Current status and technology development in implementing low carbon emission energy on underground coal gasification (UCG)

Although coal mining has played a substantial role in world’s development as a critical fuel source for at least 25 years, its value is partly offset by the massive environmental issues it presents during combustion. The shift to a net-zero CO2 emission will open unique possibilities for new coal technological models in which progressive studies and policies, development, and modernization will play a significant role. Therefore, a collection of technologies has been proposed, one of which is cost-effective is the Underground Coal Gasification (UCG) coupled with carbon capture storage (CCS) and utilization technology (CCU) UCG-CCS/CCU. This paper reviews the current status and technology development in implementing low carbon emission energy on underground coal gasification. The study, therefore, leads to discussing the modern stage of underground coal gasification and carbon capture storage development, recent pilot operations, and current developments of the growing market. At the same time, it provides a reference for underground coal gasification combined with CCUS technology

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph classification. However, prior arts on graph representation learning focus on domain specific problems and train a dedicated model for each graph dataset, which is usually non-transferable to out-of-domain data. Inspired by the recent advances in pre-training from natural language processing and computer vision, we design Graph Contrastive Coding (GCC) -- a self-supervised graph neural network pre-training framework -- to capture the universal network topological properties across multiple networks. We design GCC's pre-training task as subgraph instance discrimination in and across networks and leverage contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations. We conduct extensive experiments on three graph learning tasks and ten graph datasets. The results show that GCC pre-trained on a collection of diverse datasets can achieve competitive or better performance to its task-specific and trained-from-scratch counterparts. This suggests that the pre-training and fine-tuning paradigm presents great potential for graph representation learning.Comment: 11 pages, 5 figures, to appear in KDD 2020 proceeding

Diagenetic characteristics under abnormally low pressure: A case from the Paleogene of southern Western Sag, Liaohe Depression, Bohai Bay Basin

The effects of low pressure and abnormally low pressure on reservoir diagenesis and physical property of the Paleocene in southern part of Western Sag of Liaohe Depression, Bohai Bay Basin have been analyzed using large amounts of pressure, physical property and formation testing data. When formation pressure is low or abnormally low, the pore fluid has lower pressure, the overburden litho-static pressure is largely born by the sandstone framework, sometimes over compaction occurs, leading to densification of reservoir and stronger mechanical compaction; residual formation pressure has a negative correlation with carbonate cement content, low pressure or abnormally low pressure tight sandstone formations have higher carbonate cement content than sandstone formations with hydrostatic pressure or weak overpressure; pore fluid in sandstones with low pressure or abnormally low pressure has higher Si4+, conducive to the siliceous cementation; when dissolution happens, reservoirs with low pressure or abnormally low pressure, poor in original physical properties, are not favorable for the injection of dissolution fluid and the expulsion of dissolution products, so they have weaker dissolution. In summary, reservoirs with low pressure or abnormally low pressure have poorer physical properties. Key words: abnormally low pressure, diagenesis, reservoir physical property, tight sandstone, Western Sag, Liaohe Depressio

Profiling web users using big data. Social network analysis and mining

Profiling Web users is a fundamental issue for Web mining and social network analysis. Its basic tasks include extracting basic information, mining user preferences, and inferring user demographics (Tang et al. in ACM Trans Knowl Discov Data 5(1):2:1–2:44, 2010). Although methodologies for handling the three tasks are different, they all usually contain two stages: first identify relevant pages (data) of a user and then use machine learning models (e.g., SVM, CRFs, or DL) to extract/mine/infer profile attributes from each page. The methods were successful in the traditional Web, but are facing more and more challenges with the rapid evolution of the Web each persons information is distributed over the Web and is changing dynamically. As a result, available data for a user on the Web is redundant, and some sources may be out-of-date or incorrect. The traditional two-stage method suffers from data inconsistency and error propagation between the two stages. In this paper, we revisit the problem of Web user profiling in the big data era and propose a simple but very effective approach, referred to as MagicFG, for profiling Web users by leveraging the power of big data. To avoid error propagation, the approach processes all the extracting/mining/inferring subtasks in one unified framework. To improve the profiling performance, we present the concept of contextual credibility. The proposed framework also supports the incorporation of human knowledge. It defines human knowledge as Markov logics statements and formalizes them into a factor graph model. The MagicFG method has been deployed in an online system AMiner.org for profiling millions of researchers—e.g., extracting E-mail, inferring Gender, and mining research interests. Our empirical study in the real system shows that the proposed method offers significantly improved (+ 4–6%; p≪0.01 , t test) profiling performance in comparison with several baseline methods using rules, classification, and sequential labeling

Numerical Simulation of Combustion of Natural Gas Mixed with Hydrogen in Gas Boilers

Hydrogen mixed natural gas for combustion can improve combustion characteristics and reduce carbon emission, which has important engineering application value. A casing swirl burner model is adopted to numerically simulate and research the natural gas hydrogen mixing technology for combustion in gas boilers in this paper. Under the condition of conventional air atmosphere and constant air excess coefficient, the six working conditions for hydrogen mixing proportion into natural gas are designed to explore the combustion characteristics and the laws of pollution emissions. The temperature distributions, composition, and emission of combustion flue gas under various working conditions are analyzed and compared. Further investigation is also conducted for the variation laws of NOx and soot generation. The results show that when the boiler heating power is constant, hydrogen mixing will increase the combustion temperature, accelerate the combustion rate, reduce flue gas and CO2 emission, increase the generation of water vapor, and inhibit the generation of NOx and soot. Under the premise of meeting the fuel interchangeability, it is concluded that the optimal hydrogen mixing volume fraction of gas boilers is 24.7%