Data-driven monitoring of raceway dynamics in ironmaking blast furnaces

炼铁高炉的回旋区动态特性对高炉的运行稳定性和成本有很大的影响，但其动态行为尚未得到很好的在线监测。该项目旨在开发一个数据驱动的模型，用于监测回旋区内气-固-粉反应流动的内部状态，并在线预测回旋区异常。它将通过将颗粒-流体数值模拟与数据处理和降维状态观测器相结合，由实验室/工厂实验支持，并与来自煤炭和钢铁行业的两个行业合作伙伴合作来实现。项目成果包括代码、模型和滚道控制策略，有助于促进澳大利亚冶金煤的全球市场，并最终促进澳大利亚经济。

Raceway dynamics in ironmaking blast furnaces affect operational stability and cost considerably, yet their dynamic behaviour has not been well monitored online. The project aims to develop a data-driven model for monitoring the internal state of gas-solid-powder reacting flow in the raceway and predicting raceway anomalies online. It will be achieved by combining particle-fluid numerical simulations with data processing and reduced-order state observer, supported by lab/plant experiments, and collaborating with two industry partners from coal and steel industries. The project outcomes including codes, models and raceway control strategies can help promote Australian metallurgical coal's global markets and ultimately the Australian economy.