| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Chawla, Ashish
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (4/4 displayed)
- 2023Numerical Modelling for the Effect of Metalmould Air Gaps on Shell Thickness in Horizontal Continuous Casting of Cast Ironcitations
- 2022Laying the foundational building blocks for digitalization of horizontal continuous casting
- 2022Laying the foundational building blocks for digitalization of horizontal continuous casting
- 2021Advanced Process Models for analysis and process control of continuous casting of cast iron
Places of action
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conferencepaper
Advanced Process Models for analysis and process control of continuous casting of cast iron
Abstract
The motivation of the Industrial PhD project is to create a fast and reliable closed-loop control system for Tasso’s continuous casting unit that will increase the company’s product quality and production rate. The objective of the PhD is to acquire knowledge and develop the critical process parameters, which will aid in identifying appropriate locations for on-line sensors. The methodology adopted to achieve this would be numerical modelling (thermal) of the HCC process that will give the complete picture of the intricacies of the casting process. The next part of the project will deal with processing, analysing and optimising the process parameters, including some design parameters, of the HCC process to achieve the overall goal. The latter would be done through statistical modelling of the process parameters, either through response surface methodologies or through machine learning tools.