Suh, Changwon Choi, Byeonggyu Lee, Seockheon Kim, Dooil Cho, Jinwoo 2024-01-20T16:03:24Z 2024-01-20T16:03:24Z 2021-09-05 2011-10-17 0011-9164 https://pubs.kist.re.kr/handle/201004/129880 A new approach utilizing silica fluorescent nanoparticle as a surrogate for checking the integrity of microfiltration membrane was proposed and well applied in a previous study, but the absence of a feasible estimation model for the degree of membrane damage caused that this simple membrane integrity test was not applied easily. This study proposes genetic programming (GP) as an alternative approach to develop the model to predict the area of membrane breach with other experimental conditions (concentration of fluorescent nanoparticle, the permeate water flux and transmembrane pressure). Unlike the artificial neural network that is the most common artificial intelligence technique. GP is an inductive data-driven machine learning that evolves an explicit equation with known experimental data. The results obtained with GP models evolved were satisfactory in predicting the area of the membrane breach and, with the simple membrane integrity test, the GP technique gives a practical way for estimating the degree of membrane damage. Therefore, GP could serve as a robust approach to develop an estimation model for the new membrane integrity test. (C) 2011 Elsevier B.V. All rights reserved. English ELSEVIER SCIENCE BV ULTRAFILTRATION MEMBRANES NANOSCALE PROBES SYSTEMS SIZE Application of genetic programming to develop the model for estimating membrane damage in the membrane integrity test using fluorescent nanoparticle Article 10.1016/j.desal.2011.07.045 1 DESALINATION, v.281, pp.80 - 87 DESALINATION 281 80 87 scie scopus 000297399900012 2-s2.0-80054872297 Engineering, Chemical Water Resources Engineering Water Resources Article ULTRAFILTRATION MEMBRANES NANOSCALE PROBES SYSTEMS SIZE Membrane Integrity test Fluorescent silica nanoparticle Genetic programming Image analysis