language English
Article 2026 · 0 atıf

Pistachio Processing Wastewater treatment by Peroxi-Electrocoagulation Method and Artificial Neural Network Modelling

Bayar, Serkan, Boncukcuoglu, Recep, Tasdemir, Atila, Oguz, Ensar

Özet

In this study, the peroxi-electrocoagulation process was investigated for the treatment of pistachio processing wastewater. The electrolysis cell was configured with 5 & times; 5 iron plate electrodes at a fixed 5 mm spacing. Electrolysis time of 90 min, 25 degrees C temperature, and 200 rpm stirring speed were maintained constant throughout the experiments. The effects of H(2)O(2 )dose (94-658 mM), initial pH (2.0-6.0), and current density (1-6 mA/cm(2)) on the removal of TOC, COD, and total phenol (TPh) were evaluated. Optimum operation conditions within the studied range were found as 564 mM H2O2, 3.0 initial pH, 3 mA/cm(2) current density, resulting in 88.9% TPh, 80.7% COD, and 65% TOC removal efficiencies. Kinetic study showed that the PEC process followed second-order kinetics, with R2 of 0.82, 0.82, and 0.80 for TPh, COD, and TOC, respectively. Reaction rate constants increased with increasing current density and H2O2 concentration, whereas reaction rates decreased at pH below and above 3.0. The highest reaction rates were observed at pH 3.0, 6 mA/cm(2) CD, and 658 mM H2O2. A single hidden-layer backpropagation network with a topology of 4:6:3 was developed following hidden layer neurons optimization to predict TOC, COD, and TPh concentrations. The final ANN model performance yielded RMSE of 150.4 +/- 31, 725.7.3 +/- 156.9, and 197.2 +/- 37.9, and R2 of 0.98, 0.97, and 0.95 for TPh, COD, and TOC, respectively. The high correlation between measured and predicted concentrations (R2; TPh:0.97, COD:0.97, TOC:0.92), confirmed the reliability of the model. Under selected conditions, the specific energy consumption and operating cost were calculated as 16.2 kWh/kg COD and 65.9 $/m(3), respectively.

Anahtar Kelimeler
Peroxi-electrocoagulation (PEC) Pistachio Processing Wastewater (PPW) H2O2 Artificial Neural Networks (ANN) ELECTRO-FENTON EFFICIENT REMOVAL AQUEOUS-SOLUTION FELT CATHODE COAGULATION DYE WASTEWATERS PHENOL COD DECOLORIZATION
WoS Kategorileri
Environmental Sciences Meteorology & Atmospheric Sciences Water Resources
Künye
Dergi
WATER AIR AND SOIL POLLUTION
Yıl
2026
Cilt
237
Sayı
11
ISSN
0049-6979
eISSN
1573-2932
DOI
10.1007/s11270-026-09321-7
WoS UID
WOS:001714315900004
Yayını aç (DOI)