Analysing blast furnace data using evolutionary neural network and multiobjective genetic algorithms

Approximately one year's operational data of a TATA Steel blast furnace were subjected to a multiobjective optimisation using genetic algorithms. Data driven models were constructed for productivity, CO₂ content of the top gas and Si content of the hot metal, using an evolutionary neural network that itself evolved through a multiobjective genetic algorithm as a tradeoff between the accuracy of training and the network complexity. The final networks were selected using the corrected Akaike information criterion. Bi-objective optimisation studies were subsequently carried out between the productivity and CO₂ content with various constraints at the Si level in the hot metal. The results indicate that a productivity increase would entail either a compromise of the CO₂ fraction in the top gas or the Si content in the hot metal. The Pareto frontiers presented in this study provide the best possible parameter settings in such a scenario.