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.

Using a software-based experiment design, the application of the leaching process for the extraction of manganese from Zinc Plant Slag (ZPS) was investigated. In this study, the effect of different parameters, i.e., H2SO4 concentration, pulp density, agitation rate, temperature and reaction time, was investigated. Response Surface Methodology (RSM) based on the Central Composite Design (CCD) has been implemented to consider the main parameters. A hydrometallurgical route to manganese silicate from spent zinc plant residue has been proposed in this investigation. Based on the investigation, Mn can be extracted from ZPS in sulfuric acid without any oxidant agents. The results showed that the optimum conditions of this study are an H2SO4 concentration of 2 mol/L and a solid/liquid ratio of 0.07 g/mL at 50°C for 150 min and an agitation speed of 1000 rpm. A manganese leaching efficiency higher than 83% is reached under these conditions, with a corresponding 22% iron, 23% lead, 68% zinc and 65% aluminum.