Genetic Algorithm‐Based Variable Selection in Prediction of Hot Metal Desulfurization Kinetics

Sulfur is considered as one of the main impurities in hot metal and hot metal desulfurization is often carried out using injection of fine‐grade desulfurization reagent. The selection of variables used for predicting the course of hot metal desulphurization requires expert knowledge. However, it is difficult to model the complex interactions in the process and to evaluate a high number of possible variable subsets with manual variable selection techniques. As the amount of data gathered from the process increases, manual variable selection becomes too time‐consuming and might lead to a suboptimal prediction model. The objective of this work is to execute an automatic variable selection procedure for prediction of hot metal desulfurization based on an industrial scale data set. The variable selection problem is formulated as a constrained optimization problem, in which the objective function is formulated based on repeated leave‐multiple‐out cross‐validation. The implemented solution strategy is a binary‐coded genetic algorithm (GA). By making use of the developed model, the effect of the main production variables on the rate and efficiency of primary hot metal desulfurization is quantified. The variables related to properties of the reagent and the injection parameters were found to be of great importance. This study presents an automatic variable selection procedure for prediction of hot metal desulfurization kinetics. The variable selection problem is formulated as an optimization problem, in which the objective function is repeated leave‐multiple‐out cross‐validation. The chosen solution strategy for the problem is binary‐coded genetic algorithm. The implemented strategy is found to be a robust and fast method for prediction model identification based on noisy dataset.