Valuation of % metallization and % accretion formation in a rotary kiln of sponge iron process based on Fuzzy Logic Inference System

Abstract

Sponge iron making is a compound process, consisting of a sequence of activities necessitating extensive technological sustenance. An integrated fuzzy inference system model for control of sponge iron rotary kiln performance based on multi-criterion attributes is established in this study. Thus, this paper aims at improving the kiln performance by analyzing the effect of variation of input variables, such as kiln inclination, kiln rotation speed, feed flow rate of iron ore and coal on realizing product % metallization level and estimation of accretion formation inside the rotary kiln, thereby facilitates longevity of kiln life expectancy. Plant data from an operational industrial rotary kiln were used to confirm the functionality of the model. The results reflect that the best angle of inclination, kiln revolution and feed flow rate of iron ore and coal is 2.8 o , 4.8rpm, 6.4kg/s and 2.3kg/s, respectively. At these settings, the % metallization is projected as 94.8%, which is 2.93% higher as equated to the obtained industrial practice value. The reduction end temperatures obtained through industrial practice and simulation results were found to be comparable. It was also established that a % accretion value of less than 15% is possible for pressure and temperatures below 0.5mBars and 1060 o C, respectively.