Application of FactSage thermodynamic modeling of recycled slags (Al₂O₃–CaO–FeO–Fe₂O₃–SiO₂–PbO–ZnO) in the treatment of wastes from end-of-life-vehicles

Abstract The FactSage thermochemical software and databases calculates complex, multi-component, multi-phase equilibria involving simultaneously slag, metal, ceramic, and gas phases, over wide ranges of temperature, oxygen potential and pressure. The databases are automatically accessed by the software and the outputs of the Gibbs free energy minimization calculations can be presented in ways that are convenient to engineering practice, and as functions of key process variables. The new thermodynamic databases for slag and solid oxide phases in the Al2O3 – CaO – FeO – Fe2O3 – SiO2 – PbO – ZnO system have been developed by critical evaluation/optimization of all available phase equilibrium and thermodynamic data. By means of the optimization process, model parameters are found which reproduce all thermodynamic and phase equilibrium data within experimental error limits. Furthermore, the models permit extrapolation into regions of temperature and composition where data are not available. Phase equilibrium calculations have been undertaken, that are of interest in the thermal treatment of Automobile Shredder Residue (ASR) in the Al2O3 – CaO – FeO – Fe2O3 – SiO2 – PbO – ZnO system. This 7-component system represents only the major components of ASR. There are at least a dozen other important components in ASR not to mention the organic matter. The operating conditions deviate from thermodynamic equilibrium and the oxygen potential during the treatment of wastes is not well established. Consequently, the calculated diagrams are intended only to give an idea to industrial engineers about the trends in the liquidus temperature, extent of crystallization and partial pressures of volatile components as functions of temperature, composition and oxygen potential. These diagrams may also help to identify the process variables that are most important for industrial practice significantly reducing the amount of experimental work that has to be done to optimize the operations.