An Artificial Intelligence Method for Energy Efficient Operation of Blast Furnace in Steel Making Industry

Abstract

The iron melting furnaces are the most energy-consuming equipment of the iron and steel industry. The energy efficiency of the furnace is affected by process conditions such as the inlet temperature, velocity of the charge, and composition of the charge. Hence, optimum values of these process conditions are vital in the efficient operation of the furnace. Computational methods have been very helpful in the optimum design and operation of process equipment. In this study, a first principle (FP) model was developed for an iron-making furnace to visualize its internal dynamics. To minimize the large computational time required for the FP-based analysis, a data-based model, i.e., Artificial Neural Networks (ANN), is developed using data extracted from the FP model. The ANN model was developed using data sets comprised of the values of temperature of the charge and gasses, velocity, concentration of the oxygen, pressure, airflow directions, energy and exergy profiles, and overall exergy efficiency of the furnace along with its height. The ANN model was highly accurate in prediction and is suitable for real-time implementation in a steel manufacturing plant.