Institute of Applied Computer Science, Lodz University of Technology, Stefanowskiego 18/22, 90-924 Lodz, Poland.
Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, 2629 HZ Delft, The Netherlands.
Sensors (Basel). 2022 Feb 25;22(5):1834. doi: 10.3390/s22051834.
Electrical resistance tomography (ERT) has been used in the literature to monitor the gas-liquid separation. However, the image reconstruction algorithms used in the studies take a considerable amount of time to generate the tomograms, which is far above the time scales of the flow inside the inline separator and, as a consequence, the technique is not fast enough to capture all the relevant dynamics of the process, vital for control applications. This article proposes a new strategy based on the physics behind the measurement and simple logics to monitor the separation with a high temporal resolution by minimizing both the amount of data and the calculations required to reconstruct one frame of the flow. To demonstrate its potential, the electronics of an ERT system are used together with a high-speed camera to measure the flow inside an inline swirl separator. For the 16-electrode system used in this study, only 12 measurements are required to reconstruct the whole flow distribution with the proposed algorithm, 10× less than the minimum number of measurements of ERT (120). In terms of computational effort, the technique was shown to be 1000× faster than solving the inverse problem non-iteratively via the Gauss-Newton approach, one of the computationally cheapest techniques available. Therefore, this novel algorithm has the potential to achieve measurement speeds in the order of 10 times the ERT speed in the context of inline swirl separation, pointing to flow measurements at around 10kHz while keeping the average estimation error below 6 mm in the worst-case scenario.
电阻层析成像（ERT）技术已在文献中用于监测气液分离。然而，研究中使用的图像重建算法需要相当长的时间来生成层析图像，这远远超过了在线分离器内部流动的时间尺度，因此，该技术的速度不够快，无法捕捉到过程的所有相关动力学，这对于控制应用至关重要。本文提出了一种新策略，该策略基于测量背后的物理学原理和简单逻辑，通过最小化数据量和重建一帧流所需的计算量，以高时间分辨率监测分离。为了证明其潜力，ERT 系统的电子设备与高速摄像机一起用于测量在线旋流分离器内部的流动。对于本研究中使用的 16 电极系统，仅需 12 次测量即可用所提出的算法重建整个流动分布，比 ERT 的最小测量次数（120 次）少 10 倍。在计算工作量方面，该技术比通过高斯牛顿方法（一种最便宜的计算技术之一）非迭代地求解逆问题快 1000 倍。因此，在在线旋流分离的情况下，这种新算法有可能实现比 ERT 速度快 10 倍的测量速度，在最坏情况下，指向 10kHz 左右的流量测量，同时将平均估计误差保持在 6mm 以下。
