The Utilization and Modeling of Photo-Fenton Process as a Single Unit in Textile Wastewater Treatment

SARI B., Turkes S., Guney H., KESKİNKAN O.

CLEAN-SOIL AIR WATER, cilt.51, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 51
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1002/clen.202100328
  • Dergi Adı: CLEAN-SOIL AIR WATER
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Environment Index, Geobase, Greenfile, INSPEC, Pollution Abstracts, Veterinary Science Database
  • Anahtar Kelimeler: artificial neural network (ANN), modeling, NARX-ANN, photo-Fenton process, textile industry, wastewater treatment, FORECASTING GROUNDWATER LEVEL, ADVANCED OXIDATION PROCESSES, ARTIFICIAL NEURAL-NETWORKS, EXPERIMENTAL-DESIGN, AQUEOUS-SOLUTION, COD REMOVAL, DEGRADATION, OPTIMIZATION, INDUSTRY, REAGENT
  • Çukurova Üniversitesi Adresli: Evet

Özet

Studies on the direct application of the photo-Fenton process (PFOP) to disinfect and decontaminate textile wastewater are rare. The output of the artificial neural network (ANN) models applied to the wastewater of a textile factory producing woven fabrics, which is used to assess the efficiency of the PFOP process, are investigated and compared with each other in this study. The highest PFOP efficiency is obtained at a pH of 3. Chemical oxygen demand (COD), suspended solids (SS) and color removal rates are 94%, 90%, and 96%, respectively. The data are modeled with ANNs and nonlinear external input autoregressive ANNs (NARX-ANN) using the MATLAB R2020a software program. Both Levenberg-Marquardt (trainlm) and scaled conjugate gradient (trainscg) algorithms are employed in the ANN and NARX-ANN models, whereas hyperbolic tangent sigmoid (Tansig) and logistic sigmoid (Logsig) functions are superimposed on the hidden layer in the ANN model, and Tansig functions are superimposed on the NARX-ANN model. It is determined that the developed ANN models are more effective in estimating the PFOP efficiency. The mean squared error is 0.000 953, and the coefficient of determination (R-2) is 0.96 661.