Cement production is one of the most energy-intensive manufacturing industries, and the milling circuit of cement plants consumes around 4% of a year's global electrical energy production. It is well understood that modeling and digitalizing industrial-scale processes would help control production circuits better, improve efficiency, enhance personal training systems, and decrease plants' energy consumption. This tactical approach could be integrated using conscious lab (CL) as an innovative concept in the internet age. Surprisingly, no CL has been reported for the milling circuit of a cement plant. A robust CL interconnect datasets originated from monitoring operational variables in the plants and translating them to human basis information using explainable artificial intelligence (EAI) models. By initiating a CL for an industrial cement vertical roller mill (VRM), this study conducted a novel strategy to explore relationships between VRM monitored operational variables and their representative energy consumption factors (output temperature and motor power). Using SHapley Additive exPlanations (SHAP) as one of the most recent EAI models accurately helped fill the lack of information about correlations within VRM variables. SHAP analyses highlighted that working pressure and input gas rate with positive relationships are the key factors influencing energy consumption. eXtreme Gradient Boosting (XGBoost) as a powerful predictive tool could accurately model energy representative factors by R-square ever 0.80 in the testing phase. Comparison assessments indicated that SHAP-XGBoost could provide higher accuracy for VRM-CL structure than conventional modeling tools (Pearson correlation, Random Forest, and Support vector regression.

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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.

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.

This paper presents an experimental approach to measure and model the residence time distribution of vertical roller mill in cement clinker grinding circuit. The dispersion model, tank-in-series model, and a perfect mixer with a bypass model were employed to describe the residence time distribution. A perfect mixer with a bypass model was found to a tolerable fitting to describe the RTD of solids in VRM. The result shows 43% of the fine fraction of fresh feed as an existing bypass without spending adequate time within the VRM. The result also shows that the grinding occurs in 54% of the table surface and about 46% of the table surface can be called the dead zone.