Real-Time Power Prediction of Ball Mill Using Explicit Mathematical Formulas
Mengtao Wang, Bo Cheng, Qingfei Xiao, Weitao Yu, Jiangping He, Jiegong Dong, Qin Wang, Saizhen Jin, Yanfei Chen
Source abstract
Grinding accounts for a relatively high proportion of the total electrical energy consumption in a mineral processing plant, and research on mill operating power constitutes a key basis for realizing intelligent control and energy conservation of the grinding process. Based on the SAB (semi-autogenous grinding followed by ball mill) circuit of a mineral processing plant, this study developed three sets of explicit mathematical models for ball mill power prediction using multivariate adaptive regression splines (MARS): an initial version containing 105 terms, an advanced version with 21 terms, and a reduced version with 11 terms. The prediction results show that the reduced version, using only 11 terms, attains a coefficient of determination R2 = 0.7989 (MAE = 8.96 kW, MAPE = 0.41%), comparable to the accuracy of the initial version (R2 = 0.8166). Moreover, the reduced model can be directly embedded into a PLC or an Excel spreadsheet to achieve mill power prediction at the millisecond scale. The proposed explicit model enables real-time prediction of ball mill operating power, allowing the plant to adjust mill operation promptly and reduce operating costs; it also holds important significance for the intelligent control of the grinding process.
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