Online Prediction of Hot Metal Temperature Using Transient Model and Moving Horizon Estimation

Hashimoto, Yoshinari; Sawa, Yoshitaka; Kano, Manabu

doi:10.2355/isijinternational.isijint-2019-101

Cited by 11 publications

(1 citation statement)

References 25 publications

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“…the coke ratio at the furnace top, the CO gas reduction equilibrium, and the heat-loss, so that the calculated trajectories of the process variables for the past 72 hours coincide with the actual ones. 29)…”

Section: T Rmentioning

confidence: 99%

Practical Operation Guidance on Thermal Control of Blast Furnace

Hashimoto¹,

Okamoto²,

Kaise³

et al. 2019

ISIJ Int.

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Blast furnaces are still manually operated in the steel industry. To realize an efficient and stable operation, the authors developed an operation guidance system for controlling hot metal temperature and applied it to actual furnaces. The system features a newly developed 2D transient model that describes the sluggish and complicated process dynamics. Based on the transient model, nonlinear model predictive control (NMPC) and moving horizon estimation (MHE) were adopted to provide operators with appropriate control actions while minimizing the undesirable influence of disturbances. The online validation results demonstrated that the developed operation guidance system successfully reduced the variance of HMT by 1.9°C.

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Section: T Rmentioning

confidence: 99%

Practical Operation Guidance on Thermal Control of Blast Furnace

Hashimoto¹,

Okamoto²,

Kaise³

et al. 2019

ISIJ Int.

Self Cite

View full text Add to dashboard Cite

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Optimization of Blast Furnace Ironmaking Using Machine Learning and Genetic Algorithms

Parihar,

Nistala,

Kumar

et al. 2024

steel research int.

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Blast furnace is a multiphase counter‐current packed bed reactor that converts iron‐bearing materials such as lumps, sinter, and pellets into hot metal using metallurgical coke and pulverized coal. The quality of input materials has a significant impact on furnace performance, hot metal quality and steel plant economics. It is difficult for operators to identify the optimal settings required for efficient and safe operation based on their experience alone, given the large number of furnace parameters. A multiobjective optimization problem for maximizing furnace productivity (PROD) and minimizing fuel rate (FR) with constraints on hot metal silicon (HMSi) and temperature (HMT) is formulated and solved using a genetic algorithm. Machine learning (ML) models are developed for PROD, FR, HMSi, and HMT and tested with data from an industrial blast furnace. Pareto‐optimal solutions along with optimal settings for key manipulated variables are obtained. It is demonstrated that PROD and FR can be improved by ≈3–5% at steady state. The overall ML model‐based optimization framework can be used as part of a blast furnace digital twin system to operate the furnace efficiently in real‐time for the given quality of raw materials.

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