The Effect of Nanomaterial Type on Water Disinfection Using Data Mining

• Autorzy: Hamdan Mohammad , Khalil Rana Haj , Abdelhafez Eman , Ajib Salman

Identyfikatory

DOI

10.12911/22998993/160093

• Języki publikacji: EN

Abstrakty

EN

Multiple linear regression and artificial neural network (ANN) models were utilized in this study to assess the type influence of nanomaterials on polluted water disinfection. This was accomplished by estimating E. coli (E.C) and the total coliform (TC) concentrations in contaminated water while nanoparticles were added at various concentrations as input variables, together with water temperature, PH, and turbidity. To achieve this objective, two approaches were implemented: data mining with two types of artificial neural networks (MLP and RBF), and multiple linear regression models (MLR). The simulation was conducted using SPSS software. Data mining was revealed after the estimated findings were checked against the measured data. It was found that MLP was the most promising model in the prediction of the TC and E.C concentration, s followed by the RBF and MLR models, respectively.

Słowa kluczowe

EN

water disinfection; artificial neural network; nanotechnology; data mining

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2023
• Tom: Vol. 24, nr 4
• Strony: 244--251
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Hamdan Mohammad

• Renewable Energy Technology Department, Applied Science Private University, Amman, Jordan

autor

Khalil Rana Haj

• Renewable Energy Engineering Department, Jadara University, Irbid, Jordan

autor

Abdelhafez Eman

• Department of Alternative Energy Technology, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan

• https://orcid.org/0000-0001-9480-7582

autor

Ajib Salman

• Department of Renewable Energies and Decentralized Energy Supply, University of Applied Sciences and Arts, Werftestrasse 4, CH-6002 Luzern, Switzerland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).