Solar desalination
Performance improvement Machine learning | | Solar-driven direct contact membrane distillation systems (DCMD) are disadvantaged by low freshwater productivity and low gain-output-ratio (GOR). Consequently, this study aims to achieve two primary objectives: i) improving the solar DCMD performance, and ii) harnessing machine learning models for precise and straightforward modeling of the solar DCMD system. To achieve these goals, a novel solar DCMD system powered with oil-filled heat pipe evacuated tube collectors (HP-ETCs) and equipped with an air-cooled condenser was used for the first time. The system was evaluated under eight different scenarios covering both its energy and economic performances. The performance prediction of three different machine learning models including ANN, SVR and RF was assessed for the proposed system. The results showed that integrating an air-cooled condenser and oilfilled HP-ETCs into the solar DCMD system significantly improved the performance and reduced freshwater cost, resulting in: a 35.39–37 % increase in freshwater productivity; a 30.64–31.57 % enhancement in GOR; a 35–38 % rise in daily efficiency; and a 20 % decrease in freshwater cost. The results demonstrate that ANN and SVR have excellent performance for modeling the solar-driven DCMD system, achieving MAPE test values of approximately 1 % and 4 % for predicting permeate flux and GOR, respectively. | | | | 1. Introduction Desalination technologies can fulfil a vital role in mitigating water shortage issue worldwide. Among convent

FAQs

What problem does the study address in solar-driven DCMD systems?

Solar-driven DCMD systems are disadvantaged by low freshwater productivity and low gain-output-ratio (GOR), limiting efficiency and cost effectiveness.

What key system components are introduced for performance enhancement?

The proposed system uses oil-filled heat pipe evacuated tube collectors and an air-cooled condenser integrated into a solar-driven DCMD configuration.

Which machine learning methods are used to model the system performance?

The study evaluates ANN, SVR, and RF models for predicting permeate flux and GOR, with ANN and SVR showing strong accuracy.

Aug 5, 2026

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