Stage
Integration of neural networks
in industrial process control
adaptation logic of a run-out
table controller for hot strip
mills
Design of a hybrid physics-informed AI model for
industrial process control. Application of neural
networks, data analysis and software development to
improve steel manufacturing performance.
A partir du 22 février 2027, pour 6 mois
Maizières-lès-Metz, France.
Qui sommes-nous
ArcelorMittal est l’un des leaders mondiaux de l’industrie de l’acier. Tous les jours, ses 154 000 salariés, répartis dans 60
pays, contribuent à la construction d’un monde meilleur avec des aciers plus intelligents, qui consomment moins d'énergie
et émettent moins de carbone.
Le campus de Maizières
Le campus de Maizières est le plus important site de R&D du groupe avec plus de 700 salariés qui œuvrent au quotidien
pour faire de l’acier un produit de pointe. Issue des meilleures filières académiques, sa communauté de chercheurs et
chercheuses, composée de plus de 35 nationalités, conçoit et industrialise de nouveaux produits pour les marchés de
l’automobile, du packaging et de la construction. Pour répondre aux ambitions du groupe, la décarbonation de la
production d’acier est aujourd’hui l’un des principaux moteurs de la recherche réalisée sur le site.
Ce que nous cherchons
Niveau d'étude: Bac+5 / Master
Domaine: Computer Science / Software Development
Data Science / AI / Image Processing
Mathematics / Statistics
Electronics / Mechatronics / Automation
Modeling / Simulation
Compétences: Python programming skills, Knowedge on Machine Learning and neural network, Analytical and
problem-solving abilities, Ability to communicate technical concepts and present results in a
multidisciplinary environment, Autonomous, curious and willing to learn.
Langues: English, French
Ton rôle
The hot strip mill is the process where a hot-rolled coil is produced from a slab after being rolled multiple times in the rolling
stands and then cooled with water using a run-out table to control the shape and the mechanical properties of the strip.
A run-out table is key process in hot rolling composed of water jets controlled by multiple valves to reach a desired
temperature along the length of the product. R&D developed an industrial water-cooling controller for run-out table based
on temperature and metallurgical predictions.
Temperature control is a key challenge in such manufacturing processes, where product quality strongly depends on the
ability to accurately predict and regulate thermal behaviour under a wide range of operating conditions.
Current run out table control systems rely on physical and mathematical models to predict process behaviour and
determine optimal operating parameters. While these models provide robust and accurate predictions in most situations,
their performance may decrease when operating outside their calibration domain or when confronted with atypical process
conditions.
The objective of this internship is to investigate and develop a hybrid modelling approach combining physical models and
neural networks to improve the adaptation capabilities of the run out table controller. The work will consist in leveraging
historical process data and existing adaptation mechanisms to enhance prediction accuracy and controller performance
while maintaining the robustness required for industrial deployment.
The activities of the mission will include:
- Understanding the industrial process and the existing control strategy.
- Reviewing state-of-the-art machine learning approaches for industrial applications.
- Analysing historical process and production data.
- Developing and training neural network models in Python.
- Reusing and extending existing adaptation algorithms.
- Integrating machine learning models into the control adaptation workflow.
- Evaluating model performance through offline simulations and industrial case studies.
- Comparing conventional physical-model-based and hybrid AI-based approaches.
- Documenting developments and presenting results to the R&D team.
The training will be provided by a team of technicians and engineers in Maizières lab. Some contacts with ArcelorMittal Hot
Strip Mills could be requested to gather information.
The language of everyday communication is in French but reports and presentations will be in English. The internship
offers the opportunity to combine data science, software development and industrial process understanding on a real-world
application.
Ce que nous proposons
Un accompagnement personnalisé avec des tuteurs capables de te faire grandir dans ton
métier. Un cadre de travail agréable et stimulant qui favorise l’équilibre avec la vie
personnelle. Une cantine de qualité est à disposition sur le campus et des activités sportives
et autres avantages sont proposés par le comité d’entreprise. Rémunération attractive et
dispositifs complémentaires pour t’accompagner. Un système de navette gratuite permet de
relier le campus depuis les transports en commun.
Contact
Bouchaour, Benjamin
Postuler (QRCode ou lien ci-dessous)
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