The purpose of this paper is to explore gas/liquid two-phase flow is widely existed in industrial fields, especially in chemical engineering. Electrical resistance tomography (ERT) is considered to be one of the most promising techniques to monitor the transient flow process because of its advantages such as fast respond speed and cross-section imaging. However, maintaining high resolution in space together with low cost is still challenging for two-phase flow imaging because of the ill-conditioning of ERT inverse problem.,In this paper, a sparse reconstruction (SR) method based on the learned dictionary has been proposed for ERT, to accurately monitor the transient flow process of gas/liquid two-phase flow in a pipeline. The high-level representation of the conductivity distributions for typical flow regimes can be extracted based on denoising the deep extreme learning machine (DDELM) model, which is used as prior information for dictionary learning.,The results from simulation and dynamic experiments indicate that the proposed algorithm efficiently improves the quality of reconstructed images as compared to some typical algorithms such as Landweber and SR-discrete fourier transformation/discrete cosine transformation. Furthermore, the SR-DDELM has also used to estimate the important parameters of the chemical process, a case in point is the volume flow rate. Therefore, the SR-DDELM is considered an ideal candidate for online monitor the gas/liquid two-phase flow.,This paper fulfills a novel approach to effectively monitor the gas/liquid two-phase flow in pipelines. One deep learning model and one adaptive dictionary are trained via the same prior conductivity, respectively. The model is used to extract high-level representation. The dictionary is used to represent the features of the flow process. SR and extraction of high-level representation are performed iteratively. The new method can obviously improve the monitoring accuracy and save calculation time.

中文翻译：

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基于DDELM-AE稀疏字典的电阻断层成像气液两相流成像新方法

本文的目的是探索气液两相流在工业领域，尤其是化工领域中广泛存在的现象。电阻断层扫描 (ERT) 因其响应速度快和横截面成像等优点而被认为是监测瞬态流动过程的最有前途的技术之一。然而，由于 ERT 逆问题的病态，在空间上保持高分辨率和低成本仍然是两相流成像的挑战。本文提出了一种基于学习字典的稀疏重建（SR）方法。建议用于 ERT，以准确监测管道中气/液两相流的瞬态流动过程。可以基于深度极限学习机 (DDELM) 模型去噪，提取典型流态的电导率分布的高级表示，该模型用作字典学习的先验信息。模拟和动态实验的结果表明与一些典型的算法（如 Landweber 和 SR-离散傅立叶变换/离散余弦变换）相比，所提出的算法有效地提高了重建图像的质量。此外，SR-DDELM 还用于估算化学过程的重要参数，例如体积流量。因此，SR-DDELM 被认为是在线监测气/液两相流的理想选择。本文实现了一种有效监测管道中气/液两相流的新方法。分别通过相同的先验电导率训练一个深度学习模型和一个自适应字典。该模型用于提取高级表示。字典用于表示流程的特征。SR 和高级表示的提取是迭代执行的。新方法可以明显提高监测精度，节省计算时间。