Structural geochemical anomalies and prospecting prediction of the Qingshan Pb–Zn Deposit in Northwestern Guizhou Province, China - Yaoyang Wu; Yan Zhang; Runsheng Han; Lei Wang; Cen Shen; Mingzhi Wang (Acta Geochimica, 2026)

Pathway toward carbon neutrality of cement industry: A comprehensive analysis based on microdata - Mengling Zhang; Baokun Li; Hairui Yang; Man Zhang; Dongfang Li; Xing Zhu; Runsheng Lin; Yongkang Han; Aoyang Zhang (Fuel, 2026)

Conversion of biomass to blast furnace injection fuel via high-temperature and high-pressure steam upgrading: An experimental and DFT calculation study - Jianliang Zhang; Lian Ye; Han Dang; Jinyin Zhang; Mingwei Wang; Peng Zhao; Charles Chunbao Xu; Runsheng Xu (Fuel, 2026)

The formation of orebodies controlled by thrust faults is driven by mechanical mechanisms in the Maoping lead–zinc deposit within the Sichuan-Yunnan-Guizhou Pb–Zn polymetallic metallogenic area - Jianbiao Wu; Runsheng Han; Hongsheng Gong; Shunchuan Wu; Yan Zhang; Yanhong Liu (Acta Geochimica, 2026)

Quantifying volcanism during Oceanic Anoxic Event 2 constrained by sedimentary mercury - Yi Zhang; Hanwei Yao; Yinggang Zhang; Paul R. Bown; Stephen E. Grasby; Runsheng Yin; Kaibo Han; Huifang Guo; Chengshan Wang; Xi Chen (Global and Planetary Change, 2026)

Dynamics process of lead-zinc hydrolysis and characterization of its hydrolysis precipitates in a Pb-Zn-NaCl-H2O system at room temperature and pressure - Junfeng Liu; Yan Zhang; Runsheng Han; Pingtang Wei; Wei Zhu (Acta Geochimica, 2026)

Mineralization Mechanism of Cobalt in the Dahongshan Copper-Iron-Cobalt Polymetallic Deposit in Yunnan, China - Xuhao Kang; Runsheng Han; Yanwei Zhao; Dong Zhao; Hongsheng Gong; Liuqing Meng; Xing Mao; Long Sun; Yinkang Zhou (Acta Geochimica, 2025)

Extraction of deep mineralization information from weak and low tectono-geochemical anomalies: A case study of the Maoping germanium-rich lead–zinc deposit in northeastern Yunnan Province, China - Yixuan Yang; Gaoming Zhou; Runsheng Han; Hongsheng Gong; Jianbiao Wu; Yan Zhang; Yaya Mi (Acta Geochimica, 2025)

The zoning rule and forming mechanism of tectonite-mineralized alteration lithofacies of black/fracture zone of the Daliangzi Pb–Zn deposit in southwest Sichuan in China - Lingjie Li; Runsheng Han; Jianbiao Wu; Zhixing Feng (Acta Geochimica, 2025)

Optimal delineation of iron ore-finding target areas in the Beishan Region, Gansu Province, China, based on multi-source data mining technology - Lihui Zhu; Runsheng Han; Hao Fu; Jianmin Luo; Yunzhi Luo (Acta Geochimica, 2025)

Co-combustion reaction of corn stalk hydrochar and anthracite: Kinetics, mechanism and CO2 emission reduction - Wang Liang; Yihao Cui; Di Zhu; Yihan Chen; Yonghui Han; Lin Yue; Chunhe Jiang; Runsheng Xu; Xiaojun Ning; Jianliang Zhang; Chuan Wang; Guangwei Wang (Fuel, 2025)

Optimization of the life cycle environmental impact of shell powder and slag concrete using response surface methodology - Yi Han; Bo Yang; Li-Yi Meng; Hyeong-Kyu Cho; Runsheng Lin; Xiao-Yong Wang (Process Safety and Environmental Protection, 2025)

Controlling factors of Riedel shear spacing in the simple shear mode of strike-slip fault: Insights from sandbox models - Xu Duan; Weiwei Zhou; Runsheng Han (Journal of Structural Geology, 2024)

Theoretical and experimental investigation on the effect of biomass injection on the utilization factor of pulverized coal and the raceway state in blast furnace - Han Dang; Runsheng Xu; Jianliang Zhang; Mingyong Wang; Jinpeng Shi; Jinyin Zhang; Xiaoxia He; Guoli Jia; Zefang Hu; Dongming Zhao (Fuel, 2024)

Co-combustion behavior and catalytic mechanism of high CaO/SiO2 biomass mixed with anthracite for blast furnace injection: Experiment, modeling and DFT investigation - Han Dang; Runsheng Xu; Jianliang Zhang; Mingyong Wang; Guoli Jia; Lingxiao Xu (Chemical Engineering Journal, 2024)

Combustion behavior of co-injecting flux, pulverized coal, and natural gas in blast furnace and its influence on blast furnace smelting - Jian Bao; Jianliang Zhang; Runsheng Xu; Alberto N. Conejo; Han Dang; Shenyang Wang; Laixing Wang (Fuel, 2024)

