Integrating machine learning models to enhance the efficiency of a desalination process
Paranjpe, Nikhil Kshirsagar, Shruti Asmatulu, Ramazan
Paranjpe, Nikhil
Kshirsagar, Shruti
Asmatulu, Ramazan
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2025-03-26
Type
Conference paper
Genre
Keywords
Support vector machines,Adaptation models,Salt,Green products,Transfer learning,Desalination,Water quality,Regression tree analysis,Optimization,Overfitting,Water scarcity,Desalination,Superhydrophobic membranes,Machine learning,Support Vector Regression,Decision Tree Regression
Subjects (LCSH)
Citation
N. Paranjpe, S. Kshirsagar and R. Asmatulu, "Integrating Machine Learning Models to Enhance the Efficiency of a Desalination Process," 2025 IEEE Green Technologies Conference (GreenTech), Wichita, KS, USA, 2025, pp. 31-35, doi: 10.1109/GreenTech62170.2025.10977633.
Abstract
Freshwater scarcity is a critical global challenge, with over two billion people lacking access to safe drinking water. Desalination technologies, particularly Air Gap Membrane Distillation (AGMD) offer a sustainable solution by utilizing superhydrophobic membranes to enhance efficiency and reduce environmental impact. This study integrates material science and machine learning (ML) to optimize AGMD systems, employing Ridge Regression (RR), Support Vector Regression (SVR), and Decision Tree Regression (DTR) models to predict important desalination metrics such as desalination water produced and salt rejection rates. The results demonstrate RR's superior performance with a better R-squared and lowest mean square error (MSE), highlighting its robustness for handling multicollinearity. SVR effectively captures nonlinear relationships, while DTR provides interpretability despite its overfitting tendencies. This research advances sustainable desalination by combining inno-vative materials with ML-driven optimization, paving the way for scalable and environmentally friendly water management solutions.
Table of Contents
Description
Date of Conference: 26-28 March 2025
Date Added to IEEE Xplore: 30 April 2025
Conference Location: Wichita, KS, USA
Date Added to IEEE Xplore: 30 April 2025
Conference Location: Wichita, KS, USA
Publisher
IEEE
Journal
2025 IEEE Green Technologies Conference (GreenTech)
Book Title
Series
Digital Collection
Finding Aid URL
Use and Reproduction
Archival Collection
PubMed ID
ISSN
2166-5478
2166-546X
2166-546X