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Industrial wastewater treatment using hybrid techniques and data modeling using RSM-ANOVA approach

Authors: Pratima Gajbhiye; Anand Mohan Yadav;

Industrial wastewater treatment using hybrid techniques and data modeling using RSM-ANOVA approach

Abstract

Department of Chemical Engineering, School of Chemical Engineering and Physical Sciences, Lovely Professional University, Jalandhar-144 411, Punjab, India Meerut Institute of Engineering and Technology, Meerut-250 005, Uttar Pradesh, India E-mail: pratima.24280@lpu.co.in Manuscript received online 18 November 2020, revised and accepted 30 November 2020 Sonochemistry is nowadays used to treat both water and wastewater. The aim of this work is to examine and determine the effect of combined treatment of sonochemical effect, ultrasound (US), ozonation (O3) and sonocatalysis. In this work, we have optimized the concentration of copper oxide catalyst for ozonation and sonocatalysis, the power intensity along with operat��ing parameter at different concentrations of copper oxide (CuO) catalyst was done treating the real industrial wastewater. The optimum conditions obtained from experiments were for pH 3 and catalyst loading 0.5 g/L. The key observation was that it achieved less energy consumption for the US/O3/CuO combined system with 75.6% of COD reduction. The combined effect is responsible for the production of hydroxyl radicals which treats the waste water. The modeling was also done using the Box-Behnken Design (BBD), where the combined effect of US, O3 and CuO catalyst loading was applied. The analysis of variance (ANOVA) was applied to fit the model. The determination of coefficient (R2) and the adjusted determination of coef��ficient (Adj. R2) for response % COD removal found to be 0.9998 and 0.9995 respectively, indicating a reasonable fit of the model to the experimental data.

Keywords

ANOVA, sonocatalysis, Ultrasound, ozonation, RSM, Box-Behnken Design

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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