Investigation of optimum conditions in coal–oil agglomeration using Taguchi experimental design

Abstract

The present study was undertaken to optimize the oil agglomeration process parameters for maximum recovery of coal fines using analysis of mean (ANOM) statistical approach based on the Taguchi parameter design methodology. The various operational parameters considered during the current study were the type of coal, type of oil, coal particle size and pulp density. The study reported a maximum recovery of 91.03% under the following optimum conditions: low ash high sulphur coal, Karanja oil, coal particle size of +75–200μm and pulp density of 3% (wt./vol.). The percentage contribution of each process parameter towards the agglomerate yield determined using Analysis of Variance (ANOVA) approach was found to be of the following order: coal particle size (55.35%)>type of coal (17.84%)>pulp density (16.50%)>type of oil (8.41%). The most influential process parameter appeared to be coal particle size which has been the primary criteria used for selection of particular process for coal washing. Linear regression analysis carried out using the SPSS 19.0 statistical software further supported the same. Further, a mathematical model was also developed to predict the agglomerate yield by oil agglomeration under the given set of boundary conditions. The experimentally obtained yields were in close agreement with the predicted yield of the model. The agglomerate yield (91.03%) obtained during the confirmation experiment carried out under optimum conditions was much higher than that observed in all the test runs and thereby, the authenticity of optimization was checked.