Dynamic quality prediction and control in rotary sponge iron kilns

Abstract

The quality of sponge iron produced in the coal-fired rotary kilns at TATA Steel Long Products Limited (TSLPL) is permitted to vary in a small window of 80-83% Fe. In order to adhere to this window, the expected quality in the next hour is predicted, and appropriate actions are taken if changes or rates of change that take place are not in the right direction. Airflow rate through the primary air blower is controlled constantly. For quality prediction and control purposes, the One Step Ahead Adaptive Control and Prediction Algorithm (OSAA) and Grey (1,1) prediction algorithm have been tailored and tuned. The working principle of OSAA and Grey(1,1) are described, and their accuracy is compared. The results show that OSAA performs better than the Grey(1,1). The OSAA is therefore used to predict the changes required in volume of air blown through the primary air blower and also the amount of injection coal. An expert control system has been developed based on allowable windows of control variables.

Shibu Meher

PhD Scholar in Materials Research

My research interests include Material Physics, Computational Sciences, Artificial Intelligence and Quantum Computing.