The power of computational thermochemistry in high-temperature process design and optimization: Part 2 – Pyrometallurgical process modeling using FactFlow

Abstract

Computational thermochemistry is an essential tool when it comes to the design of new industrial pyrometallurgical processes. It also enables the optimization of existing processes by analyzing the effect of various operating conditions on key indicators such as the metal recovery, the product composition, the direct emissions and the process overall energy balance. The modeling of these complex processes requires the use of multiple streams and equilibrium reactors in order to perform a large series of thermodynamic calculations. It also needs to account for the kinetic limitations of key chemical reactions. Current thermochemical software restricts users to single equilibrium reactor calculations or necessitates advanced programming knowledge to build customized pyrometallurgical processes.

In this work, we introduce a new process simulation interface called FactFlow, a multi-stream/multi-unit process simulator embedded in the FactSage package. It offers an intuitive and efficient interface for handling streams, performing equilibrium calculations and allowing the use of stream recycling loops. It also uses the extensive thermodynamic databases available in FactSage to describe the energetics of oxides, sulfides, carbides, salts and metallic phases. This new process simulator interface enables the solving of mass and energy balances of a wide range of pyrometallurgical processes related to the primary production of iron and ferroalloys, copper, titanium and more. In this work, this new interface is used to describe four pyrometallurgical processes, i.e. (i) ferrosilicon alloy production using a submerged arc furnace, (ii) the primary production of copper and the impact of E-waste recycling using a Noranda-like process, (iii) the primary titanium production via the Kroll process, and (iv) the production of direct reduction iron ore pellets via the MIDREX process. Results of the simulations performed in this work are systematically compared to data available in the literature.