Optimization of ranitidine hydrochloride removal from simulated pharmaceutical waste by activated charcoal from mung bean husk using response surface methodology and artificial neural network. Issue 39 (20th August 2016)

• Record Type: Journal Article
• Authors: Mondal, Sandip; Aikat, Kaustav; Halder, Gopinath
• Is Part Of: Desalination and water treatment. Volume 57:Issue 39(2016)
• Page Start: 18366
• Page End: 18378
• Publication Date: 2016-08-20
• Subjects: Ranitidine hydrochloride -- Mung bean husk -- Simulated pharmaceutical waste -- Response surface methodology -- Artificial neural network; Saline water conversion -- Periodicals; Saline water conversion; Water -- Purification; Periodicals; 628.167
• DOI: 10.1080/19443994.2015.1088899
• Languages: English
• ISSNs: 1944-3994

Abstract

Abstract: The removal of ranitidine hydrochloride (RH) from simulated pharmaceutical aqueous solution using steam-activated charcoal from mung bean husk (MBH) by batch adsorption technique was investigated. The adsorbent was characterized by Brunauer–Emmett–Teller surface area analyzer, SEM, and point of zero charge. The influence of three process parameters such as adsorbent dose, solution pH, and agitation on the performance of the activated carbon was studied. The removal of 100 mg L −1 RH was 99.16% at pH 2, adsorbent dose 0.75 gm L −1, and agitation speed of 180 rpm. The effects of process parameters on the removal efficiency were optimized as per central composite design of response surface methodology (RSM), and the same design was used for training set for artificial neural network (ANN). The results showed that ANN has better prediction capability as compared to RSM and SAC developed from MBH could be a promising adsorbent for RH removal from simulated pharmaceutical waste.