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A Thermodynamic Model of Phosphate Capacity for CaO-SiO2-MgO-FeO-Fe2O3-MnO-Al2O3-P2O5 Slags Equilibrated with Molten Steel during a Top–Bottom Combined Blown Converter Steelmaking Process Based on the Ion and Molecule Coexistence Theory

“…[22][23][24][25][26][27][28][29][30] To clarify the difference between phosphate capacity and phosphate capacity index, the relationship between phosphate capacity and phosphate capacity index was first deduced, the relationship between the phosphate capacity or phosphate capacity index and the phosphorus distribution ratio L P ¼ L 0 P ¼ ðpct P 2 O 5 Þ ½pct P] 2 ; or L 00 P ¼ ðpct PO 3À 4 Þ ½pct P]; or L 000 P ¼ ðpct P 2 O 5 Þ=½pct P] has been also established. The predicted phosphate capacity or phosphate capacity index of CaO-SiO 2 -MgO-FeOFe 2 O 3 -MnO-Al 2 O 3 -P 2 O 5 slags at the steelmaking endpoint during a top-bottom combined blown converter steelmaking process by the developed IMCT C PO 3À 4 prediction model was compared with the measured phosphate capacity or phosphate capacity index, as well as the calculated phosphate capacity or phosphate capacity index by other models, such as Selin's model, [10] Mori's model, [11] Suito's model, [6,12] and Young's model. [13] The contribution ratio of each structural unit containing P 2 O 5 to the total phosphate capacity C PO 3À 4 or phosphate capacity index C PO 3À 4 ;index of the slags was also determined from the developed IMCT C PO 3À 4 model.…”

“…Large amounts of phosphate capacity C PO 3À 4 data for various slags have been measured based on slag-gas equilibrium reaction since the 1970s after it was proposed by Wagner [1] ; meanwhile, a lot of phosphate capacity index C PO 3À 4 ;index data have been also measured based on slag-metal equilibrium reaction, although some researchers did not distinguish the concept of phosphate capacity C PO 3À 4 and phosphate capacity index C PO 3À 4 ;index . [4][5][6][7][8][9] Some prediction models for calculating phosphate capacity C PO 3À 4 and phosphate capacity index C PO 3À 4 ;index had been developed, such as Selin's model [10] for CaO-SiO 2 -CaF 2 slags, Mori's model [11] for CaO-MgO-SiO 2 -Fe t O slags, Suito's model [6,12] for CaO-MgO-Fe t O-SiO 2 slags, Young's models [13] for CaO-SiO 2 -MgO-MnO slags, and Kunisada's model [14] for soda-based slags. It should be emphasized that the original meaning of the applied C P 3À ;index ¼ ðpct P)= ½pct P] Á ½pct O] 5=2 in Suito's model as well as in Young's model is different from the defined phosphate capacity C PO 3À 4 by Wagner [1] or phosphate capacity index C PO 3À 4 ;index by Yang et al [2,3] All these prediction models were developed from mathematical regression of experimental data rather than based on the dephosphorization reaction according to metallurgical physicochemistry.…”

Phosphate, Phosphide, Nitride and Carbide Capacity Predictions of Molten Melts by Using an Artificial Neural Network Approach

“…Some experimental C PO 4 3− values in Table 2, not only ternary but also of lower sub-systems neighbor to liquidus region were taken into account for each calculation. The experimental C PO 4 3− values of the melt compositions which are not in liquid region were neglected to prevent erroneous C PO 4 3− results. The iso-phosphate capacity contours were inserted to phase diagrams which were generated by FactSage 6.4 using "Phase Diagram" module.…”

“…The results showed that the values obtained by neural network computation have lower RMSE values and higher estimation precision than corresponding statistical and thermodynamic models. It was also constructed iso-phosphate counters on ternary phase diagrams of CaO-CaF 2 -Al 2 O 3 slag system at 1 773 K, establishing a link among experimental C PO 4 3− values of molten ternary and lower sub-systems. It can be concluded that artificial neural network based computation is a very useful technique for predicting impurity capacity values in molten melts, when other empirical/theoretical models are not inadequate for their Ci estimations.…”

“…3) Mori used the optical basicity concept in their formula for CaO-MgO-SiO 2 -Fe t O slags at 1 873 K. 4) Kobayashi and Kodama enhanced a temperature-dependent formula for phosphate capacity prediction using the same concept for MnO-Fe t O-CaO-MgO-SiO 2 slags. 5) Apart from above mentioned models, Yang et (Received on May 5, 2015; accepted on November 16, 2015) In the present study, the impurity capacities (Ci) of phosphate, phosphide, nitride and carbide in binary and multi-component molten melt systems at different temperatures were estimated using the artificial neural network approach.…”