Optimization of the process parameter of lean-grade self-reducing pellets by surface response modelling

Abstract In the Indian context, the production of steel is increasing day by day. The huge crude steel output requires enormous amounts of raw materials from the mining industry. The availability of high-grade steel-making raw materials has decreased worldwide. There are huge challenges for the metallurgist in developing a suitable process in the steel industry, which is essential due to the possible scarcity of high-grade raw materials in the future. In this present study, an appropriate process for lean-grade iron ore and non-coking coals, along with the binders, is used as self-reducing composite pellets for iron making. These composite pellets are made in different combinations, including particle sizes, different non-coking coals, and binders. Initially, the mechanical properties of composite pellets are evaluated through the shatter and abrasion index. Then, cold-bonded self-reducing pellets are directly reduced in a standard tube furnace at varying temperatures and times. The reduced samples are further characterized using X-ray diffraction, scanning electron microscopy, and chemical analysis methods. Finally, Box–Behnken Design statistical modelling based on the extent of reduction ( EOR ) is successfully utilized for optimizing the process parameters.