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Desalination has become essential in the fight against the world’s water shortage, and vacuum membrane distillation (VMD) is becoming increasingly popular due to its high potential for brackish and seawater desalination and its energy efficiency. Precise estimation of VMD functionality is necessary for both process design and optimization. In this work, we introduce a modeling technique that combines genetic algorithm-based artificial neural network (GA-ANN) methodologies to improve the permeate flux prediction accuracy of the VMD process. Here, four input parameters, feed inlet temperature, vacuum pressure, feed flow rate, and feed salt concentration, have been considered for the modeling. In addition, 38 sets of experimental datasets have been divided into three parts: training (70%), validation (15%), and testing (15%) for the simulation. The results show that the GA-ANN model is highly accurate in predicting the permeate flux of the process. The findings also show that the GA-ANN model works better with minimum error and a high regression coefficient for testing (R = 99.48%) and offers a productive way to maximize the process, which helps produce fresh water that is affordable and sustainable. This work improves our knowledge and provides a viable framework for predicting and optimizing other membrane-based desalination processes.

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Accurate Permeate Flux Prediction of the VMD Desalination Process Using GA-ANN Technique

  • 章节

  • 发表日期：

    2025年

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  • Rajesh Mahadeva,
  • Umesh Kumar,
  • Abhijeet Ghosh,
  • Vivek Patel,
  • Vinay Gupta

摘要

Desalination has become essential in the fight against the world’s water shortage, and vacuum membrane distillation (VMD) is becoming increasingly popular due to its high potential for brackish and seawater desalination and its energy efficiency. Precise estimation of VMD functionality is necessary for both process design and optimization. In this work, we introduce a modeling technique that combines genetic algorithm-based artificial neural network (GA-ANN) methodologies to improve the permeate flux prediction accuracy of the VMD process. Here, four input parameters, feed inlet temperature, vacuum pressure, feed flow rate, and feed salt concentration, have been considered for the modeling. In addition, 38 sets of experimental datasets have been divided into three parts: training (70%), validation (15%), and testing (15%) for the simulation. The results show that the GA-ANN model is highly accurate in predicting the permeate flux of the process. The findings also show that the GA-ANN model works better with minimum error and a high regression coefficient for testing (R = 99.48%) and offers a productive way to maximize the process, which helps produce fresh water that is affordable and sustainable. This work improves our knowledge and provides a viable framework for predicting and optimizing other membrane-based desalination processes.