Reactive Separation of Gallic Acid: Experimentation and Optimization Using Response Surface Methodology and Artificial Neural Network

被引：24

作者：

Rewatkar, K. ^{[1
]}

Shende, D. Z. ^{[1
]}

Wasewar, K. L. ^{[1
]}

机构：

[1] VNIT, Dept Chem Engn, Adv Separat & Analyt Lab, Nagpur 440010, Maharashtra, India

来源：

CHEMICAL AND BIOCHEMICAL ENGINEERING QUARTERLY | 2017年 / 31卷 / 01期

关键词：

gallic acid; reactive extraction; Artificial Neural Network; Response Surface Methodology; optimization; MILL WASTE-WATER; AQUEOUS-SOLUTIONS; ACRYLIC-ACID; EXTRACTION; PHENOLS; PLANTS;

D O I：

10.15255/CABEQ.2016.931

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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-n-butyl 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 degrees C, 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.

引用

页码：33 / 46

页数：14

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