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Optimization of process variables for treatment of food industry effluents by electrocoagulation View Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000696</doi_batch_id><timestamp>1714116434</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 3 </title></titles><publication_date><month>12</month><year>2024</year></publication_date><journal_volume><volume>20</volume></journal_volume><issue>3</issue></journal_issue><journal_article><titles><title>Optimization of process variables for treatment of food industry effluents by electrocoagulation </title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p><p>The present study investigates the process optimization for treatment of potato processing industry wastewater by electrocoagulation. Performance of treatability was evaluated by achieved chemical oxygen demand (COD) removal efficiencies. Response Surface Methodology was applied to determine individual and interactive impacts of the selected operation variables (pH, conductivity, current density and duration period) on COD removal. Optimization studies were also performed for energy consumption costs which still remain as the major drawback of the electrocoagulation applications. Interactions between dependent and independent process variables were interpreted by the relevant surface plots. Sufficient correlation coefficients were obtained for the predictive regression models of the optimization. Results of the ANOVA tests showed that both of the models are statistically significant. Impacts of the process parameters on response values have been evaluated numerically by Pareto analysis. Furthermore, validation studies were conducted to confirm the accuracy of the model developed for prediction of COD removal efficiencies.</p> </p></abstract><publication_date><month>09</month><day>26</day><year>2018</year></publication_date><pages><first_page>551</first_page><last_page>557</last_page></pages><publisher_item><identifier id_type="pii">gnest_02640</identifier></publisher_item><doi_data><doi>10.30955/gnj.002640</doi><timestamp>1714116434</timestamp><resource>https://journal.gnest.org/publication/gnest_02640</resource></doi_data></journal_article></journal></body></doi_batch> </div>
