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From Black Box to Machine Learning: A Journey through Membrane Process Modelling.

Claudia F Galinha1, João G Crespo1

  • 1LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal.

Membranes
|August 26, 2021
PubMed
Summary
This summary is machine-generated.

Non-mechanistic modeling, including machine learning, is increasingly vital for understanding and controlling complex membrane processes. This approach aids in predicting performance, optimizing conditions, and managing fouling in membrane systems.

Keywords:
ANNPCAPLSartificial intelligencebig datachemometricsfluorescence excitation-emission matrices (EEM)membrane processesmodellingmultivariate data analysis

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Area of Science:

  • Chemical Engineering
  • Environmental Science
  • Materials Science

Background:

  • Membrane processes involve complex physicochemical and biological phenomena requiring robust modeling for simulation and prediction.
  • Traditional mechanistic models face challenges in capturing the full complexity of these systems.
  • Data-driven, non-mechanistic modeling approaches have emerged as powerful tools despite initial skepticism.

Purpose of the Study:

  • To provide a personal perspective on the application of non-mechanistic modeling in membrane processes.
  • To review the evolution and impact of data-driven modeling in this field over the past 25 years.
  • To offer guidelines for applying advanced mathematical tools to membrane process modeling.

Main Methods:

  • Review of non-mechanistic modeling techniques applied to various membrane processes.
  • Analysis of research experience in data-driven modeling of membrane systems.
  • Discussion of machine learning applications in membrane process simulation and control.

Main Results:

  • Non-mechanistic modeling has proven effective in simulating membrane performance and predicting fouling.
  • These data-driven approaches contribute to process optimization, monitoring, and control.
  • The application of computational tools has evolved significantly, overcoming earlier scientific reservations.

Conclusions:

  • Non-mechanistic modeling is an indispensable tool for advancing the understanding and application of membrane processes.
  • Continued development and application of these methods will enhance process efficiency and control.
  • Guidelines are provided to facilitate the effective use of advanced mathematical modeling in membrane science.