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

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.