Modeling azo dye removal by sono-fenton processes using response surface methodology and artificial neural network approaches
| dc.authorid | 0000-0002-1628-5026 | |
| dc.authorid | 0000-0003-2734-8544 | |
| dc.contributor.author | Baştürk, Emine | |
| dc.contributor.author | Alver, Alper | |
| dc.date.accessioned | 2019-10-16T13:10:04Z | |
| dc.date.available | 2019-10-16T13:10:04Z | |
| dc.date.issued | 2019 | |
| dc.department | Mühendislik Fakültesi | |
| dc.description.abstract | Textile industry wastewaters, which cause serious problems in the environment and human health, include synthetic dyes, complex organic pollutants, surfactants, and other toxic chemicals and therefore must be removed by advanced treatment methods. Determination of appropriate treatment conditions for efficient use of advanced treatment methods is an important and necessary step. In the last thirty years, the Artificial Neural Network-Genetic Algorithm (ANN-GA) and Response Surface Methodology (RSM) have emerged as the most effective empirical modeling and optimization methods especially for nonlinear systems. Reactive Red 195 azo dyestuff was chosen as the target pollutant. The color removal efficiency was modeled and optimized as a function of Sono-Fenton conditions such as H2O2 dosage, Fe2+ dosage, initial pH value, ultrasound power, and ultrasound frequency, using ANN-GA and RSM. The generalization and predictive ability of these methods were compared using the results of the 46 experimental sets generated by the Box-Behnken design. The mean square errors for these models are 3.01612 and 0.00295, and the regression coefficients showing the superiority of ANN in determining nonlinear behavior are 0.9856 and 0.9164, respectively. In optimal conditions, the prediction errors with hybrid ANN-GA and RSM models are 0.002% and 3.225%, respectively. | |
| dc.description.abstract | ... | |
| dc.identifier.doi | 10.1016/j.jenvman.2019.109300 | |
| dc.identifier.endpage | 8 | en_US |
| dc.identifier.issn | 0010-9300 | |
| dc.identifier.issue | - | en_US |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 2 | en_US |
| dc.identifier.uri | https://dx.doi.org/10.1016/j.jenvman.2019.109300 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/6853 | |
| dc.identifier.volume | 248 | en_US |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.indekslendigikaynak | PubMed | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.relation.ispartof | Journal of Environmental Management | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/embargoedAccess | |
| dc.subject | Reactive Red 195 | |
| dc.subject | Sono-fenton | |
| dc.subject | Box-behnken Design | |
| dc.subject | Response Surface Methodology | |
| dc.subject | Artificial Neural Network | |
| dc.subject | Genetic Algorithm | |
| dc.title | Modeling azo dye removal by sono-fenton processes using response surface methodology and artificial neural network approaches | |
| dc.type | Article |
