SARS-CoV-2 removal by mix matrix membrane: A novel application of artificial neural network based simulation in MATLAB for evaluating wastewater reuse risks

Sasan Zahmatkesh; Yousof Rezakhani; Abdoulmohammad Gholamzadeh Chofreh; Melika Karimian; Chongqing Wang; Iman Ghodrati; Mudassir Hasan; Mika Sillanpaa; Hitesh Panchal; Ramsha Khan

doi:10.1016/j.chemosphere.2022.136837

SARS-CoV-2 removal by mix matrix membrane: A novel application of artificial neural network based simulation in MATLAB for evaluating wastewater reuse risks

Chemosphere. 2023 Jan:310:136837. doi: 10.1016/j.chemosphere.2022.136837. Epub 2022 Oct 14.

Authors

Affiliations

¹ Department of Chemical Engineering, University of Science and Technology of Mazandaran, P.O. Box 48518-78195, Behshahr, Iran; Tecnologico de Monterrey, Escuela de Ingenieríay Ciencias, Puebla, Mexico. Electronic address: sasan-zahmatkesh@mazust.ac.ir.
² Department of Civil Engineering, Pardis Branch, Islamic Azad University, Pardis, Iran.
³ Sustainable Process Integration Laboratory, SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology, VUT Brno, Technická 2896/2, 616 00, Brno, Czech Republic.
⁴ Faculty of Civil Engineering, Architecture and Urban Planning, University of Eyvanekey, Iran.
⁵ School of Chemical Engineering, Zhengzhou University, Zhengzhou, 450001, China.
⁶ Department of Computer Engineering, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran.
⁷ Department of Chemical Engineering, College of Engineering, King Khalid University, Abha, 61411, Saudi Arabia.
⁸ 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.
⁹ Mechanical Engineering Department, Government Engineering College, Patan, Gujarat, India.
¹⁰ Faculty of Civil Engineering, Institute of Technology, Shri Ramswaroop Memorial University, Barabanki, 225003, UP, India.

Abstract

The COVID-19 outbreak led to the discovery of SARS-CoV-2 in sewage; thus, wastewater treatment plants (WWTPs) could have the virus in their effluent. However, whether SARS-CoV-2 is eradicated by sewage treatment is virtually unknown. Specifically, the objectives of this study include (i) determining whether a mixed matrixed membrane (MMM) is able to remove SARS-CoV-2 (polycarbonate (PC)-hydrous manganese oxide (HMO) and PC-silver nanoparticles (Ag-NP)), (ii) comparing filtration performance among different secondary treatment processes, and (iii) evaluating whether artificial neural networks (ANNs) can be employed as performance indicators to reduce SARS-CoV-2 in the treatment of sewage. At Shariati Hospital in Mashhad, Iran, secondary treatment effluent during the outbreak of COVID-19 was collected from a WWTP. There were two PC-Ag-NP and PC-HMO processes at the WWTP targeted. RT-qPCR was employed to detect the presence of SARS-CoV-2 in sewage fractions. For the purposes of determining SARS-CoV-2 prevalence rates in the treated effluent, 10 L of effluent specimens were collected in middle-risk and low-risk treatment MMMs. For PC-HMO, the log reduction value (LRV) for SARS-CoV-2 was 1.3-1 log10 for moderate risk and 0.96-1 log10 for low risk, whereas for PC-Ag-NP, the LRV was 0.99-1.3 log10 for moderate risk and 0.94-0.98 log10 for low risk. MMMs demonstrated the most robust absorption performance during the sampling period, with the least significant LRV recorded in PC-Ag-NP and PC-HMO at 0.94 log10 and 0.96 log10, respectively.

Keywords: Artificial neural network; Mix matrix membrane; SARS-CoV-2; Wastewater treatment.

MeSH terms

COVID-19* / epidemiology
Humans
Metal Nanoparticles*
Neural Networks, Computer
SARS-CoV-2
Sewage
Silver
Wastewater

Substances

Waste Water
Sewage
Silver