The coal-supercritical water hydrogen production technology utilizes high-temperature and high-pressure conditions to gasify coal in supercritical water,enabling efficient and low-emission coal conversion and hydrogen production process.To alleviate the time-consuming simulation process caused by the complex multiphase flow behavior within the reactor,as well as the issues such as short prediction time and rapid prediction deterioration when constructing conventional data-driven surrogate models,this paper proposed a deep learning model called POD-Koopman based on the proper orthogonal decomposition(POD)and the Koopman theory,which can capture and learn the long-term spatiotemporal evolution characteristics of the complex flow,thus facilitating the long-term rolling prediction.The test results show that it can accurately predict the time-varying behavior of the multiphase flow field in the reactor on a rolling basis with a small computational overhead,and assist in the industrial design and optimization tasks of downstream hydrogen production reactors.
关键词
超临界水煤制氢/反应器/本征正交分解/Koopman/瞬态多相流/长时滚动预测
Key words
coal-supercritical water hydrogen production/reactor/proper orthogonal decomposition/Koopman/transient multiphase flow/long-term rolling prediction
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