The operational stability of the blast furnace is highly dependent upon the quality of the raw materials and operating conditions. Several problems arise in blast furnace where raw materials quality is deteriorated leading to the higher fuel consumption and increased hot metal production cost. This in turn disturbs the thermal stability of the blast furnace. The present paper is related to a system for optimizing fuel consumption rate in a blast furnace. The method comprises generating a visualization of a blast furnace. Further, identifying a reference batch of the burden which produced hot metal of desired temperature. Further, the model provides coal rate predictions for the operators, and thus prevents the large variation in the thermal conditions of the blast furnace and provides high levels of operational stability. Current prediction model considers the real-time working state of BF and calculates the fuel requirement of the furnace thereby predicting the deviation in fuel rate from normal operating value and pinpoints the process and raw material parameters causing the deviation. Moreover, the HMT is achieved by the batch of the burden whose chemistry is tracked from the supply to the consumption of the raw materials.