Document Type

Journal Article

Publication Title

Desalination

Volume

574

Publisher

Elsevier

School

School of Engineering

RAS ID

64673

Comments

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

Abstract

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 oil-filled 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 MAPEtest values of approximately 1 % and 4 % for predicting permeate flux and GOR, respectively.

DOI

10.1016/j.desal.2023.117255

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Share

 
COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.