Modeling the effects of gyrotactic microbes in membrane bioreactor systems for micropollutant removal from sewer water: ANN and statistical-based optimization review

Industrial wastewater has many different harmful substances, such as pesticides, medicines, and dyes, that can damage the environment. Traditional treatment methods usually do not work well because these pollutants are often mixed and difficult to remove. Hence, the innovative treatment technologies, particularly biological processes such as Membrane Bioreactors (MBRs) have become the efficient and sustainable solutions to safely remediate industrial wastewater in the long-term and in a friendly to environment manner. The novelty of this work is a computational investigation of gyrotactic microorganisms suspended in a nanofluid (water-based) flowing over a vertical sheet to achieve optimum MBR system performance, with subsequent prediction and optimization of important parameters using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM).The new set of mathematical governing equations are formulated to investigate the impacts of fluid velocity, temperature, microbial distribution, local nusselt number and density of motile microorganisms. MATLAB bvp4c solver is used to find the numerical solution of these equations. The result shows that the amplification in bioconvection Rayleigh number (Rb) decreases the fluid velocity but increases temperature thus promoting microbe activity and heat transfer to maximize the performance of the MBR. In order to further optimize performance of the system, the Levenberg-Marquardt algorithm was used to develop ANN which is also trained using simulation data and RSM optimization is done using the Design Expert 13. Analysis of Variance (ANOVA) is used to test the adequacy of RSM model. The findings indicates that the ANN model has been found to give the best accuracy (R 2 = 0.99996) followed by RSM (R 2 = 0.9938). These results will serve as important information regarding the interactions of bio-convective and nanofluid to optimize the MBR systems, in an advanced wastewater treatment process.

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Publication Details

Journal
International Communications in Heat and Mass Transfer
Published
2025-10-09
DOI
https://doi.org/10.1016/j.icheatmasstransfer.2025.109826
Primary Topic
Hops Chemistry and Applications
Type
article
Field-Weighted Citation Impact
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article

Modeling the effects of gyrotactic microbes in membrane bioreactor systems for micropollutant removal from sewer water: ANN and statistical-based optimization review

International Communications in Heat and Mass Transfer
Hops Chemistry and Applications
article

Modeling the effects of gyrotactic microbes in membrane bioreactor systems for micropollutant removal from sewer water: ANN and statistical-based optimization review

article en

Abstract

Industrial wastewater has many different harmful substances, such as pesticides, medicines, and dyes, that can damage the environment. Traditional treatment methods usually do not work well because these pollutants are often mixed and difficult to remove. Hence, the innovative treatment technologies, particularly biological processes such as Membrane Bioreactors (MBRs) have become the efficient and sustainable solutions to safely remediate industrial wastewater in the long-term and in a friendly to environment manner. The novelty of this work is a computational investigation of gyrotactic microorganisms suspended in a nanofluid (water-based) flowing over a vertical sheet to achieve optimum MBR system performance, with subsequent prediction and optimization of important parameters using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM).The new set of mathematical governing equations are formulated to investigate the impacts of fluid velocity, temperature, microbial distribution, local nusselt number and density of motile microorganisms. MATLAB bvp4c solver is used to find the numerical solution of these equations. The result shows that the amplification in bioconvection Rayleigh number (Rb) decreases the fluid velocity but increases temperature thus promoting microbe activity and heat transfer to maximize the performance of the MBR. In order to further optimize performance of the system, the Levenberg-Marquardt algorithm was used to develop ANN which is also trained using simulation data and RSM optimization is done using the Design Expert 13. Analysis of Variance (ANOVA) is used to test the adequacy of RSM model. The findings indicates that the ANN model has been found to give the best accuracy (R 2 = 0.99996) followed by RSM (R 2 = 0.9938). These results will serve as important information regarding the interactions of bio-convective and nanofluid to optimize the MBR systems, in an advanced wastewater treatment process.

International Communications in Heat and Mass TransferVol. 169
Vellore Institute of Technology University (IN)
Clean water and sanitation
Openalex Percentile: Top 39%
Hops Chemistry and Applications
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