Advanced Process Control Of An Industrial Iron Ore Pellet Induration Process

The quality of iron ore pellets, represented in terms of parameters such as cold compressive strength (CCS) and Tumble Index (TI), has a strongly influence on the productivity and energy of reduction furnaces like the blast furnace and on the quality of hot metal or liquid iron. The main objective of pelletization operations is to produce iron ore pellets of desirable specifications and ensure high levels of plant productivity and energy efficiency. Advanced process control of an industrial iron ore pellet induration process using a soft sensor for pellet quality and fuzzy-logic based control algorithm is described in this paper. The pellet quality soft sensor consists of a rigorous mathematical model of the induration process and a statistical model for the pellet quality parameters. The mathematical model is based on the fundamental principles of heat and mass transfer between gases and pellets, kinetics of drying and condensation of moisture, combustion of coke, calcination of limestone, and melting and solidification of pellets. The predictions of wind-box temperature profiles are in close agreement with the measured profiles. The time-temperature profiles of the pellets during induration, along with raw material characteristics and induration process operating conditions, are used as inputs to the quality model to predict cold compressive strength and the Tumble Index. The predictions of the quality soft sensor along with the rules based on fuzzy-logic derived from simulation results and operator experience are then utilized to control the induration process to achieve precise control of the quality parameters and to optimize the induration process in terms of fuel efficiency and productivity. The mathematical model is also utilized as a soft sensor for temperature, moisture and composition profiles of pellets and gases. Results of implementation of this model-based control strategy on an industrial scale induration process will be presented. Keywords: iron ore, pelletization, simulation, process control, optimisation