Energy storage salt cavern construction and evaluation technology

With the demand for peak-shaving of renewable energy and the approach of carbon peaking and carbon neutrality goals, salt caverns are expected to play a more effective role in oil and gas storage, compressed air energy storage, large-scale hydrogen storage, and temporary carbon dioxide storage. In order to effectively utilize the underground space of salt mines on a sound scientific basis, the construction of salt caverns for energy storage should implement the maximum utilization of salt layers, improve the cavern construction efficiency, shorten the construction period, and ensure cavern safety. In this work, built upon design experience and on-site practice in salt cavern gas storage, the four pivotal construction stages-conceptual design, solution mining simulation, tightness assessment, and stability evaluation-have been thoroughly enhanced, strengthening the technical framework for salt cavern energy storage.Document Type: PerspectiveCited as: Wan, J., Meng, T., Li, J., Liu, W. Energy storage salt cavern construction and evaluation technology. Advances in Geo-Energy Research, 2023, 9(3): 141-145. https://doi.org/10.46690/ager.2023.09.0

Follicular dendritic cell sarcoma: a report of six cases and a review of the Chinese literature

<p>Abstract</p> <p>Goals</p> <p>The main purpose of this study is to broaden the clinicopathological spectrum and increase recognition of follicular dendritic cell sarcoma (FDCS) through analysis of the clinical and pathological features of 50 cases.</p> <p>Methods</p> <p>The clinicopathological features of total 50 cases of FDCS were analyzed including a review of 44 cases reported in Chinese literature before October 2009 and six original cases from the pathology files conducted by the authors.</p> <p>Results</p> <p>The youngest patient came under observation in this study is only seven years old. Including the cases contributed by the authors, our literary review indicated that male dominated the tumor cases (M: F = 3: 2). 28 cases (56%) present with this disease in extranodal sites. Tumor cells demonstrated positive staining for the follicular dendritic cell markers CD21 (47/49), CD35 (43/45), CD23 (20/23) and CD68 (23/25). In situ hybridization for Epstein-Barr virus-encoded RNA was performed in 10 cases. Nevertheless, EBV expression was absent in all these cases. The follow-up analysis of all cases shows that 26 (81.2%) patients were alive and disease free; 6 (18.8%) patients were alive with recurrent disease or metastasis; and nobody had died of this disease at the time of last follow-up.</p> <p>Conclusions</p> <p>The diagnosis of the FDCS is based on the findings of morphology and immunohistochemistry. The FDCS occurred in China should be viewed and treated as a low-grade sarcoma, and the role of the EBV in the pathogenesis of this tumor is still uncertain. There is a possibility that the tumor might be racial or geographic correlated, because most cases were reported from Eastern Asia area; it's particular the case of the liver or spleen tumor.</p

Short Intussusception Valves Prevent Reflux After Jejunal Interposition Bilioduodenal Anastomosis

Short whole circumference and semi-circumference intussusception valves were created in interposition cholecysto-jejunal-duodenal conduits in pigs to determine which method best prevented gastrointestinal reflux into the biliary tract. Following intravenous injection of 99 mTc-HIDA the time interval for its excretion from the liver and appearance in the duodenum was not different in either whole or semi-circumference valve animals or in controls without valves. After intragastric administration of 99 mTc-DTPA the relative radioactivity of gallbladder contents (reflux) in the cohort without valves was significantly higher than in both cohorts with valves. Animals with semi-circumferential valves in turn had significantly higher levels of nuclide than those with whole circumference valves. Reflux was observed grossly in 100% of animals without valves, in 20% of those with semi-circumference valves, and in no animals with whole circumference valves. This study indicates that both Whole and semi-circumference intussusception valves placed in jejunal biliary conduits allow unimpeded flow of bile into the gastrointestinal tract. Whole circumference valves are more effective for prevention of reflux than semi-circumferential valves

Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation

Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large amount of training data. The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods. Furthermore, under few labeled training data, many existing CNNs are even ineffective. To address these challenges, we propose a Semi-supervised SAR ATR Framework with transductive Auxiliary Segmentation (SFAS). The proposed framework focuses on exploiting the transductive generalization on available unlabeled samples with an auxiliary loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR samples and information residue loss (IRL) in training, the framework can employ the proposed training loop process and gradually exploit the information compilation of recognition and segmentation to construct a helpful inductive bias and achieve high performance. Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning. The recognition performance of 94.18\% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results. Facing variances of EOCs, the recognition ratios are higher than 88.00\% when 10 training samples each class

