Model predictive control for improving waste heat recovery in coke dry quenching processes

Abstract

CDQ (coke dry quenching) is a widely used method for recovering waste heat in the steel industry. We have developed a novel, data driven modeling approach and model based control for a CDQ unit to increase steam generation in a cogeneration system. First, the correlation between steam generation and TCGB (the temperature of circulation gas entering the associated boiler) was confirmed. Subsequently, a nonlinear variable selection method was employed to build models of TCGB and the carbon monoxide concentration of the circulation gas. The models obtained were implemented to achieve MPC (model predictive control) for regulating the supplementary gas to maximize steam generation in an existing steelmaking plant. Upon comparison of the original process and the proposed modified operation, the effectiveness of the implementation of MPC was justified. The results showed that steam generation was increased by 7%. In our approach, the large amount of available operational data stored electronically was used to establish the models. Modification of the established system is not required. Taking into account that no capital investment is required, the process improvement is remarkable in terms of its return on investment.