Optimum influence of kaolin additive and combustion characteristics of Albizia zygia wood-coconut-husk blends on ash yield

Abstract

Property of solid fuel is characterized based on the amount of ash yield after combustion. This study evaluates the influence of kaolin additives and combustion characteristics of Albizia zygia wood and coconut husk mixture on ash yield. D-Optimal Design under the Combined Methodology of Design Expert was employed to mix the solid fuel constituents alongside particle size in order to determine the ash yield of the mixture. The input parameters (wood, coconut husk, kaolin, and particle size) and output parameter (ash yield) of combustion process were also modeled using Artificial Neural Network (ANN). The 23 data points obtained from design of experiment were divided into training data and testing data sets in the relative proportion 9:1. A quadratic regression model (p<0.05) was obtained for the ash prediction. The optimal values established for the ash yield were wood (85 %), coconut husk (5.0 %), kaolin (10 %) and particle sizes (2.50 mm) respectively. The coefficient of determination (R2) obtained for the model is 0.8825 while the adjusted R2 is 0.8153. The ANN (R2) values for the model predictions were 0.939 for the training set and 0.926 for the testing set respectively. Thus, this study demonstrated that combustion of wood-coconut additive mixture could be efficient for energy generation.