Multi-model based PSO method for burden distribution matrix optimization with expected burden distribution output behaviors

Burden distribution is one of the most important operations, and also an important upper regulation in blast furnace ( BF ) iron-making process. Burden distribution output behaviors ( BDOB ) at the throat of BF is a 3-dimensional spatial distribution produced by burden distribution matrix ( BDM ) , including burden surface output shape ( BSOS ) and material layer initial thickness distribution ( MLITD ). Due to the lack of effective model to describe the complex input-output relations, BDM optimization and adjustment is carried out by experienced foremen. Focusing on this practical challenge, this work studies complex burden distribution input-output relations, and gives a description of expected MLITD under specific integral constraint on the basis of engineering practice. Furthermore, according to the decision variables in different number fields, this work studies optimization of BDM with expected MLITD, and proposes a multi-mode based particle swarm optimization ( PSO ) procedure for optimization of decision variables. Finally, experiments using industrial data show that the proposed model is effective, and optimized BDM calculated by this multi-model based PSO method can be used for expected distribution tracking.