Statistical Analysis and Optimization of Acid Dye Biosorption by Brewery Waste Biomass Using Response Surface Methodology

V Jaikumar,V Ramamurthi

doi:10.5539/mas.v3n4p71

Abstract

Biosorption of Acid Yellow (AY 17) and Acid Blue (AB 25) were investigated using a biomass obtained from brewery industrial waste spent brewery grains (SBG). A 24 full factorial response surface central composite design with seven replicates at the centre point and thus a total of 31 experiments were employed for experimental design and analysis of the results. The combined effect of time, pH, adsorbent dosage and dye concentration on the dye biosorption was studied and optimized using response surface methodology. The optimum contact time, pH, adsorbent dosage and dye concentration were found to be 45min, 6, 0.5g, 75 mg/L respectively for the maximum decolorization of AY 17(97.2%) and 40 min, 2, 0.4g and 75 mg/L respectively for the maximum decolorization of AB 25(97.9%). A quadratic model was obtained for dye decolourization through this design. The experimental values were in good agreement with predicted values and the model developed was highly significant, the correlation coefficient being 0.89 and 0.905 for AY 17 and AB 25 respectively. Experimental results were analyzed by Analysis of variance (ANOVA) statistical concept.

Highlights

(McKay, 1984); the hardwood sawdust (Asfour et al, 1985); Bagasse pith (McKay et al, 1987); Fly ash (Khare et al, 1987); Paddy straw (Deo, 1993); Rice husk (Lee & Low, 1997); Slag (Ramakrishna & Viraraghavan, 1997); Chitosan (Juang et al, 1997); Palm fruit bunch (Nasser, 1997); Bone char (Ko et al, 2000)
Experimental plan showing the coded value of the variables together with dye removal efficiency are given in Table 3.The analysis focused on how the colour removal efficiency is influenced by independent variables, i.e., time (X1), pH(X2), adsorbent dosage(X3) and dye concentration(X4).The dependent output variable is maximum removal efficiency
The present investigation clearly demonstrated the applicability of spent brewery grains (SBG) as biosorbent for AY 17 and AB 25 dye removal from aqueous solutions

Summary

Introduction

(McKay, 1984); the hardwood sawdust (Asfour et al, 1985); Bagasse pith (McKay et al, 1987); Fly ash (Khare et al, 1987); Paddy straw (Deo, 1993); Rice husk (Lee & Low, 1997); Slag (Ramakrishna & Viraraghavan, 1997); Chitosan (Juang et al, 1997); Palm fruit bunch (Nasser, 1997); Bone char (Ko et al, 2000). The traditional step-by-step approach, widely used, involves a large number of independent runs and does not enable us to establish the multiple interacting parameters This method is time consuming, material consuming and requires large number of experimental trials to find out the effects, which are unreliable. Designed experiments to optimize the system with lesser number of experiments are the need of the hour These limitations of the traditional method can be eliminated by optimizing all the affecting parameters collectively by statistical experimental design (Montgomery, 1991). In this present study, experiments were designed by incorporating all important process variables namely time, pH, adsorbent dosage, and initial dye concentration using Statistical Design Software Minitab 14 (USA). The corresponding interactions among the variables were studied and optimized using central composite design and response surface and contour plots

Methods

Results

Conclusion