Enhancing Steelmaking Operations through Interactive Immersive Simulation

Context: The steel industry is a crucial sector in the global economy, and the efficiency of steelmaking operations significantly impacts production quality, energy consumption, and environmental factors. Effective training of operators in the steel industry is essential for optimizing these operations. This Ph.D. thesis proposal aims to integrate modeling and simulation, 3D experiences, and artificial intelligence to enhance the training of operators and improve steelmaking performance.

Research Objectives: The primary objectives of this research are as follows:

1. Development of Educational Models: Create educational models for Arc Furnace Operations Training to define retraining programs based on required skills and performance metrics.
2. Modeling and Simulation: Develop a simulation model using Business Process Model and Notation (BPMN) to orchestrate a 3D environment that accurately replicates operations. This model will provide a platform for training and performance assessment.
3. Integration of AI: Incorporate artificial intelligence modules, including machine learning, to dynamically adapt training content based on data collected in real steel plants.
4. Distributed Simulation Challenges: Heterogeneous components operate in parallel. Therefore, there is a need for distributed simulation techniques to address interoperability issues, and time management and synchronization

Conclusion: This Ph.D. thesis proposal focuses on enhancing steelmaking operations through innovative training methods, interactive immersive simulations, and the integration of artificial intelligence. The research aims to bridge the gap between theoretical knowledge and practical expertise, ultimately improving the performance of operators and the steel industry as a whole.