Using Electrical Resistance Tomography Images to Analyze the Batch and Continuous-Flow Mixing of Pseudoplastic Fluids Possessing Yield Stress in the Agitated Tanks
Non-ideal flows, such as dead zones and channeling, are commonly occurred in the mixing of nonNewtonian fluids possessing yield stress. It is a challenging task to visualize such phenomena occurring inside the mixing vessels due to the opacity of the fluids. Numerous conventional techniques have been used to study the mixing operations in both batch and continuous modes; however, these techniques have their own limitations as they are not suitable for opaque fluids or they might interfere with the local flow pattern. Therefore, the core objective of this study was to employ electrical resistance tomography (ERT), which is an efficient and nonintrusive flow visualization technique for both opaque and transparent fluids, to study the batch and continuousflow mixing of non-Newtonian fluids possessing yield stress in a stirred vessel. In this study the ERT technique was employed to: (i) measure the mixing time in the batch mode, (ii) measure the cavern size in the batch mode, (iii) analyze the cavern deformation in the continuous mode, and (iv) visualize the flow pattern generated by the impeller inside the stirred tank in the continuous mode. The 2D and 3D tomography images were used to identify the existence of the non-ideal flows encountered inside the mixing vessel. The ERT data were also used to validate the CFD model. Applying the outcomes of this research can enhance the design criteria of the mixing of nonNewtonian fluids in both batch and continuous modes.
Macromixing hydrodynamic study in draft-tube airlift reactors using electrical resistance tomography
The present study summarizes results of mixing characteristics in a draft tube airlift bioreactor using ERT. This technique offers the possibility for noninvasive and nonintrusive visualization of flow fields in the bioreactor and has rarely been utilized previously to analyze operating parameters and mixing characteristics in this type of bioreactors. Several operating parameters and geometric characteristics were examined. In general, results showed that the increase in superficial gas velocity corresponds to an increase in energy applied and thus, to a decrease in mixing time. This generally corresponded to an increase in liquid circulation velocity and shear rate values. Bottom clearances and draft tube diameters affected flow resistance and frictional losses. The influence of sparger configurations on mixing time and liquid circulation velocity was significant due to their effect on gas distribution. However, the effect of sparger configuration on shear rate was not significant, with 20% reduction in shear rates using the cross-shaped sparger. Fluid viscosity showed a marked influence on both mixing times and circulation velocity especially in the coalescing media of sugar and xanthan gum (XG) solutions. Results from this work will help to develop a clear pattern for operation and mixing that can help to improve several industrial processes, especially the ones related to emerging fields of technology such as the biotechnology industry.
Research on Fracture Recognition in Well Logging Images: Adversarial Learning with Attention
Semantic recognition of fractures in well logging images is vital for engineers to implement oil and gas exploration. An essential approach to accomplishing the object is to adopt deep learning based on convolutional neural networks. However, due to the lack of annotated labels in well logging images, it is scarcely available to directly train the semantic segmentation network. In this paper, we explore a domain shift model attempting to achieve domain adaptation from one annotated dataset to our target well logging images. This model's core is to utilize adversarial learning, including generator and discriminator, which can prompt the model to generate fracture segmentation similar to the source domain in the target domain. For enhancing the domain adaptive model further, an attention module is introduced, which can suppress redundant noise in semantic segmentation results. We demonstrate that our proposed model performs well by extensive tests and ablation experiments in the ultrasonic well logging images.