Deep Study on Fouling Modelling of Ultrafiltration Membranes Used for OMW Treatment: Comparison Between Semi-empirical Models, Response Surface, and Artificial Neural Networks

• Published in: Food and bioprocess technology Vol. 16; no. 10; pp. 2126 - 2146
• Main Authors: Cifuentes-Cabezas, Magdalena, Bohórquez-Zurita, José Luis, Gil-Herrero, Sandra, Vincent-Vela, María Cinta, Mendoza-Roca, José Antonio, Álvarez-Blanco, Silvia
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2023; Springer Nature B.V
• Subjects: Agriculture; Artificial neural networks; Biotechnology; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Cross flow; Empirical analysis; Flow velocity; Food industries wastewaters; Food Science; Fouling; Machine learning; Mathematical models; Membrane processes; Membranes; Modelling; Neural networks; Olive oil; Pore size; Pressure effects; Residuals; Response surface methodology; Ultrafiltration; Variance analysis; Velocity; Wastewater; Fouling mechanisms; Response surface; Ultrafiltration; Artificial neural networks; Semi-empirical models
• ISSN: 1935-5130; 1935-5149
• DOI: 10.1007/s11947-023-03033-0

Olive oil production generates a large amount of wastewater called olive mill wastewater. This paper presents the study of the effect of transmembrane pressure and cross flow velocity on the decrease in permeate flux of different ultrafiltration membranes (material and pore size) when treating a two-phase olive mill wastewater (olive oil washing wastewater). Both semi-empirical models (Hermia models adapted to tangential filtration, combined model, and series resistance model), as well as statistical and machine learning methods (response surface methodology and artificial neural networks), were studied. Regarding the Hermia model, despite the good fit, the main drawback is that it does not consider the possibility that these mechanisms occur simultaneously in the same process. According to the accuracy of the fit of the models, in terms of R 2 and SD, both the series resistance model and the combined model were able to represent the experimental data well. This indicates that both cake layer formation and pore blockage contributed to membrane fouling. The inorganic membranes showed a greater tendency to irreversible fouling, with higher values of the R a /R T (adsorption/total resistance) ratio. Response surface methodology ANOVA showed that both cross flow velocity and transmembrane pressure are significant variables with respect to permeate flux for all membranes studied. Regarding artificial neural networks, the tansig function presented better results than the selu function, all presenting high R 2 , ranging from 0.96 to 0.99. However, the comparison of all the analyzed models showed that depending on the membrane, one model fits better than the others. Finally, through this work, it was possible to provide a better understanding of the data modelling of different ultrafiltration membranes used for the treatment of olive mill wastewater.