A Comparison of Laboratory Coal Testing with the Blast Furnace Process and Coal Injection

The injection of coal through tuyeres into a blast furnace is widely adopted throughout the industry to reduce the amount of coke used and to improve the efficiency of the iron making process. Coals are selected depending on their availability, cost, and the physical and chemical properties determined by tests, such as the volatile matter content, fixed carbon, and ash content. This paper describes research comparing the laboratory measured properties of injection coals that were used over a two-month production period compared to the process variables and measurements of the blast furnace during that study period. In addition to the standard tests, a drop tube furnace (DTF) was used to compare the burnout of coals and the char properties against the production data using a range of statistical techniques. Linear regression modelling indicated that the coal type was the most important predictor of the coal rate but that the properties measured using laboratory tests of those coals were a minor feature in the model. However, comparisons of the Spearman's correlations between different variables indicated that the reverse Boudouard reactivity of the chars, prepared in the DTF from the coals, did appear to be related to some extent to the coal and coke rates on production. It appears that the constant process adjustments made by the process control systems on the furnace make it difficult to identify strong correlations with the laboratory data and that the frequency of coal sampling and the coal blend variability are likely to contribute to this difficulty.

Automated summary

The injection of coal into blast furnaces is a widely used method to reduce coke usage and improve iron making efficiency. Research shows that coal type is the most important predictor of coal rate, while laboratory-tested coal properties play a minor role in the prediction model. The reactivity of char from coal in relation to coal and coke rates during production was found to have some correlation, despite the challenges of identifying strong correlations between laboratory data and process control adjustments in the furnace.