Improving Blast Furnace Operations Through Advanced Analytics

Hot blast is an input to the blast furnace and is instrumental in blast furnace efficiency. The current state of stove operations is not standardized. Many times, operators based on their experience take critical decisions. To standardize the decision-making process in the most optimum way, an analytics research project with an iron and steel manufacturing industry was kick-started. The paper describes the present control system of the hot blast heating process and describes a model complementing the control system. The model is built by using K-means clustering algorithm and principal component analysis to recommend the operators the critical variable set point. The process variables in the plant are continuously changing and thus make impossible for operators to take the most optimum decision accounting all the variables. The model covers all the critical variables whether controllable or non-controllable and is aimed at increasing the heat recovery in stoves and increasing the temperature of the hot blast. The end result of the research would be to reduce the variability and increase the median temperature, eventually reducing the cost of hot metal. The dashboard displays model generated recommendations to the operators and also monitors the compliance by operators on week-by-week basis. Based on different business rules, the model is scheduled to be retuned in specified intervals which takes care of the change in efficiencies of process equipment.