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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

A PSO-ELM Based Prediction Model for Sulfur Content at the Endpoint of Hot Metal Pretreatment

Author(s)

Xianwu Zhang, Mingmei Zhu, Zhengjiang Yang, Chenghong Li, Qin Xianhong

On-Site Speaker (Planned)

Xianwu Zhang

Abstract Scope

Accurate prediction of sulfur content at the endpoint of hot metal pretreatment is the key to achieve precise control of the desulfurization process. In the study, a prediction model that using Particle Swarm Optimization (PSO) to optimize the hyperparameters of Extreme Learning Machine (ELM) was proposed. The PSO algorithm was utilized to perform global optimization of ELM's hyperparameters, including input weights and hidden layer thresholds. The approach overcomes the issue of traditional ELM's tendency to fall into local optima, thereby enhancing the prediction accuracy. Using the production data of a plant, a PSO-ELM prediction model was constructed and compared with ELM, support vector machine (SVM) models. The results showed that the PSO-ELM model achieved a hit ratio of 94.2% within a sulfur content range of ą0.0003%, significantly improving over ELM and SVM. The study provides an effective solution for the precise control of sulfur content in hot metal pre-desulfurization process.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Modeling and Simulation, Iron and Steel

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