Predictive models and operation guidance system for iron ore pellet induration in traveling grate–rotary kiln process. (4th August 2015)

• Record Type: Journal Article
• Authors: Fan, Xiao-hui; Yang, Gui-ming; Chen, Xu-ling; Gao, Lu; Huang, Xiao-xian; Li, Xi
• Is Part Of: Computers & chemical engineering. Volume 79(2015)
• Page Start: 80
• Page End: 90
• Publication Date: 2015-08-04
• Subjects: Predictive models -- Iron ore pellets -- Traveling grate -- Rotary kiln -- Operation guidance; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2015.04.035
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Coupled predictive models of pellet temperature in traveling grate–rotary kiln were built. Prediction of pellet strength was realized using the calculated time–temperature profile. Models were validated by industrial data at steady state. An operation guidance system was developed to optimize grate kiln process. Abstract: Thermal state of iron ore pellets in industrial traveling grate–rotary kiln process cannot be revealed straightforward, which is unfavorable for field operations. In this study, coupled predictive models of pellet thermal state within traveling grate and rotary kiln were established. Based on the calculated temperature profiles, predictive model of pellet compression strength was also established to assist in process optimization. All the models proposed were validated by the industrial data collected from a domestic plant, and the results show that grate model possesses a high accuracy, kiln model is considered to be accurate to within 10–15% of actual values, and strength model can identify the variation of pellet strength caused by the thermal changes. The proposed models were embodied into an operation guidance system developed for a large-scale pelletizing plant, and the system running results illustrate that the predictive models and expertise rules established can optimize the process very well.