Strategies for Development of Process Control Models for Hot Metal Desulfurization: Conventional and AI Techniques

Abstract

Process control models can be developed by using both conventional and AI approaches. The conventional approaches include regression models and process kinetic models, and the artificial intelligence (AI) approaches are based on artificial neural nets (ANN), genetic algorithm (GA), and fuzzy rule-based expert systems FRBES. Plant data on hot metal desulfurization, carried out by injecting calcium carbide, is analyzed to test and tune different types and combinations of models and then evaluate their relative performance. Although the control models based on process fundamentals provide a fillip to the new ideas for technological developments and improvements, the combination of conventional and AI approaches may be a better option for process control on the shop floor. It is advisable to first develop and test all models and then decide about the best strategy of using them.