Statistical Model of The Postcombustion Subprocess in an Oven of Multiple Hearth Furnace

Abstract

were achieved through experimental identification, for mean square fit values between 0.72 and 6.1. Also defined as input variables: the air flow to hearths four and six, and as output variables: the temperature corresponding to these hearths. Montero [4], obtained dynamic mathematical models, with adjustment between 62 and 72%, which characterize the reduction furnaces of the company in question; where they were selected as input variables: the flow of ore fed to the furnace; Air flow to hearths four and six. As output variables: temperature of these hearths and concentration of residual carbon monoxide. To design an effective control strategy for the post-combustion subprocess, it is necessary to know the behavior of the variables in different situations and to obtain a process model. The objective of the work is to obtain a statistical model that represents the behavior of the post-combustion thread. Abstract Complex multivariable processes are generated in multi-hearth furnaces, and their modeling contains a high index of uncertainty. The main variables that characterize the post-combustion subprocess were identified and data were taken that comprise a period of three months of operation of the installation, to which a regression analysis was carried out step by step backwards. This analysis allowed us to determine that the linear correlation coefficient for hearth temperature four was 0.79 and 0.65 for hearth temperature six, in addition to identifying the independent variables that most influence these process output variables.