PreprintArticleVersion 1This version is not peer-reviewed
A Novel Modeling Optimization Approach for a Seven-channel Titania Ceramic Membrane in an Oily Wastewater Filtration System Based on Experimentation, Full Factorial Design, and Machine Learning
Version 1
: Received: 12 August 2024 / Approved: 12 August 2024 / Online: 12 August 2024 (23:46:01 CEST)
How to cite:
Echakouri, M.; Henni, A.; Salama, A. A Novel Modeling Optimization Approach for a Seven-channel Titania Ceramic Membrane in an Oily Wastewater Filtration System Based on Experimentation, Full Factorial Design, and Machine Learning. Preprints2024, 2024080839. https://doi.org/10.20944/preprints202408.0839.v1
Echakouri, M.; Henni, A.; Salama, A. A Novel Modeling Optimization Approach for a Seven-channel Titania Ceramic Membrane in an Oily Wastewater Filtration System Based on Experimentation, Full Factorial Design, and Machine Learning. Preprints 2024, 2024080839. https://doi.org/10.20944/preprints202408.0839.v1
Echakouri, M.; Henni, A.; Salama, A. A Novel Modeling Optimization Approach for a Seven-channel Titania Ceramic Membrane in an Oily Wastewater Filtration System Based on Experimentation, Full Factorial Design, and Machine Learning. Preprints2024, 2024080839. https://doi.org/10.20944/preprints202408.0839.v1
APA Style
Echakouri, M., Henni, A., & Salama, A. (2024). A Novel Modeling Optimization Approach for a Seven-channel Titania Ceramic Membrane in an Oily Wastewater Filtration System Based on Experimentation, Full Factorial Design, and Machine Learning. Preprints. https://doi.org/10.20944/preprints202408.0839.v1
Chicago/Turabian Style
Echakouri, M., Amr Henni and Amgad Salama. 2024 "A Novel Modeling Optimization Approach for a Seven-channel Titania Ceramic Membrane in an Oily Wastewater Filtration System Based on Experimentation, Full Factorial Design, and Machine Learning" Preprints. https://doi.org/10.20944/preprints202408.0839.v1
Abstract
This comprehensive study looks at how operational conditions affect the performance of a novel seven-channel titania ceramic ultrafiltration membrane for the treatment of produced water. A Full factorial design experiment (23) was conducted to study the effect of the cross-flow operating factors on the membrane permeate flux decline and the overall permeate volume. Eleven experimental runs were performed for three important process operating variables: Transmembrane Pressure (TMP), Crossflow Velocity (CFV), and Filtration Time (FT). Steady final membrane fluxes and permeate volumes were recorded for each experimental run. Under the optimized conditions (1.5 bar, 1 m/s, and 2 hours), the membrane performance index demonstrated an oil rejection rate of 99%, a flux of 297 liters/m2·hr (LMH), a 38% overall initial flux decline, and a total permeate volume of 8.14 L. The regression models used for the steady-state membrane permeate flux decline and overall permeate volume led to the highest goodness of fit to the experimental data with a correlation coefficient of 0.999. A Multiple Linear Regression method and an Artificial Neural Network approach were also employed to model the experimental membrane permeate flux decline and analyze the impact of the operating conditions on membrane performance. The predictions of the Gaussian regression and the Levenberg-Marquardt backpropagation method were validated with a determination coefficient of 99 % and a mean square error of 0.07.
Keywords
Oily Wastewater; Titania Ceramic Membrane; Fouling Control; Optimization; Statistical Modeling; Full Factorial Design; Multiple Linear Regression; Artificial Neural Network
Subject
Engineering, Chemical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
