The roles of artificial intelligence techniques for increasing the prediction performance of important parameters and their optimization in membrane processes: A systematic review

Shuai Yuan; Hussein Ajam; Zainab Ali Bu Sinnah; Farag M A Altalbawy; Sabah Auda Abdul Ameer; Ahmed Husain; Zuhair I Al Mashhadani; Ahmed Alkhayyat; Ali Alsalamy; Riham Ali Zubaid; Yan Cao

doi:10.1016/j.ecoenv.2023.115066

The roles of artificial intelligence techniques for increasing the prediction performance of important parameters and their optimization in membrane processes: A systematic review

Ecotoxicol Environ Saf. 2023 Jul 15:260:115066. doi: 10.1016/j.ecoenv.2023.115066. Epub 2023 May 30.

Authors

Affiliations

¹ Information Engineering College, Yantai Institute of Technology, Yantai, Shandong 264005, China. Electronic address: ytlgys@126.com.
² Department of Intelligent Medical Systems, Al Mustaqbal University College, Babylon 51001, Iraq.
³ Mathematics Department, University Colleges at Nairiyah, University of Hafr Al Batin, Saudi Arabia.
⁴ National Institute of Laser Enhanced Sciences (NILES), University of Cairo, Giza 12613, Egypt; Department of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia.
⁵ Ahl Al Bayt University, Kerbala, Iraq.
⁶ Department of Medical Instrumentation, Al-farahidi University, Baghdad, Iraq.
⁷ Al-Nisour University College, Baghdad, Iraq.
⁸ Scientific Research Centre of the Islamic University, The Islamic University, Najaf, Iraq.
⁹ College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, Iraq.
¹⁰ Mazaya University College, Iraq.
¹¹ School of Computer Science and Engineering, Xi'an Technological University, Xi'an 710021, China.

PMID: 37262969
DOI: 10.1016/j.ecoenv.2023.115066

Abstract

Membrane-based separation processes has been recently of significant global interest compared to other conventional separation approaches due to possessing undeniable advantages like superior performance, environmentally-benign nature and simplicity of application. Computational simulation of fluids has shown its undeniable role in modeling and simulation of numerous physical/chemical phenomena including chemical engineering, chemical reaction, aerodynamics, drug delivery and plasma physics. Definition of fluids can be occurred using the Navier-Stokes equations, but solving the equations remains an important challenge. In membrane-based separation processes, true perception of fluid's manner through disparate membrane modules is an important concern, which has been significantly limited applying numerical/computational procedures such s computational fluid dynamics (CFD). Despite this noteworthy advantage, the optimization of membrane processes using CFD is time-consuming and expensive. Therefore, combination of artificial intelligence (AI) and CFD can result in the creation of a promising hybrid model to accurately predict the model results and appropriately optimize membrane processes and phase separation. This paper aims to provide a comprehensive overview about the advantages of commonly-employed ML-based techniques in combination with the CFD to intelligently increase the optimization accuracy and predict mass transfer and the unfavorable events (i.e., fouling) in various membrane processes. To reach this objective, four principal strategies of AI including SL, USL, SSL and ANN were explained and their advantages/disadvantages were discussed. Then after, prevalent ML-based algorithm for membrane-based separation processes. Finally, the application potential of AI techniques in different membrane processes (i.e., fouling control, desalination and wastewater treatment) were presented.

Keywords: Artificial intelligence, CFD; Membrane processes; Model prediction; Optimization.

Publication types

Systematic Review
Review

MeSH terms

Algorithms
Artificial Intelligence*
Computer Simulation
Hydrodynamics
Water Purification* / methods