Implementation of Taguchi method, ANOVA and regression analyses to enhance char yield by carbonization in lignite-biomass blended

Abstract

ABSTRACT Energy is a basic need for modern societies. Renewable energy refers to producing and using energy without harming the environment and without consuming resources. The transition to renewable and sustainable energy can provide many environmental, economic and social benefits. Academic research plays an important role in achieving this transition. Sustainable energy is critical to protecting the environment, promoting economic development and supporting social improvements. The transition to sustainable energy is a necessity for the future of humanity. For this purpose, in this study, lignite and biomass (apricot kernel shell) carbonization experiments were carried out under different conditions. Mixing ratio (Lignite/Biomass (w/w 1:1, 1:2, 1:3)), temperature (400°C, 500°C, 600°C) and heating rate (10°C/min, 30°C/min, 50°C/min) were determined as variable parameters. Taguchi’s experimental design was used to optimize the parameters. The orthogonal array design plan was determined as L9 according to the available variables (33 x 33). Since the study aimed to obtain clean solid fuel, char yields were taken into account from the results. Signal-to-noise (S/N) ratios were calculated based on the larger the better condition. As a result of the carbonization experiments performed under Taguchi optimization conditions, it was determined that the S/N ratio was 31.73 and the char yield was 40.19%. Since the estimated char yield with the Taguchi method was 38.85%, it was concluded that the optimization was achieved with 96.67% accuracy. The test parameters satisfying these conditions were determined as 1:1 lignite/biomass mixing ratio, 400°C temperature and 30°C/min heating rate. As a result of the ANOVA analysis, it was seen that the heating rate did not have a significant effect on these parameters. In contrast, the temperature and mixing ratio were important variables. Also, regression analysis (linear and quadratic) was used in this study to calculate the equations for the prediction of char yield. It was concluded that the linear regression model was more successful in estimating the char yield.