Abstract
Process algorithm, numerical model and techno-economic assessment of charge calculation and furnace bath optimization for target alloy for induction furnace-based steelmaking is presented in this study. The developed algorithm combines the make-to-order (MTO) and charge optimization planning (COP) of the steel melting shop in the production of target steel composition. Using a system-level approach, the unit operations involved in the melting process were analyzed with the purpose of initial charge calculation, prevailing alloy charge prediction and optimizing the sequence of melt chemistry modification. The model performance was established using real-time production data from a cast iron-based foundry with a 1- and 2-ton induction furnace capacity and a medium carbon-based foundry with a 10- and 15-ton induction furnace capacity. A simulation engine (CastMELT) was developed in Java IDE with a MySQL database for continuous interaction with changing process parameters to run the model for validation. The comparison between the model prediction and production results was analyzed for charge prediction, melt modification and ferroalloy optimization and possible cost savings. The model performance for elemental charge prediction and calculation purpose with respect to the charge input (at overall scrap meltdown) gave R-squared, Standard Error, Pearson correlation and Significance value of (0.934, 0.06, 0.97, 0.0003) for Carbon prediction, (0.962, 0.06, 0.98, 0.00009) for Silicon prediction, (0.999, 0.048, 0.999, 9E -11) for Manganese Prediction, and (0.997, 0.076, 0.999, 6E -7) for Chromium prediction respectively. Correlation analysis for melt modification (after charging of ferroalloy) using the model for after-alloying spark analysis compared with the target chemistry is at 99.82%. The results validate the suitability of the developed model as a functional system of induction furnace melting for combined charge calculation and melt optimization Techno-economic evaluation results showed that 0.98% - 0.25% ferroalloy saving per ton of melt is possible using the model. This brings about an annual production cost savings of 100,000 $/y in foundry A (medium carbon steel) and 20,000 $/y in foundry B (cast iron) on the use of different ferroalloy materials.
Highlights
Melting as a major operation in the foundry is carried out by charging commercially pure metals, external and internal scrap, and additives to achieve a target alloy composition
Through detailed modelling of the system-level parameters required for induction furnace melting of scrap, this study presents a parametric and validation study to understand the charge balancing, furnace bath melt optimization, and ferroalloys savings possible with induction furnace melting
The numerical model was evaluated first for charge calculation predictions using the simulation engine with the results presented in supplementary material 3 and 4 for foundry A and foundry B respectively
Summary
Melting as a major operation in the foundry is carried out by charging commercially pure metals, external and internal scrap, and additives to achieve a target alloy composition. Refining is done to remove the deleterious gases and elements from the molten metal through material addition to bring the final tap chemistry within a specific range set by internal standard and/or industry [3]. Cast Iron and Steel scraps are the most important raw material in the foundry shop where cast products of the scrap melts are produced contributing about 60% to 80% of the total production costs [4] [5]. Cast products ranging from simple machine parts to special steel alloys are produced from an intelligent campaign in the foundry shop through programming intelligence [6]. The extent to which the scrap mix for refining can be optimized, and the degree to which the melting operation can be controlled and automated to achieve the right chemistry of melt in the foundry is limited by the knowledge of the properties of the scrap and other raw materials in the charge mix [7] [8] [9]
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