A Multivariate Approach for the Treatment of Mixed Anodizing Coating Wastewater by Electrochemical Processes

This research examined the treatment of mixed wastewater generated by the anodizing coating industry through electrocoagulation (EC) and electrooxidation (EO) processes. Key operational parameters, initial pH, applied current, and electrolysis time, were systematically varied to optimize contaminant removal. Process optimization was conducted using the Box-Behnken design (BBD), and the statistical validity of the model fit was assessed via analysis of variance (ANOVA). The responses for the EC and EO processes were chemical oxygen demand (COD), aluminum (Al), and sulfate removal efficiencies. The correlation coefficients (R<sup>2</sup>) for all model responses exceeded 0.95, demonstrating a strong agreement between predicted and observed values. Validation experiments confirmed high pollutant removal efficiencies, with COD removal rates of 94.5% and 90.8%, Al removal rates of 98.5% and 92.0%, and sulfate removal rates of 85.0% and 72.0% for the EC and EO processes, respectively. Although both procedures successfully accomplished effective pollutant removal, the EO method did not meet the discharge standards for the sewerage system, particularly in relation to sulfate concentration These findings highlight the superior efficacy of the EC process in sulfate removal, particularly for mixed wastewater from the anodizing coating industry.

Authors

Institutions

Publication Details

Journal
Water Environment Research
Published
2025-08-27
DOI
https://doi.org/10.1002/wer.70166
Primary Topic
Advanced oxidation water treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Multivariate Approach for the Treatment of Mixed Anodizing Coating Wastewater by Electrochemical Processes

Mehmet Çakmakçı, Tuğba Türk, Senem Yazıcı Güvenç, Oruç Kaan Türk
Water Environment Research
Advanced oxidation water treatment
article

A Multivariate Approach for the Treatment of Mixed Anodizing Coating Wastewater by Electrochemical Processes

Mehmet Çakmakçı, Tuğba Türk, Senem Yazıcı Güvenç, Oruç Kaan Türk
article en

Abstract

This research examined the treatment of mixed wastewater generated by the anodizing coating industry through electrocoagulation (EC) and electrooxidation (EO) processes. Key operational parameters, initial pH, applied current, and electrolysis time, were systematically varied to optimize contaminant removal. Process optimization was conducted using the Box-Behnken design (BBD), and the statistical validity of the model fit was assessed via analysis of variance (ANOVA). The responses for the EC and EO processes were chemical oxygen demand (COD), aluminum (Al), and sulfate removal efficiencies. The correlation coefficients (R<sup>2</sup>) for all model responses exceeded 0.95, demonstrating a strong agreement between predicted and observed values. Validation experiments confirmed high pollutant removal efficiencies, with COD removal rates of 94.5% and 90.8%, Al removal rates of 98.5% and 92.0%, and sulfate removal rates of 85.0% and 72.0% for the EC and EO processes, respectively. Although both procedures successfully accomplished effective pollutant removal, the EO method did not meet the discharge standards for the sewerage system, particularly in relation to sulfate concentration These findings highlight the superior efficacy of the EC process in sulfate removal, particularly for mixed wastewater from the anodizing coating industry.

Water Environment ResearchVol. 97(9)
Yıldız Technical University (TR)
Openalex Percentile: Top 79%
Advanced oxidation water treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.