Factsage thermodynamic software

Modified : October 7, Database modules:. Calculate modules:. Reference: Bale, C. Calphad 54, 35 FactSage has a very broad range of applications, both in the industry and in the academia. It can be used to calculate complex phase equilibria in many systems of interest consisting of a mixture of inorganic substances reacting with each other.

This includes oxides and metallic solid phases, aqueous solutions and different types of liquid and gaseous phases. It is the ideal tool for the calculation of high temperature phase equilibria which are relevant for the metallurgical and ceramic industry. In addition to the calculation of phase equilibria and phase diagrams, it is also possible to simulate the dynamics of reactors or processes by using FactSage.

It may be that you wish to design a new material with a certain crystallographic structure or possessing specific chemical, physical, mechanical or electromagnetic properties. Once FactSage can predict which phase forms under specific conditions, one is able to design new materials, i. A material is not defined only through its chemical composition, but its properties will very much depend on the processing conditions, which in their turn define the details of its microstructure. The same material, in what concerns its chemical composition, may have completely different properties depending on its microstructure.

This must include also the consideration of kinetic constrained states and metastable materials. Whenever a material should be optimized by engineering its microstructure, FactSage can be efficiently used as a tool with this objective. In the broad area of metallurgical processes, one aims to extract a metal from its corresponding mineral or to refine an impure melt through a variety of methods. The Teach package helps everybody who uses it to understand that Computational Thermochemistry is a tool for application in day-to-day work but also a method for gaining insight into a sometimes relatively sobering theoretical subject.

The package assists the user in practicing the three step process which is always involved in the application of Computational Thermochemistry:. In order to execute this three step process properly the handbook links the practical assignments given in section II on the one hand with an outline of the theoretical background of Computational Thermochemistry section I and on the other with information on the actual use of the various modules of FactSage section III, together with the slide show files of FactSage.

Below is given in tabular form an overview of the various applications used in the practical assignments:. Systems with pure substances 1. Data fore pure substances in SER-form and as relative G-functions 2.

Aluminothermic welding of rails 3. Isothermal standard and non-standard reactions between Cu and Cu2O 4. Production of ultra-pure silicon by CVD 5. Gas equilibria, substance properties, combustion calculations 6. Application of the phase rule to the system Ca-C-O 2. Systems with solution phases 1. Thermochemistry of an alloy system, Co-Cr-Fe 3. Thermochemistry of an alloy system, using a private database: the Cr-W system 3.

Phase Diagrams 1. From the Gibbs energies to the phase diagram: the binary system Pb-Sn 2. Zero-phase-fraction lines, learning how the work in given phase diagrams 3.

Aqueous solutions 1. If the reactor is very stagnant, considering the individual diffusion coefficient of each component of the liquid slag may be more appropriate. For example, diffusion coefficients of CaO and SiO 2 can be very different in liquid slag. Therefore, the simplification brought by assuming an overall diffusion coefficient of slag components can have a limited impact on the simulation results with the description of most pyrometallurgical processes using the EERZ model.

When large variations in slag chemistry and temperature take place within a given process, the slag viscosity can be taken into account in the determination of the EERZ volume to have a more flexible and accurate description of the process using the EERZ model. For the development of process models, the industrial partners have provided wide range of real operation conditions and sampling data. With these precious data, the model parameters, such as the overall mass transfer coefficient equation as a function of mixing energy, were tuned to reproduce the plant data.

In all cases, FactSage databases have been used for the equilibrium calculations and FactSage Macro Processing has been utilized to build such kinetic models. All the input conditions and calculation results can be stored and passed to the different equilibrium calculations or externally to text files or Microsoft Excel TM files using the macro processing code. The schematics of FactSage macro processing is given in Figure As an example, the simulation model for the ladle furnace LF is presented in Figure In order to show the accuracy and versatility of the model, the simulation results for a complex LF operation involving multiple flux additions, metallic additions, and arcing operation are presented in Figure In order to check the accuracy of the model, extensive plant sampling of slag, metal and non-metallic inclusions were carried out every 2 to 3 min intervals.

As can be seen in Figure 25 , the simulation results from the present LF model are in very good agreement with plant data for the composition of slag, liquid steel, and non-metallic inclusion, and liquid steel temperature. It should be noted that the model results without considering steel reoxidation dotted lines are less accurate. The gradual CaO and MgO pickup in the inclusions is well reproduced with the present model Figure 25 d.

One of the most important elements to control in the LF process is sulfur. In order to reduce and accurately control S in liquid metal, a reduced slag with high basicity is prerequisite and fast reaction kinetics between slag and metal is necessary.

In addition, reoxidation of steel should be minimized. Figure 26 a shows the variation of S content in the operation shown in Figure As can be seen, the evolution of the S content in plant is very well reproduced by the LF model. The present LF model was used to simulate 24 different heats with various process conditions. The comparison between simulated and calculated S content in metal for 24 heats is shown in Figure 26 b.

Each point represents a sample taken during the heat. As can be seen, the model is able to reproduce accurately the evolution of the LF process under various process conditions. Schematic diagram of reaction zones of the ladle furnace model developed for Tata Steel Europe. The plant data are from Tata Steel Europe[ 79 ].

