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Environmental Science and Pollution Research

, Volume 26, Issue 18, pp 18739–18751 | Cite as

Design and analysis for the removal of active pharmaceutical residues from synthetic wastewater stream

  • Chinmoy Deb
  • Bonny Thawani
  • Sujith Menon
  • Varun Gore
  • Vijayalakshmi Chellappan
  • Shivendu Ranjan
  • Mahesh GanesapillaiEmail author
Research Article

Abstract

The removal of three over-the-counter pharmaceuticals from aqueous solution using four different adsorbents was analyzed. To study the effect of infused pharmaceutical and adsorbent on the adsorption system, both the concentration of drug and adsorbent dosage were varied, with constant temperature and pressure at different contact time. Adsorption kinetics, isotherm models, and ANOVA allegorized a generic trend for pharmaceutical removal efficiency of the adsorbents that varied as follows: activated carbon > fly ash > bentonite > sugar cane bagasse ash. The Tempkin model appears to fit the isotherm data better than Freundlich and Langmuir. Correspondingly, the kinetic studies implied a pseudo-second-order fit, to understand the mechanism by which the solute accumulates on the surface of a solid and gets adsorbed to the surface via intra-particle diffusion. Furthermore, some special cases of removal tendencies were noted based on sorbate-sorbent interaction. Effectively, it was observed that at an adsorbent loading of 2 g and initial concentration of 0.2 mmol L−1, bentonite, fly ash, and activated carbon were able to strip more than 80% of all pharmaceuticals from urine. A framework for the highest significance of the experiments was obtained using response surface methodology by the combination of ciprofloxacin-bentonite followed by paracetamol-activated carbon and ibuprofen-activated carbon. Quasi-Newton and Bayesian regression methods were implemented on Langmuir isotherm by designing the neural network for the batch adsorption experiments. Based on the numerical calculations and graphical representations, the proposed model leads to the result that error is minimized and the values are optimized for different pharmaceuticals such as paracetamol, ibuprofen, ciprofloxacin that can be removed from wastewater streams by locally available adsorbents.

Graphical abstract

Keywords

Human urine Emerging pollutants Pharmaceutical residue Wastewater treatment Artificial neural network 

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Mass Transfer Group, School of Chemical EngineeringVellore Institute of TechnologyVelloreIndia
  2. 2.Department of Mathematics, School of Advanced SciencesVellore Institute of TechnologyChennaiIndia
  3. 3.Faculty of Engineering and the Built EnvironmentUniversity of JohannesburgJohannesburgSouth Africa

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