Decomposition of PPCPs by ultrasound-assisted advanced Fenton reaction: A case study with salicylic acid.

Ultrason Sonochem

Institute of Environmental Sciences, Bogazici University, 34342 Istanbul, Turkey. Electronic address:

Published: November 2017

AI Article Synopsis

  • The study investigates the degradation of salicylic acid, a common pharmaceutical agent, using sonocatalysis techniques that involve sonication with zero-valent iron and hydrogen peroxide.
  • The research is split into two parts: the first focuses on optimizing operational parameters like frequency, reaction time, and reagent doses; the second employs response surface methodology to analyze the effects of pH, power, and reagent doses on the degradation process.
  • The model predicts optimal conditions for maximum salicylic acid degradation but highlights the need for adjustments to reduce energy and reagent consumption, with validation showing high prediction accuracy (5.0-9.4% deviation from actual results).

Article Abstract

The study is about the degradation of a widely used pharmaceutical and personal care product-salicylic acid by sonocatalysis, and the experimental design of the reaction system. The first part of the study consists of sonication (572kHz) in the presence of zero-valent iron (ZVI) with or without HO to select and optimize the operational parameters as frequency, time, initial solute concentration, dose of reagents and pH. The second part consists of the use of response surface methodology and multiple regression to develop an experimental design modeland to assess the individual and interactive effects of pH, power (P), ZVI dose and HO. The results showed that the optimal conditions predicted by the model without defining any restrictions are: pH=2.0, P=120W, ZVI=24mgL, which provide total salicyclic acid and 48% TOC decay. However, the prediction implies intensive consumption of energy and reagents, and must therefore be modified by restricting the value of TOC decay to a lower value and that of pH to a higher one. Cross-validation tests showed that the prediction accuracy of the model was considerably high with 5.0-9.4% deviation from the experimental data.

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Source
http://dx.doi.org/10.1016/j.ultsonch.2017.04.013DOI Listing

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