Abstract
This study investigates the optimization of fine coal-oil
agglomeration by focusing on critical parameters: pulp density, oil dosage,
pH, agglomeration time, agitation rate, and oil type. The goal is to improve
coal quality, specifically reducing ash value and moisture, while maximizing
calorific value (CV), fixed carbon (Fixed C), and minimizing sulphur
content. Using a combination of Response Surface Methodology (RSM),
random forest, and decision trees, the study identifies optimal conditions for
agglomeration. A pulp density of 20%wt yielded a relative 18% ash
reduction and a 0.5% sulphur reduction. An oil dosage of 15% was found to
be efficient, significantly enhancing the calorific value to 27.5 MJ/kg and
fixed carbon to 48% and as dry (ad) moisture basis of 5.58%. The optimal
pH of 4 preserves electrostatic properties crucial for effective
agglomeration, while an agitation rate of 2800 rpm and agglomeration time
of 12 minutes further improve coal recovery. The incorporation of advanced
modelling techniques such as random forest and decision trees enhance
predictive accuracy, ensuring reliable optimization of these parameters.
These findings underline the potential of this approach to improve coal
recovery processes. They also promote sustainable practices in the coal
industry.