Izvorni znanstveni članak
https://doi.org/10.15255/CABEQ.2016.931
Reactive Separation of Gallic Acid: Experimentation and Optimization Using Response Surface Methodology and Artificial Neural Network
K. Rewatkar
; Advanced Separation and Analytical Laboratory, Department of Chemical Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur-440010, Maharashtra, INDIA
D. Z. Shende
; Advanced Separation and Analytical Laboratory, Department of Chemical Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur-440010, Maharashtra, INDIA
K. L. Wasewar
orcid.org/0000-0001-7453-6308
; Advanced Separation and Analytical Laboratory, Department of Chemical Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur-440010, Maharashtra, INDIA
Sažetak
Gallic acid is a major phenolic pollutant present in the wastewater generated from cork boiling, olive mill, and pharmaceutical industries. Experimental and statistical modelling using response surface methodology (RSM) and artificial neural network (ANN) were carried out for reactive separation of gallic acid from aqueous stream using tri-nbutyl phosphate (TBP) in hexanol. TBP has a more significant effect on extraction efficiency as compared to temperature and pH. The optimum conditions of 2.34 g L–1, 65.65 % v/v, 19 oC, and 1.8 of initial concentration of gallic acid, concentration of TBP, temperature,
and pH, respectively, were obtained using RSM. Under optimum conditions, extraction efficiency of 99.45 % was obtained for gallic acid. The ANN and RSM results were compared with experimental unseen data. Error analysis suggested the better performance
of ANN for extraction efficiency predictions.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Ključne riječi
gallic acid; reactive extraction; Artificial Neural Network; Response Surface Methodology; optimization
Hrčak ID:
179821
URI
Datum izdavanja:
13.4.2017.
Posjeta: 1.162 *