The membrane processes include the complex frameworks, typically integrating various physio-chemical aspects, and the biological activities, based on the systems researched. In that regard, the process modeling is essential to predict and simulate the process and the performance of membranes, to infer concerning the optimum process aspects, meant to analyze fouling developments, and principally, the controls and monitoring of processes. Irrespective of the real terminological dissemination such as Machine Learning (ML), the application of computing instruments to the processes of model membrane was considered in the past are insignificant from the scholarly perspective, not contributing to our knowledge of the aspects included. Irrespective of the controversies, in the past two decades, non-mechanistic and data-driven modeling is applicable to illustrate various membrane process, and in the establishment of novel tracking and modeling approaches. In that regard, this paper concentrates on the provision of a custom aspect regarding the use of Non-Mechanistic Modeling (NMM) in membrane processing, assessing the transformations endorsed by our experience, accomplished as a research segment operational in the membrane process segment. Furthermore, the guidelines are the problems for the application of the state-of-the-art computational instruments Membrane Computing (MC).
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Agnar Alfons Ramel
Agnar Alfons Ramel
Faculty of Physical Sciences Mathematics, University of Iceland, Reykjavík, Iceland.
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Agnar Alfons Ramel, “Analysis of Membrane Process Model from Black Box to Machine Learning”, Journal of Machine and Computing, vol.2, no.1, pp. 001-008, January 2022. doi: 10.53759/7669/jmc202202001.
