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A physics-based model for industrial steam-methane reformer optimization with non-uniform temperature field

机译:温度场不均匀的基于物理的工业蒸汽-甲烷重整器优化模型

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摘要

In an industrial hydrogen production facility, steam-methane reforming reactions take place inside hundreds of catalyst-filled tubes placed in a large scale, high temperature furnace. Process efficiency depends strongly on the wall temperature distribution of the ensemble of reformer tubes; a narrower distribution has a process intensification effect, by providing similar processing experience to every feedstock molecule. Such process intensification efforts require a furnace model that can predict the temperature distribution as a function of operating conditions. Currently available furnace modeling solutions are either computationally intensive, making them unsuitable for (online) optimization calculations, or empirical, having limited accuracy when wide changes in operating conditions are required. In this work, a physics-based furnace model is presented that overcomes these limitations. Empirical perturbations in a Hottel zone radiation model are proposed to capture the spatially non-symmetrical temperature distribution. The low computational time makes the model suitable for operational intensification based on reduction of temperature distribution non-uniformity.
机译:在工业制氢设备中,蒸汽-甲烷重整反应在放置在大型高温炉中的数百个催化剂填充管内发生。工艺效率在很大程度上取决于重整管整体的壁温分布。通过为每个原料分子提供相似的加工经验,较窄的分布可增强工艺效果。这样的过程强化工作需要能够根据操作条件预测温度分布的熔炉模型。当前可用的熔炉建模解决方案要么是计算密集型的,使其不适合(在线)优化计算,要么是经验性的,在需要对工作条件进行广泛更改时,其精度有限。在这项工作中,提出了克服这些限制的基于物理的熔炉模型。提出了Hottel区域辐射模型中的经验扰动,以捕获空间非对称温度分布。较低的计算时间使该模型适合于基于温度分布不均匀性的减少而进行的强化操作。

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