Advancing Sustainable Wastewater Treatment Using Enhanced Membrane Oil Flux and Separation Efficiency through Experimental-Based Chemometric Learning

Jamilu Usman, Sani I. Abba*, Ibrahim Muhammed, Ismail Abdulazeez, Dahiru U. Lawal*, Lukka Thuyavan Yogarathinam, Abdullah Bafaqeer, Nadeem Baig, Isam H. Aljundi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Efficient oil–water separation using membranes directly aligns with removing oil pollutants from water sources, promoting water quality. Hence, mitigating environmental harm from oil spills and contamination and fostering ecosystem health for sustainable development. Computational learning, such as artificial intelligence (AI), enhances membrane oil flux and separation efficiency by optimizing process parameters, leading to improved oil–water separation and aligning AI with sustainable environmental protection and resource efficiency solutions. This study employed phase-inversion coupled with sintering to create the ceramic membrane. The Stöber method was adopted to prepare the superhydrophobic silica sol-gel solutions. The data from the mentioned experiment were imposed into regression models, namely, multilinear regression analysis (MLR), support vector regression (SVR), and robust linear regression (RLR), to simulate three different scenarios (oil flux, separation efficiency, and oil flux and separation efficiency). The outcomes were validated and evaluated using several statistical (R2, MSE, R, and RMSE) and graphical visualizations. For oil flux, the results show that the most effective simulation was achieved in SVR-M2 and the statistical criteria for the testing phase were R2 = 0.9847, R = 0.9923, RMSE = 0.0333, and MSE = 0.0011. Similarly, SVR-M2 was superior to other modeling techniques for the separation efficiency in the testing phase (R2 = 0.9945, R = 0.9972, RMSE = 0.0282, MSE = 0.0008). Reliability outcomes promise to revolutionize how we model and optimize membrane-based oil–water separation processes, with implications for various industries seeking sustainable and efficient solutions.

Original languageEnglish
Article number3611
JournalWater (Switzerland)
Volume15
Issue number20
DOIs
StatePublished - Oct 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • artificial intelligence
  • membrane
  • oil–water separation
  • optimization

ASJC Scopus subject areas

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

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