Operational data of BOF and the slag samples for different starting conditions of phosphorus (0 . 06-0 . 26%P) and silicon content (0 . 3-1 . 2%Si) of hot metal have been analysed. The contribution of parameters which are well known to affect phosphorus distribution at tap, such as basicity, temperature, FeO content of slag, slag mass etc., is investigated through models of the ionic theory of slag, optical basicity, regular solution approach, and molecular theory of slag. The best overall results are obtained by the model based on the molecular theory of slag in which several operational parameters are also incorporated. Investigations of different slag samples, based on optical, SEM, EPMA and X-ray studies, reveal the effect of MgO and Al 2 O 3 on slag morphology and phosphorus distribution in different phases. It is important to consider the phosphorus distribution ratio in the solid and liquid part of the slag. The solid part of the slag, which is mostly dicalciumsilicate, can contain up to 5% phosphorus. The phosphorus content of the liquid part of the slag may depend upon the phosphorus content of the hot metal or the phosphorus load of the slag. It is found that incorporation of the effect of dicalciumsilicate in the model improves the accuracy of prediction. For better process control the addition of iron ore towards the end of the blow must be avoided while treating high phosphorus hot metal or during the production of ultralow phosphorus steels.

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The melting of steel scrap in high temperature liquid iron melt is investigated by conducting cold model experiments of the melting of ice sample of different geometries and sizes in an argonstirred vessel containing water. The melting process of ice samples is observed using a highspeed camera. Design of experiments is based on similarity criteria. The relationships between non-dimensional groups related to heat transfer (Nu, Re, Pr, and Gr) are derived for different experimental conditions. The results are compared with those reported in the literature. The heat transfer coefficient is estimated as a function of mixing power and is found to be in good agreement with the calculated values obtained by using reported relationships in literature.

Powder consumption, Q s , is an important process variable in the continuous casting of steel but there is no agreement on the factors affecting Q s . This study seeks to identify those factors which have an effect on Q s . A large database containing powder consumption values and casting conditions, mould flux and steel composition data from a large number of steelworks has been analysed using statistical techniques to identify those factors which have significant effect on powder consumption. The following parameters, in decreasing order, were found to be significant (at the 95% confidence level): casting speed, viscosity, stroke length, oscillation frequency and break temperature.

Conventional models for prediction of silicon content of blast furnace hot metal are briefly reviewed. Four different artificial neural net (ANN) models, namely, back propagation algorithm (SPA), dynamic learning rate algorithm, functional link network (FLN) and fuzzy neural network (FNN), are trained and tested on operational data from blast furnace (BF1) at Visakhapatnam Steel Plant. FNN can predict silicon mass content of hot metal with a standard' error (actual versus predicted) of 0.09% and correlation coefficient Ofre.86; standard back propagation predicts with a standard err,orof 0.08 % and correlation coefficient of 0.79. ( Vorausberechnung desSlIIclumgehaltes 1m f10sslgen Rohelsen mit kOnstllchen neuronaienNetzen. Herkommliche Modelle zur Vorausberechnung des Siliciumgehalts lrn Hochofen-Roheisen werden kurz angerissen. Vier verschiedene kOnstliche neuronale Netzmodelle (ANN) werden trainiert und mit Betriebsdaten des Hochofens der Visakhapatnam Stahl getestet. Hierzu gehOren der ROckrechnungsalgorithmus BPA, der dynamische Lerngeschwindigkeitsalgorithmus, das FLN (Functional Link Netzwerk) sowie das Fuzzy Neuronale Netzwerk (FNN). Letzteres kann den Siliciummassengehalt im Roheisen mit einem Standardfehler (tatsachlicher : berechneter Wert) von 0,09% und einem Korrelationskoeffizienten von 0,86 vorausberechnen. Mit dem ROckrechnungsalgorithmus kommt man zu einem Standardfehler von 0,08% bzw. einem Korrelationskoeffizienten von 0,79.