Effect of interface morphology on the heterogeneous development of bedding-perpendicular joints in soft–hard interbedded strata - Han Bao; Xiaoguang Li; Hengxing Lan; Runsheng Pei; Changgen Yan (Journal of Structural Geology, 2024)

Evolution behavior and kinetic analysis of vacuum-extruded iron-rich dust briquette in blast furnace - Lian Ye; Jianliang Zhang; Jiyong Yu; Runsheng Xu; Han Dang (Journal of Cleaner Production, 2023)

Hydrothermal carbonization of waste furniture for clean blast furnace fuel production: Physicochemical, gasification characteristics and conversion mechanism investigation - Han Dang; Runsheng Xu; Jianliang Zhang; Mingyong Wang; Kun Xu (Chemical Engineering Journal, 2023)

Multi-layer metallogeny of the maoping carbonate-hosted Pb–Zn deposit, SW China: Constraints from C–O–S–Pb isotopes - Lei Wang; Runsheng Han; Yan Zhang; Xiaodong Li; Lanye Wang (Applied Geochemistry, 2023)

Enhancing performance and sustainability of ultra-high-performance concrete through solid calcium carbonate precipitation - Yi Han; Runsheng Lin; Xiao-Yong Wang; TaeSoo Kim (Environmental Science and Pollution Research, 2023)

Prediction of significant wave height based on EEMD and deep learning - Tao Song; Jiarong Wang; Jidong Huo; Wei Wei; Runsheng Han; Danya Xu; Fan Meng (Frontiers in Marine Science, 2023)
Accurate and reliable wave significant wave height(SWH) prediction is an important task for marine and engineering applications. This study aims to develop a new deep learning algorithm to accurately predict the SWH of deep and distant ocean. In this study, we combine two methods, Ensemble Empirical Mode Decomposition (EEMD) and Long Short-Term Memory (LSTM), to construct an EEMD-LSTM model, and explore the optimal parameters of the model through experiments. A total of 5328 hours of SWH data from November 30, 2020, to July 9, 2021, are used to train and test the model to predict the SWH for the future 1h, 3h, 6h, 12h, and 18h. The results show that the EEMD-LSTM model has the best results compared with other comparative models for short-term and medium- and long-term predictions. The RMSEs are 0.0204, 0.0279, 0.0452, 0.0941, and 0.1949 for the SWH prediction in the future 1, 3, 6, 12, and 18 h. It can be used as a rapid SWH prediction system to ensure navigation safety to a certain extent, which has great practical significance and application value.

A significant wave height prediction method based on deep learning combining the correlation between wind and wind waves - Tao Song; Runsheng Han; Fan Meng; Jiarong Wang; Wei Wei; Shiqiu Peng (Frontiers in Marine Science, 2022)
Accurate wave height prediction is significant in ports, energy, fisheries, and other offshore operations. In this study, a regional significant wave height prediction model with a high spatial and temporal resolution is proposed based on the ConvLSTM algorithm. The model learns the intrinsic correlations of the data generated by the numerical model, making it possible to combine the correlations between wind and wind waves to improve the predictions. In addition, this study also optimizes the long-term prediction ability of the model through the proposed Mask method and Replace mechanism. The experimental results show that the introduction of the wind field can significantly improve the significant wave height prediction results. The research on the prediction effect of the entire study area and two separate stations shows that the prediction performance of the proposed model is better than the existing methods. The model makes full use of the physical correlation between wind and wind waves, and the validity is up to 24 hours. The 24-hour forecast R² reached 0.69.

Unsupervised Machine Learning for Improved Delaunay Triangulation - Tao Song; Jiarong Wang; Danya Xu; Wei Wei; Runsheng Han; Fan Meng; Ying Li; Pengfei Xie (Journal of Marine Science and Engineering, 2021)
Physical oceanography models rely heavily on grid discretization. It is known that unstructured grids perform well in dealing with boundary fitting problems in complex nearshore regions. However, it is time-consuming to find a set of unstructured grids in specific ocean areas, particularly in the case of land areas that are frequently changed by human construction. In this work, an attempt was made to use machine learning for the optimization of the unstructured triangular meshes formed with Delaunay triangulation in the global ocean field, so that the triangles in the triangular mesh were closer to equilateral triangles, the long, narrow triangles in the triangular mesh were reduced, and the mesh quality was improved. Specifically, we used Delaunay triangulation to generate the unstructured grid, and then developed a K-means clustering-based algorithm to optimize the unstructured grid. With the proposed method, unstructured meshes were generated and optimized for global oceans, small sea areas, and the South China Sea estuary to carry out data experiments. The results suggested that the proportion of triangles with a triangle shape factor greater than 0.7 amounted to 77.80%, 79.78%, and 79.78%, respectively, in the unstructured mesh. Meanwhile, the proportion of long, narrow triangles in the unstructured mesh was decreased to 8.99%, 3.46%, and 4.12%, respectively.

An experimental study on metal precipitation driven by fluid mixing: implications for genesis of carbonate-hosted lead–zinc ore deposits - Yan Zhang; Runsheng Han; Xing Ding; Junjie He; Yurong Wang (Acta Geochimica, 2019)