煤炭超临界水制氢反应器内多相流场智能滚动预测研究  

Study on intelligent rolling prediction of the multiphase flows in coal supercritical water fluidized bed reactor for hydrogen production

作　　者：丁家琦 刘海涛[1] 赵普 朱香凝 王晓放[1] 谢蓉[1] DING Jiaqi;LIU Haitao;ZHAO Pu;ZHU Xiangning;WANG Xiaofang;XIE Rong(School of Energy and Power Engineering,Dalian University of Technology,Dalian 116024,Liaoning,China)

机构地区：[1]大连理工大学能源与动力学院,辽宁大连116024

出　　处：《化工学报》2024年第8期2886-2896,共11页CIESC Journal

基　　金：国家重点研发计划项目(2020YFA0714403);国家自然科学基金面上项目(52375231);辽宁省自然科学基金面上项目(2022-MS-135)。

摘　　要：煤炭超临界水制氢技术在高温高压条件下利用超临界水充分气化煤炭,实现了高效低排放的转化和制氢过程。为解决因反应器内复杂多相流行为导致的仿真耗时问题,以及常见代理模型时序预测时间短、精度下降快等问题,提出基于本征正交分解(proper orthogonal decomposition,POD)和Koopman理论的深度学习模型POD-Koopman,用于捕捉和学习反应器内复杂流场的长时时空演变特征,实现数据驱动的长时滚动预测。测试结果表明其能在较小计算开销下准确滚动预测反应器内多相流场时变行为,助力下游制氢反应器工业化设计及优化任务。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 

分 类 号：TK123[动力工程及工程热物理—工程热物理]