Smart factory is one of commanding trends in the manufacturing industry. The steel industry is quickly adopting the smart factory concept and digital transformation. All leading steel companies collect enormous amount of data from sensors at the plant in order to analyze them and extract relationships between operation conditions and process outputs.

However, upstream processes like ironmaking and steelmaking deal with liquid metal at high temperatures. Due to the nature of the process, there are many uncontrollable factors in operation and noticeable scatter in the collected data, which make difficult to adopt the smart factory process. Therefore, sometimes the analysis of big data from the plant may not give the proper direction or trend due to the scatter. There are many key components to complete a smart factory.

One of the key components is the so-called digital twin, which is the digital replica of an actual plant virtual plant simulator. In the case of the ironmaking and steelmaking process, chemical reactions between liquid metal, slag, gas, and refractory materials are the most important factors controlling the productivity and quality of the final molten metal product.

Therefore, creating a digital twin accounting for accurate thermodynamics and kinetics of chemical reactions is highly necessary. A robust digital twin can provide the simulation of possible scenarios and predict possible outcomes, without affecting the physical production. Based on the real-time collected data, the digital twin can also provide optimal process conditions, create synergy across the plant, detect defects and predict maintenance.

The process simulation model based on the EERZ concept can be a good digital twin candidate for the ironmaking and steelmaking process. Several steelmaking companies in the steelmaking consortium already use the process model as digital twin. The models provide virtual simulations for a wide range of operation conditions and chemistry. These simulation results can be used as big data for the development of level 2 control model in the plant.

As part of the steelmaking consortium project, our future direction in the development of process models is building a virtual integrated steel plant.

With this conversion, a 1-hour real operation process can be simulated within 5 to 10 min, demonstrating that models based on the EERZ concept can be applied to real-time process simulation. Eventually, a virtual steel plant containing a series of process models in one platform can be automatically operated with an Artificial Intelligent AI control system to provide the best operation conditions under the target constraints dynamically changing depending on economic situation.

After validation, it can be directly connected to the level 2 control system at the plant to provide real-time optimal operation conditions, as schematically presented in Figure The FactSage thermodynamic databases covering a wide range of materials processes have been developed in close and long-term collaborations with industrial partners.

A sound thermodynamic model reflecting the nature of a solution is key to accurately describe the thermodynamics of the solution phase and develop a comprehensive thermodynamic database for the solution. In FactSage, the Modified Quasichemical Model has been exclusively and successfully used for describing liquid solution phases such as molten slag, matte, salts, and even metallic phases.

The bond structure calculated from the model has also been used for the development of physical property models such as viscosity, molar volume, electrical conductivity, etc. In the present study, the current FactSage databases and on-going developments were briefly overviewed. Several application examples of the thermodynamics databases to the steel industry were covered, from iron ore sintering, alkali circulation in the blast furnace, dephosphorization in the basic oxygen furnace, secondary refining and casting process of high alloy steels, new steel grade design, to the galvanizing coating line.

The on-going advancements on virtual process simulation in collaboration with steel companies were presented. These virtual process simulations based on the Effective Equilibrium Reaction Zone EERZ model have already been adopted for smart factory in the steel industry. The FactSage software with thermodynamic databases has also been applied in the aluminum, non-ferrous, combustion and energy industries, etc. We believe that CALPHAD-type thermodynamic databases will become more and more essential for materials research and processing in both the academic community and industrial sectors.

Saunders and A. Google Scholar. Jung, Calphad, , vol. Lehman, K Hack, M. Van Ende, E. Jak, and I. Jung Computational thermodynamics. In: S. Seetharaman, A. McLean, R. Guthrie, and S. Pelton, and M. Blander, Metall. B, , vol. Blander and A.

Pelton, Geochim. Acta, , vol. Pelton, S. Decterov, G. Eriksson, C. Robelin, Y. Dessureault, Metall. Pelton, and P. Chartrand, Metall. A, , vol. Chartrand, and A. Pelton, Metall. Article Google Scholar. Pelton, Calphad, , vol. Chartrand and A. Pelton, J. Phase Equilibria, , vol.

Muggianu, M. Gambino and J. Bros, J. Toop, Trans. AIME, , vol. CAS Google Scholar. Kohler, Monatsh. Chemie, , vol. Jung, S. Decterov, A. Jung, Asia Steel Conf. Kang, and A. Lindberg, R. Backman and P.

Chartrand, J. Cui and I. Eriksson, P. Wu, M. Blander, and A. Pelton, Can. Park and P. Rhee, J. Solids, , vol. Kim, and Y. Kang, J. Phase Equilib. Jung, Metall. Trans A, , vol. Jung, D. Kang, W. Park, N. Kim and S. Ahn, Calphad, , vol. Waldner and A. Hillert, J. Alloy Comp. Bragg and E. Williams: Proc. A , , vol.

Wagner: Thermodynamics of alloys. Addison-Wesley, Reading, MA, Pelton and C. Bale, Metall. Bale and A. Book Google Scholar. Pelton, G. Eriksson, J. Romero-Serrano, Metall. Pelton, Glastechnische Berichte, , vol.

Grundy, H. Liu, I. Decterov and A.