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Zahmatkesh S, Rezakhani Y, Arabi A, Hasan M, Ahmad Z, Wang C, Sillanpää M, Al-Bahrani M, Ghodrati I. An approach to removing COD and BOD based on polycarbonate mixed matrix membranes that contain hydrous manganese oxide and silver nanoparticles: A novel application of artificial neural network based simulation in MATLAB. CHEMOSPHERE 2022; 308:136304. [PMID: 36096310 DOI: 10.1016/j.chemosphere.2022.136304] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/20/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
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.
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Affiliation(s)
- Sasan Zahmatkesh
- Department of Chemical Engineering, University of Science and Technology of Mazandaran, P.O. Box 48518-78195, Behshahr, Iran.
| | - Yousof Rezakhani
- Department of Civil Engineering, Pardis Branch, Islamic Azad University, Pardis, Iran
| | - Alireza Arabi
- Center for Processing and Characterization of Nanostructured Materials, School of Mechanical Engineering, University of Tehran, P.O.B.14399-57131,1450, Iran
| | - Mudassir Hasan
- College of Engineering, Department of Chemical Engineering, King Khalid University, Abha, 61411, Saudi Arabia
| | - Zubair Ahmad
- School of Chemical Engineering, Yeungnam University, Gyeongsan, 712-749, Republic of Korea.
| | - Chongqing Wang
- School of Chemical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Mika Sillanpää
- Faculty of Science and Technology, School of Applied Physics, University Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia; International Research Centre of Nanotechnology for Himalayan Sustainability (IRCNHS), Shoolini University, Solan, 173212, Himachal Pradesh, India; Department of Chemical Engineering, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, P. O. Box 17011, Doornfontein, 2028, South Africa; Zhejiang Rongsheng Environmental Protection Paper Co. LTD, NO.588 East Zhennan Road, Pinghu Economic Development Zone, Zhejiang, 314213, PR China
| | - Mohammed Al-Bahrani
- Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon, 51001, Iraq
| | - Iman Ghodrati
- Department of Computer Engineering, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran
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