In the second blog post of this series related to the Blast Furnace Diagram, we explore some variations of the diagram constructed in the first blog post, while sticking to carbon as the only reducing agent in the system. This blog post was written in collaboration with Klaus Hack. The first diagram that we will …
Constructing a Blast Furnace Diagram with FactSage – Part II Read More »
In this article, some of the features and recent additions to the ChemApp for Python package are introduced and explained. As a brief summary, ChemApp for Python is a Python module that encapsulates the ChemApp thermodynamic calculation library. It does provide different ways to access it. To users that are accustomed to ChemApp from other …
Advanced functionalities of ChemApp for Python: Streams Read More »
The so-called blast-furnace diagram describes which phases are stable in the different regions of the blast furnace, depending on the conditions of temperature and of the atmosphere inside the furnace[1]. It is basically a phase diagram, where the volume fraction of CO is represented on the X-axis and the temperature is represented on the Y-axis, …
Constructing a Blast Furnace Diagram with FactSage 8.2 Read More »
The GTT Users‘ Meeting 2022, which took place online, was a huge success with more than 150 participants from all over the world! The final program including the recorded video presentations can be found here: https://gtt-technologies.de/workshop2022/ As you can see, the program contained an excellent mix of theoretical and application-oriented presentations covering topics mainly in …
Video presentations from the GTT Users’ Meeting 2022 are online Read More »
KilnSimu is a standalone software that can be used for thermodynamic modelling rotary drum furnaces. It is a joint production by VTT and GTT. While it relies on the calculation routines of ChemApp, it is user-friendly and specialized to one specific application. The rotary kiln is concurrently used in several industries. The rotary drum provides …
In connection with the Industry 4.0 initiative, tools for process simulation and process optimization are recently gaining in importance. In the context of metallurgy, its digitalization will work as an enabler of the „circular economy“ [1], by a combination of fundamentally-based process models, artificial intelligence, big data approaches and last by not least, guided by …