An approach to removing COD and BOD based on polycarbonate mixed matrix membranes that contain hydrous manganese
Sasan Zahmatkesh1, Yousof Rezakhani2, Alireza Arabi3
1Department of Chemical Engineering, University of Science and Technology of Mazandaran, P.O. Box 48518-78195, Behshahr, Iran.
Abstract:
This study aimed to determine the efficacy of novel ultrafiltration and mixed matrix membrane (MMM) composed of hydrous manganese oxide (HMO) and silver nanoparticles (Ag-NPs) for the removal of biological oxygen demand (BOD) and chemical oxygen demand (COD). In the polycarbonate (PC) MMM, the weight percent of HMO and Ag-NP has been increased from 5% to 10%. A neural network (ANN) was used in this study to compare PC-HMO and Ag-NP. MMM was evaluated in combination with HMO and Ag-NP loadings in order to assess their effects on pure water flux, mean pore size, porosity, and efficacy in removing BOD and COD. HMO and Ag-NPs can decrease membrane porosity in the casting solution while increasing mean pore size. According to the study's findings, the artificial neural network model appears to be highly appropriate for predicting the removal of BOD and COD. To develop a successful model, a suitable input dataset was selected, which consisted of BOD and COD. An ideal model architecture for MMM was proposed based on an optimal number of hidden layers (2 layers) and neurons (5-8 neurons). Experiments and predicted data show a strong correlation between the developed models. BOD was predicted with an excellent R2 and a low root mean square error (RMSE) of 0.99 and 0.05%, respectively, while COD was predicted with an excellent R2 and a low RMSE of 0.99 and 0.09%, respectively. Based on the results, Ag-NP was found to be an excellent candidate for the preparation of MMMs as well as convenient for the removal of BOD and COD from polluted water sources.
Insights
This study shows that membranes with hydrous manganese oxide (HMO) and silver nanoparticles (Ag-NPs) effectively remove biological oxygen demand (BOD) and chemical oxygen demand (COD). An artificial neural network (ANN) accurately predicted pollutant removal, highlighting Ag-NPs for water purification.
Area of Science:
- Materials Science
- Environmental Engineering
- Nanotechnology
Background:
- Growing concerns over water pollution necessitate advanced treatment methods.
- Mixed Matrix Membranes (MMMs) offer tunable properties for contaminant removal.
- Nanomaterials like silver nanoparticles (Ag-NPs) show promise in water treatment applications.
Purpose of the Study:
- To evaluate the efficacy of novel ultrafiltration MMMs incorporating hydrous manganese oxide (HMO) and Ag-NPs for removing BOD and COD.
- To investigate the impact of varying HMO and Ag-NP concentrations on membrane performance.
- To utilize an artificial neural network (ANN) for predicting the removal of BOD and COD by the developed MMMs.
Main Methods:
- Fabrication of polycarbonate (PC) based MMMs with 5-10% weight loadings of HMO and Ag-NPs.
- Characterization of membrane properties including pure water flux, mean pore size, and porosity.
- Application of an ANN model with selected input datasets (BOD, COD) to predict removal efficiencies.
- Experimental validation of the ANN model's predictions.
Main Results:
- Increased HMO and Ag-NP content decreased membrane porosity while increasing mean pore size.
- The ANN model accurately predicted BOD and COD removal, achieving an R2 of 0.99 and low RMSE (0.05% for BOD, 0.09% for COD).
- Strong correlations were observed between experimental data and ANN model predictions.
Conclusions:
- MMMs incorporating HMO and Ag-NPs are highly effective for removing BOD and COD from polluted water.
- Ag-NPs are a suitable component for preparing efficient MMMs for water purification.
- ANN modeling provides a reliable approach for predicting the performance of such membranes.
