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IA Project - Electric Arc Furnace (EAF)
Optimization of an electric arc furnace's energy consumption using artificial intelligence.
START
02/25
END
06/25
TEAM
3 pers.
STATUS
ARCHIVED
01OBJECTIVES
- 01Predict the furnace consumption based on input parameters.
- 02Implement a decision support system for the operator.
- 03Reduce energy consumption while maintaining the quality of the produced steel.
02PROCESS
STEP 01
Research and state of the art on electric arc furnaces and AI applications in metallurgy
STEP 02
Collection and preprocessing of operational data from the EAF
STEP 03
Treatment and exploratory data analysis to identify key factors influencing energy consumption.
STEP 04
Development of machine learning models to predict consumption.
STEP 05
Evaluation and validation of the models.
03TECHNOLOGIES USED
ExcelPythonJamovi
04SKILLS
- •Project Management
- •Team Management
- •Massive Data Analysis
- •Machine Learning
- •Python Programming (Pandas, Scikit-learn)
05PROJECT MEDIA
01 / 01
06RESULTS & METRICS
- R01The results are highly dependent on the quality of the data provided by the factory, which varies from country to country.
- R02A single model that could predict the consumption of all furnaces is not feasible because it depends on the technology used, the strategy and the quality of production.
- R03The most effective machine learning algorithms are random forest, gradient boosting and a meta-model combining several models with results of up to 92% accuracy.