Stable operation of submerged-arc furnaces producing high-carbon ferromanganese (HCFeMn) and silicomanganese (SiMn) requires tapping of consistent amounts of liquid slag and metal. Minimal effort to initiate and sustain tapping at reasonable rates is desired, accommodating fluctuations in especially slag chemical composition and temperature. An analytical model is presented that estimates the tapping rate of the liquid slag-metal mixture as a function of taphole dimensions, coke bed particulate properties, and slag and metal physicochemical properties with dependencies on chemical composition and temperature. This model may be used to evaluate the sensitivity to fluctuations in these parameters, and to determine the influence of converting between HCFeMn and SiMn production. The model was applied to typical HCFeMn and SiMn process conditions, using modeled slag viscosities and densities. Tapping flow rates estimated were comparable to operational data and found to be dependent mostly on slag viscosity. Slag viscosities were generally lower for typical SiMn slags due to the higher temperature used for calculating viscosity. It was predicted that flow through the taphole would mostly develop into laminar flow, with the pressure drop predominantly over the coke bed. Flow rates were found to be more dependent on the taphole diameter than on the taphole length.

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The modeling of thermochemical properties is important in studying the physical behavior of slag in the operation of pyrometallurgical smelters. To study the flow of slag through a submerged-arc furnace (SAF) taphole, knowledge of thermochemical properties such as viscosity, thermal conductivity, density and heat capacity are required. In literature various models exist for silicate slags that enable thermochemical properties to be predicted as functions of chemical composition and temperature. This paper reports on the application of models in the CaO-MnO-SiO 2 -Al 2 O 3 -MgO slag system to be used in future CFD modeling of slag tapped from SAFs producing high-carbon ferromanganese (HCFeMn) or silicomanganese (SiMn). FactSage 6.2 is used to estimate the phase composition of slags with varying chemical composition and temperature. The dependence of thermochemical property models on chemical composition and temperature is illustrated in the form of ternary diagrams showing the predicted property values as a function of basicity (chemical composition) and temperature. Slag compositions typical of HCFeMn and SiMn processes are used. Each thermochemical property is calculated at 1400, 1500 and 1600 C at a fixed weight percentage ratio Al 2 O 3 /SiO 2 of 0.57 and 6% MgO. Ternary phase diagrams (1400, 1500 and 1600 C) and a ternary liquidus temperature diagram are also presented for the system. Since viscosity has the most significant influence on flow behavior, results from various viscosity models have been compared with measured data. Predictions for thermal conductivity, density, and heat capacity are also discussed.

In the chlorination process for TiO 2 pigment production, blends of titania feedstocks such as ilmenite, synthetic rutile (SR), natural rutile, upgraded slag, and chloride-grade slag are reacted with coke and chlorine at a temperature of around 1000°C to form TiCl 4 , which is the main product, and other waste metal chlorides. TiCl 4 is the main feed material for the TiO 2 pigment-making process. Feeding different titania materials to the chlorinator affects the amounts of coke and chlorine required for the process, the amount of waste generated, waste disposal costs, the amount of TiCl 4 produced, and bed build-up rates. These factors influence the value of the feedstock. Generally, a higher TiO 2 feedstock is more valued since less waste is generated and less reagents are consumed. To quantify the impact of different feedstocks on the chlorinator, a techno-economic model was developed to describe the chlorination process and estimate process variables at steady state. This paper describes the development of the model and studies in which the model has been used to quantify the effects of using different feedstocks.