Ventilation Prediction for an Industrial Cement Raw Ball Mill by BNN-A Conscious Lab Approach

In cement mills, ventilation is a critical key for maintaining temperature and material transportation. However, relationships between operational variables and ventilation factors for an industrial cement ball mill were not addressed until today. This investigation is going to fill this gap based on a newly developed concept named conscious laboratory (CL). For constructing the CL, a boosted neural network (BNN), as a recently developed comprehensive artificial intelligence model, was applied through over 35 different variables, with more than 2000 records monitored for an industrial cement ball mill. BNN could assess multivariable nonlinear relationships among this vast dataset, and indicated mill outlet pressure and the ampere of the separator fan had the highest rank for the ventilation prediction. BNN could accurately model ventilation factors based on the operational variables with a root mean square error (RMSE) of 0.6. BNN showed a lower error than other traditional machine learning models (RMSE: random forest 0.71, support vector regression: 0.76). Since improving the milling efficiency has an essential role in machine development and energy utilization, these results can open a new window to the optimal designing of comminution units for the material technologies.

Automated summary

Ventilation is crucial for maintaining temperature and material transportation in cement mills. A study using a concept called conscious laboratory (CL) and a boosted neural network (BNN) found that mill outlet pressure and separator fan ampere were the most important variables for predicting ventilation in an industrial cement ball mill. BNN was also shown to accurately model ventilation factors based on operational variables with a low error rate.