Lei Zhang

Dalian University of Technology

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Minxu Lu

University of Science and Technology Beijing

Aimin Li

Dalian University of Technology

Kelang Jin

Zhejiang University

Hao Zhou

McGill University

Xuexue Wang

Zhejiang University of Technology

Kongyun Zhu

Dalian Minzu University

Xiang Liu

Hubei University of Science and Technology

Yulin Zhang

Shandong Tumor Hospital

Chuanshuai Chen

Dalian University of Technology

Zhu Wang

Zhejiang University of Technology

Cooperative Institutions

Biocat

University of Science and Technology Beijing

Chinese Academy of Sciences

Dalian University of Technology

Zhejiang University

China University of Mining and Technology

University of Chinese Academy of Sciences

Sichuan University

Peking University

Ministry of Education of the People's Republic of China

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Research on prediction of nanocrystalline alloy hysteresis properties based on long short-term memory network

Scientific Reports (2025)

Hailin Li Bo Zhang Yongpeng Shen Lei Zhang Kun Liu

In order to predict the hysteresis characteristics of nanocrystalline alloy materials at different frequencies, a data-driven hysteresis prediction model based on the encoder–decoder architecture, which combines long short-term memory network and feedforward neural network, is proposed in this paper. The data-driven based magnetic hysteresis prediction model can take advantage of the powerful nonlinear learning ability of artificial neural network to train and learn its magnetic hysteresis characteristics of nanocrystalline alloy materials at different frequencies. Firstly, based on the encoder–decoder architecture, a hysteresis prediction model is constructed by combining long short-term memory network and feedforward neural network. Subsequently, in order to obtain the training set and validation set used for the data-driven based hysteresis prediction model, the Jiles–Atherton (J–A) hysteresis model is identified based on the B–H measurement data of a small number of nanocrystalline alloy materials at different frequencies for expediency since it is quite cumbersome and time-consuming to get these B–H data by measurement. Finally, the validity and accuracy of the data-driven based hysteresis prediction model are proved by the validation set. The maximum error is about 10.29%. The results show that the hysteresis model of neural network is able to predict hysteresis characteristics with considering the effect of frequency, which provides a new way for the simulation of hysteresis characteristics.

Nanocrystalline material

Hysteresis

10.1038/s41598-025-91138-1

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Breakthrough of shallow shale gas exploration in Taiyang anticline area and its significance for resource development in Zhaotong, Yunnan Province, China

Petroleum Exploration and Development (2020)

Xing Liang Zhengyu Xu Zhao Zhang Wang Weixu Jiehui Zhang

Based on exploration and development results and evaluation of marine shale gas in South China in the past ten years, in view of the features of "high maturity, strong tectonic reformation and high shear stress" of the shale in Zhaotong exploration zone in the Yunnan and Guizhou Plateau, as well as the key issues of long time diffusion and leakage of shallow shale gas, and the preservation conditions, the factors controlling shallow shale gas sweet spot and key zone selection evaluation technology of shale gas are investigated. From 2017 to 2018, the first significant exploration breakthrough was made in the Taiyang anticline at a buried depth of 700 to 2 000 m, discovering large-scale proved geological reserves of shallow shale gas. By examining the accumulation conditions and sweet spot control factors of the shallow shale gas in this area, it is found that the accumulation and productivity potential of shale gas in the mountainous area with complex structure outside basin are controlled by five factors: (1) The gas-rich area has weak tectonic reformation and good preservation conditions on the whole, taking on typical anticline trap occurrence mode. (2) The gas-rich area is in over-pressure state and high in shale gas content. (3) The gas-rich area has high quality shale and thus superior source rock condition. (4) The gas-rich area has high quality reservoirs dominated by class I. (5) The shale gas reservoir in the gas-rich area has high content of brittle minerals and small difference between maximum and minimum horizontal stresses which are conducive to hydraulic fracturing. The innovative practice and core technologies formed during the exploration and production capacity construction of shallow shale gas in the Zhaotong demonstration zone have great reference significance for shallow shale gas exploration and development in other areas.

Anticline

Shale Gas

10.1016/s1876-3804(20)60002-7

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Citations (51)

A materials terminology knowledge graph automatically constructed from text corpus

Scientific Data (2024)

Yuwei Zhang Fang-Yi Chen Zeyi Liu Yunzhuo Ju Dongliang Cui

Abstract A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.

Knowledge Sharing

Knowledge graph

10.1038/s41597-024-03448-0

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Citations (3)

Multi-process production occurs in the iron and steel industry, supporting ‘dual carbon’ target: An in-depth study of CO2 emissions from different processes

Journal of Environmental Sciences (2023)

Hongming Na Yuxing Yuan Tao Du Tianbao Zhang Xi Zhao

Scrap

Pig iron

Carbon fibers

Basic oxygen steelmaking

Direct reduced iron

10.1016/j.jes.2023.03.031

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Citations (21)

Electrochemical and thermodynamic properties of 1-phenyl-3-(phenylamino)propan-1-one with Na ₂ WO ₄ on N80 steel

Royal Society Open Science (2020)

Yun Wang Jun Hu Lei Zhang Jiangli Cao Minxu Lu

The corrosion inhibition effect and adsorption behaviour of 1-phenyl-3-(phenylamino)propan-1-one (PPAPO) on N80 steel in hydrochloric acid solution have been investigated by Fourier transform infrared (FTIR), electrochemical method and scanning electron microscopy. The corrosion inhibition mechanism of PPAPO mixed with Na 2 WO 4 was interpreted from the thermodynamic point of view. The results indicated that PPAPO mixed with Na 2 WO 4 acted as a mixed-type inhibitor. The inhibition film formed on N80 steel surface can increase the charge transfer resistance and prevent the occurrence of corrosion reaction, thereby reducing the corrosion rate of metal surface. The inhibition efficiency was up to 96.65%; the inhibitor PPAPO with Na 2 WO 4 showed good synergistic effect on N80 corrosion behaviour in HCl solution. The adsorption behaviour of inhibitors on N80 steel surface was in accordance with the Langmuir adsorption model and mainly belonged to chemisorption. The adsorption process of PPAPO on N80 surface was spontaneous and irreversible endothermic reaction.

Chemisorption

Hydrochloric acid

Endothermic process

Langmuir adsorption model

Corrosion inhibitor

10.1098/rsos.191692

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Citations (2)

Efficiently sintering of MSWI fly ash at a low temperature enhanced by in-situ pressure assistant: Process performance and product characterization

Waste Management (2021)

Xuexue Wang Lei Zhang Kongyun Zhu Changjing Li Yulin Zhang

10.1016/j.wasman.2021.07.036