Optimization studies on coal–oil agglomeration using Taguchi (L16) experimental design

Abstract

Coal–oil agglomeration has been a prominent fine coal processing technique, which involves agitation of coal–water slurries in presence of immiscible oil forming coal–oil agglomerates. The present study investigates this technique using Taguchi (L16) design of experiments (DoE) methodology. Further, the process parameters like oil dosage, agitation speed, agglomeration time, pH and temperature were optimized using Analysis of Mean (ANOM) statistical approach and the percentage contribution of each of these parameters towards organic matter recovery (OMR) was determined by Analysis of Variance (ANOVA) statistical approach.The optimum conditions for maximum OMR (91.38%) were identified as oil dosage of 30%, agitation speed of 2500rpm, agglomeration time of 2min, pH of 12 and temperature of 30°C. The contribution of each of the above process parameters towards the OMR was found to be in the following order: pH (30.38%)>agitation speed (23.06%)>oil dosage (20.63%)>agglomeration time (13.72%)>temperature (10.61%). Multiple linear regression analysis carried out using SPSS 19.0 ascertained a similar order of influence on the OMR. The highest contribution of suspension pH further establishes the fact that oil agglomeration process is a surface property based technique, as pH affects the surface charge on coal/mineral matter/oil droplets. A mathematical model was also developed using SPSS 19.0 to predict the OMR by oil agglomeration under the given set of experimental conditions. The developed model could predict the OMR to a significant extent, which can be further improved by a DoE technique of higher order.