Despite the practical importance, 3D measurements of gas–solid distribution in fluidized beds calls for further breakthroughs. Here an approach combing a recently developed mobile electrical capacitance tomography (ECT) sensor with Fourier Neural Operator (FNO) is developed, in which the fluidized bed is divided into a series of cross‐sectional slices along axial direction. At any given instant, the gas–solid distribution in one slice is measured by mobile ECT and the others, meantime, are predicted by FNO pre‐trained using experimental data. We verified this approach via computational fluid dynamics (CFD) simulations and experimental measurement of static object (i.e., cone, cylinder, and sphere) in fluidized bed. Following we applied this approach to direct measure 3D gas–solid distribution in a bubbling fluidized bed, and found that satisfactory image correlation coefficients and solid concentration average absolute deviation could be obtained, which indicates the proposed approach is promising for 3D fluidized bed measurements.

中文翻译：

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将移动电容层析成像与 Fourier 神经算子相结合进行 3D 流化床测量


尽管具有实际重要性，但流化床中气固分布的 3D 测量需要进一步的突破。这里开发了一种将最近开发的移动电容断层扫描 （ECT） 传感器与傅里叶神经算子 （FNO） 相结合的方法，其中流化床沿轴向分成一系列横截面切片。在任何给定的时刻，一个切片中的气固分布由移动 ECT 测量，而其他切片则由使用实验数据预先训练的 FNO 预测。我们通过计算流体动力学 （CFD） 模拟和流化床中静态物体 （即圆锥体、圆柱体和球体） 的实验测量验证了这种方法。随后，我们将该方法应用于鼓泡流化床中的 3D 气固分布，发现可以获得令人满意的图像相关系数和固体浓度平均绝对偏差，这表明所提出的方法在 3D 流化床测量中很有前途。