Journal
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS
Volume 45, Issue 3, Pages 947-954Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jtice.2013.09.006
Keywords
Artificial neural networks (ANNs); Nanoscale zero valent iron (nZVI); Fenton process; Textile wastewater; ORP; DO; pH
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Funding
- National Science Council of the Republic of China, Taiwan [NSC 100-2221-E-239-006-MY2]
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The nanoscale zero valent iron-Fenton (nZVI-Fenton) process combines the advantages of nZVI reduction and Fenton oxidation, was regarded as a very effective process for the treatment of azo-dye/textile wastewater. In this paper, we present the results of the investigation of the on-line monitoring of oxidation-reduction potential (ORP), pH, and dissolved oxygen (DO) to evaluate the effectiveness of the nZVI-Fenton process for the removal of color and chemical oxygen demand (COD) from azo-dyes textile wastewater. The experimental results indicated that the optimal doses of nZVI and H2O2 for color removal were 60-80 mg/L and 300-400 mg/L, respectively. The nZVI reduction removed around 80-90% of the color; however, less than 15% of the COD was removed. To attain the desired removal of COD, much higher doses of nZVI and H2O2 were required, i.e., 200-225 mg/L and 1000-1125 mg/L, respectively. ORP, pH, and DO had direct and meaningful correlations with the removal efficiencies of both color and COD. Thus, regression models and artificial neural networks (ANN) models were used to predict the color and COD removal efficiencies using the monitoring data acquired for ORP, pH, and DO. The ANN model provided very precise prediction results with R-2 values in the range of 0.96-0.99. The predictions provided by the regression model had R-2 values in the range of 0.92-0.95. These results indicated that on-line ORP, DO, and pH monitoring data can be used as input to either model to obtain a reliable evaluation of the effectiveness of the nZVI-Fenton process for the removal of color and COD from azodyes textile wastewater. (c) 2013 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
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