Application of Neural Net Modeling and Inverse Control to the Desulphurization of Hot Metal Process

Optimization techniques are powerful in determining inputs to a process that will drive its output to a desired target. Thisinverse control problem reduces to minimizing a positive cost function that measures the difference between the output andits target value. In this paper, we present a method for inverse control that uses a combination of artificial neural net modelingand optimization. We apply this method to the desulphurization of hot metal process. In this steel industry application, thesulphur content of hot metal, obtained at the end of calcium carbide powder injection into 400 ton torpedo ladles is predictedas a function of hot metal weight, treatment time, initial sulphur content, gas flow rate, and powder injection rate. Based onthe prediction model, the optimization algorithm coordinates the five inputs or part of them to achieve a desired sulphurcontent in the hot metal.