In a journey of centuries, the ironmaking blast furnace has gone through many technological changes, which made it a fit-for-purpose till today. However, for the first time in history, its existence is being challenged by other iron making processes, due to its high carbon footprint compared to others. Many researchers are working on adapting the existing blast furnace process to meet the new challenges. Modelling has played a crucial role in new adaptations of the process. The present review has been dedicated to historical developments of blast furnace modelling. The models are broadly classified into comprehensive, zone-specific, and data-driven models. The comprehensive models are further classified into lumped models, 1-D, 2-D and 3-D steady state and transient models, and CFD-DEM models. For the purpose of brevity, the zone-specific models are exemplified by burden distribution models only. Finally, developments in data-driven models are discussed, before some general conclusions are presented.

No abstract

Effective control of a blast furnace (BF) process requires accurate estimates of key process indicators (KPIs), namely, productivity, coke rate, direct reduction per cent, adiabatic flame temperature, bosh gas volume and top gas utilization. Some of these KPIs are obtained directly from the measurements, and some are derived by carrying out material and energy balances on measurements of different feeds, their compositions and temperatures. Due to errors in the measurements, the estimates of the KPIs can be inconsistent or misleading, which may result in misinterpretation of the current state of the BF process. Hence, it is necessary to reconcile the measurement data before these are used either for interpreting the current furnace state directly or as an input to other models. In the proposed methodology, data reconciliation and gross error detection techniques are used to improve the accuracy of the estimates of process variables and parameters, by ensuring that they satisfy process constraints such as elemental balances of iron, nitrogen, carbon, oxygen and hydrogen. Since the BF is a fed‐batch process, a customized version of these techniques has been developed and applied real time to an operating BF. The method is shown to be useful in deriving consistent estimates of the hot metal production rate, identifying gross errors in the online gas analyser and for estimating unmeasured parameters, such as top gas flow rate, its moisture concentration and calorific value which are useful for the purpose of stove heating in the downstream process.

No abstract