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 ...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% ferroall展开更多
A system-level evaluation was used to analyze the induction furnace operation and process system in this study. This paper presents an investigation into the relationship between the instantaneous chemical composition...A system-level evaluation was used to analyze the induction furnace operation and process system in this study. This paper presents an investigation into the relationship between the instantaneous chemical composition of a molten bath and its energy consumption in steelmaking. This was evaluated using numerical modelling to solve for the estimated melting time prediction for the induction furnace operation. This work provides an insight into the lowering of energy consumption and estimated production time in steelmaking using material charge balancing approach. Enthalpy computation was implemented to develop an energy consumption model for the molten metal using a specific charge composition approach. Computational simulation program engine (CastMELT) was also developed in Java programming language with a MySQL database server for seamless specific charge composition analysis and testing. 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. Using parameter fitting techniques on the measured operational data of the induction furnaces at different periods of melting, the results from the model predictions and real-time melting showed good correlation between 81% - 95%. A further analysis that compared the relationship between the mass composition of a current molten bath and melting, time showed that energy consumption can be reduced with effective material balancing and controlled charge. Melting time was obtained as a function of the elemental charge composition of the molten bath in relation to the overall scrap material charge. This validates the approach taken by this research using material charge and thermodynamic of melting to optimize and better control melting operation in foundry and reduce traditional waste during iron and steel making.展开更多
文摘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% ferroall
文摘A system-level evaluation was used to analyze the induction furnace operation and process system in this study. This paper presents an investigation into the relationship between the instantaneous chemical composition of a molten bath and its energy consumption in steelmaking. This was evaluated using numerical modelling to solve for the estimated melting time prediction for the induction furnace operation. This work provides an insight into the lowering of energy consumption and estimated production time in steelmaking using material charge balancing approach. Enthalpy computation was implemented to develop an energy consumption model for the molten metal using a specific charge composition approach. Computational simulation program engine (CastMELT) was also developed in Java programming language with a MySQL database server for seamless specific charge composition analysis and testing. 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. Using parameter fitting techniques on the measured operational data of the induction furnaces at different periods of melting, the results from the model predictions and real-time melting showed good correlation between 81% - 95%. A further analysis that compared the relationship between the mass composition of a current molten bath and melting, time showed that energy consumption can be reduced with effective material balancing and controlled charge. Melting time was obtained as a function of the elemental charge composition of the molten bath in relation to the overall scrap material charge. This validates the approach taken by this research using material charge and thermodynamic of melting to optimize and better control melting operation in foundry and reduce traditional waste during iron and steel making.
