31. Artificial neural network (ANN) approach for modeling of methyl orange adsorption by Syzygium cumini seed coat
Main Article Content
Abstract
In current study remediation of methyl orange was studied from aqueous media using Syzygium cumini seed coat as an active material. Batch adsorption method was utilized for the purpose and parameters optimization, including pH (3, 7 & 12), initial concentration (40, 80 & 120 ppm) and contact time (40, 80 & 120 minutes). After parameters optimization these variables were taken as input layer of Artificial Neural Network (ANN) to calculate predictive model for the removal of dye. For this purpose STATISTICA 10 software was used and architecture used for model generation was 3-8-1, which includes 3 input layers, 8 hidden and 1 output layers. Experimental results shows that Syzygium cumini seed coat is an excellent adsorber for methyl orange due to different functional groups ( -OH, C=O and C-O) present on its surface as analyzed by FTIR and rough surface shown in SEM image. Predictive modeling was performed by ANN to determine adsorption capacity of the studied adsorbent for dyes. Validation of generated model was done by measuring R-square value (0.96). Results indicates Syzygium cumini can be successfully utilized for remediation of methyl orange from aqueous media and predictive modeling by ANN can predict future adsorption on this adsorbent.
Keywords: Adsorption; Artificial Neural Network (ANN); Azo dyes; Predictive modeling; Syzygium cumini