designing a predictive algorithm based on fuzzy logic inorde to determine the bond work index of iron ore from its mineralogical composition

This study introduces a fuzzy logic-based predictive algorithm for estimating the bond work index (wi) of iron ore using its mineralogical composition. the input variables for the model include mineral abundance, iron content (fe), hematite-to-magnetite ratio (feo), silica content (sio2), phosphorus (p), and sulfur (s). the algorithm leverages a fuzzy inference system (fis) with if-then rules derived from experimental data to establish relationships between the input variables and the bond work index. the fis processes these inputs and generates fuzzy outputs, which are then defuzzified to provide crisp predictions. the approach is designed to address challenges associated with conventional methods for measuring the bond work index, which are often time-consuming, costly, and require specialized equipment. by integrating expert knowledge with experimental data, the fuzzy model offers a reliable and efficient alternative that reduces dependency on traditional testing while effectively managing uncertainty and nonlinear relationships. the proposed method was evaluated using statistical metrics such as the correlation coefficient, mean square error, and mean absolute error. the results demonstrate a high degree of accuracy and a strong correlation between the predicted and measured values, validating the model s robustness and reliability. the findings also highlight the model’s potential for application across varying mineralogical conditions, enhancing the efficiency of mineral processing operations. by optimizing grinding processes and reducing energy consumption, the fuzzy logic-based approach proves to be a valuable tool for the mining industry, offering a cost-effective solution for predicting the bond work index.