Melting Time Prediction Model for Induction Furnace Melting Using Specific Thermal Consumption from Material Charge Approach

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.

Arctic summer sea ice phenology including ponding from 1982 to 2017

Information on the Arctic sea ice climate indicators is crucial to business strategic planning and climate monitoring.Data on the evolvement of the Arctic sea ice and decadal trends of phenology factors during melt season are necessary for climate prediction under global warming.Previous studies on Arctic sea ice phenology did not involve melt ponds that dramatically lower the ice surface albedo and tremendously affect the process of sea ice surface melt.Temporal means and trends of the Arctic sea ice phenology from 1982 to 2017 were examined based on satellite-derived sea ice concentration and albedo measurements.Moreover,the timing of ice ponding and two periods corresponding to it were newly proposed as key stages in the melt season.Therefore,four timings,i.e.,date of snow and ice surface melt onset(MO),date of pond onset(PO),date of sea ice opening(DOO),and date of sea ice retreat(DOR);and three durations,i.e.,melt pond formation period(MPFP,i.e.,MO–PO),melt pond extension period(MPEP,i.e.,PO–DOR),and seasonal loss of ice period(SLIP,i.e.,DOO–DOR),were used.PO ranged from late April in the peripheral seas to late June in the central Arctic Ocean in Bootstrap results,whereas the pan-Arctic was observed nearly 4 days later in NASA Team results.Significant negative trends were presented in the MPEP in the Hudson Bay,the Baffin Bay,the Greenland Sea,the Kara and Barents seas in both results,indicating that the Arctic sea ice undergoes a quick transition from ice to open water,thereby extending the melt season year to year.The high correlation coefficient between MO and PO,MPFP illustrated that MO predominates the process of pond formation.