An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR

Existing synthetic aperture radar automatic target recognition (SAR ATR) methods have been effective for the classification of seen target classes. However, it is more meaningful and challenging to distinguish the unseen target classes, i.e., open set recognition (OSR) problem, which is an urgent problem for the practical SAR ATR. The key solution of OSR is to effectively establish the exclusiveness of feature distribution of known classes. In this letter, we propose an entropy-awareness meta-learning method that improves the exclusiveness of feature distribution of known classes which means our method is effective for not only classifying the seen classes but also encountering the unseen other classes. Through meta-learning tasks, the proposed method learns to construct a feature space of the dynamic-assigned known classes. This feature space is required by the tasks to reject all other classes not belonging to the known classes. At the same time, the proposed entropy-awareness loss helps the model to enhance the feature space with effective and robust discrimination between the known and unknown classes. Therefore, our method can construct a dynamic feature space with discrimination between the known and unknown classes to simultaneously classify the dynamic-assigned known classes and reject the unknown classes. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown the effectiveness of our method for SAR OSR

SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network

Sufficient synthetic aperture radar (SAR) target images are very important for the development of researches. However, available SAR target images are often limited in practice, which hinders the progress of SAR application. In this paper, we propose an azimuth-controllable generative adversarial network to generate precise SAR target images with an intermediate azimuth between two given SAR images' azimuths. This network mainly contains three parts: generator, discriminator, and predictor. Through the proposed specific network structure, the generator can extract and fuse the optimal target features from two input SAR target images to generate SAR target image. Then a similarity discriminator and an azimuth predictor are designed. The similarity discriminator can differentiate the generated SAR target images from the real SAR images to ensure the accuracy of the generated, while the azimuth predictor measures the difference of azimuth between the generated and the desired to ensure the azimuth controllability of the generated. Therefore, the proposed network can generate precise SAR images, and their azimuths can be controlled well by the inputs of the deep network, which can generate the target images in different azimuths to solve the small sample problem to some degree and benefit the researches of SAR images. Extensive experimental results show the superiority of the proposed method in azimuth controllability and accuracy of SAR target image generation

SAR ATR under Limited Training Data Via MobileNetV3

In recent years, deep learning has been widely used to solve the bottleneck problem of synthetic aperture radar (SAR) automatic target recognition (ATR). However, most current methods rely heavily on a large number of training samples and have many parameters which lead to failure under limited training samples. In practical applications, the SAR ATR method needs not only superior performance under limited training data but also real-time performance. Therefore, we try to use a lightweight network for SAR ATR under limited training samples, which has fewer parameters, less computational effort, and shorter inference time than normal networks. At the same time, the lightweight network combines the advantages of existing lightweight networks and uses a combination of MnasNet and NetAdapt algorithms to find the optimal neural network architecture for a given problem. Through experiments and comparisons under the moving and stationary target acquisition and recognition (MSTAR) dataset, the lightweight network is validated to have excellent recognition performance for SAR ATR on limited training samples and be very computationally small, reflecting the great potential of this network structure for practical applications.Comment: 6 pages, 3 figures, published in 2023 IEEE Radar Conference (RadarConf23

Compressed air energy storage in salt caverns in China: Development and outlook

With the promotion of China’s carbon peaking and carbon neutrality goals, the energy industry is transforming from traditional fossil energy to renewable energy, which is sustainable, clean and safe. The development of renewable energy is not only an important measure to achieve the above goals but also a significant factor to alleviate the global energy crisis. Salt caverns, with good air tightness, have been considered as the best choice for large-scale underground energy storage. To elaborate on the research and future development of salt cavern compressed air energy storage technology in China, this paper analyzes the mode and characteristics of compressed air energy storage, explores the current development, key technologies and engineering experience of the construction of underground salt caverns for compressed air energy storage at home and abroad. Focusing on salt cavern compressed air energy storage technology, this paper provides a deep analysis of large-diameter drilling and completion, solution mining and morphology control, and evaluates the factors affecting cavern tightness and wellbore integrity. The future development and challenges of underground salt caverns for compressed air energy storage in China are discussed, and the prospects for the three key technologies of large-diameter drilling and completion and wellbore integrity, solution mining morphology control and detection, and tubing corrosion and control are considered. This paper aims to provide a useful reference for the development of underground salt cavern compressed air energy storage technology, the transformation of green and renewable energy, and the realization of carbon neutral vision.Document Type: Invited reviewCited as: Wan, M., Ji, W., Wan, J., He, Y., Li, J., Liu, W., Jurado, M. J. Compressed air energy storage in salt caverns in China: Development and outlook. Advances in Geo-Energy Research, 2023, 9(1): 54-67. https://doi.org/10.46690/ager.2023.07.0