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Screening of coal is one of the processes carried out to produce clean coal suitable for the blast furnace. In this work, the screening of moist coal was carried out for different angles of the screen and frequencies. A 2 mm screen perforation was used to separate undersize coal of size +1 mm-2 mm from the +1 mm-3 mm coal samples. For each experimental condition, the screening efficiency was calculated. Maximum screening efficiency of 85.96%, 75.64%, and 63.46% was obtained at 4%, 6%, and 8% moisture content, respectively. As the moisture content of coal increases, the efficiency minimizes due to high screen clogging. After determining the screening efficiency, prediction was carried out using regression modeling. In this work, linear and second-order polynomial regression modeling was utilized to develop a prediction model for the experimental values. From the results, it was clear that the polynomial regression model has high regression coefficient (R 2 ) percentage and low  P -value in comparison with the linear regression model. After prediction, validation was carried out on the best fit model. The value of Variance Account For (VAF), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) was in the acceptable range, which shows that the developed model was most effective.

Experimentation and statistical prediction of screening performance of coal with different moisture content in the vibrating screen