Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm

Sani I. Abba, Jamilu Usman*, Ismail Abdulazeez, Dahiru U. Lawal*, Nadeem Baig, A. G. Usman, Isam H. Aljundi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The need for reliable, state-of-the-art environmental investigations and pioneering approaches to address pressing ecological dilemmas and to nurture the sustainable development goals (SDGs) cannot be overstated. With the power to revolutionize desalination processes, artificial intelligence (AI) models hold the potential to address global water scarcity challenges and contribute to a more sustainable and resilient future. The realm of desalination has exhibited a mounting inclination toward modeling the efficacy of the hybrid nanofiltration/reverse osmosis (NF–RO) process. In this research, the performance of NF–RO based on permeate conductivity was developed using deep learning long short-term memory (LSTM) integrated with an optimized metaheuristic crow search algorithm (CSA) (LSTM-CSA). Before model development, an uncertainty Monte Carlo simulation was adopted to evaluate the uncertainty attributed to the prediction. The results based on several performance statistical criteria (root mean square error (RMSE) and mean absolute error (MAE)) demonstrated the reliability of both LSTM (RMSE = 0.1971, MAE = 0.2022) and the LSTM-CSA (RMSE = 0.1890, MAE = 0.1420), with the latter achieving the highest accuracy. The accuracy was also evaluated using new 2D graphical visualization, including a cumulative distribution function (CDF) and fan plot to justify the other evaluation indicators such as standard deviation and determination coefficients. The outcomes proved that AI could optimize energy usage, identify energy-saving opportunities, and suggest more sustainable operating strategies. Additionally, AI can aid in developing advanced brine treatment techniques, facilitating the extraction of valuable resources from the brine, thus minimizing waste and maximizing resource utilization.

Original languageEnglish
Article number3515
JournalWater (Switzerland)
Volume15
Issue number19
DOIs
StatePublished - Oct 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • deep learning
  • desalination
  • machine learning
  • permeate conductivity
  • uncertainty

ASJC Scopus subject areas

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology

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