China university of mining and technology, China
š Xiaolin Yang is a highly skilled Business Analyst with a Ph.D. in Mineral Process Engineering and specialized expertise in mineral separation and industrial production optimization. Known for his analytical approach and technical knowledge, Xiaolin currently serves as a Postdoctoral Researcher at Henan Investment Group, where he provides valuable industry insights, investment assessments, and strategies for process improvement. His background in machine learning and image analysis supports his innovative contributions to mineral processing.
Publication Profile
ORCID
Education
š Xiaolin Yang completed his Bachelorās degree in Mineral Process Engineering at China University of Mining and Technology (2015-2019) and later earned a Doctorate in the same field from the same institution (2019-2024). His research spans mineral separation techniques, machine learning applications, and image analysis, all aimed at advancing processing efficiency.
Experience
š¼ Xiaolin is currently a Postdoctoral Researcher at Henan Investment Group, where he contributes to industry research, investment evaluation, and production optimization. His role includes preparing assessment reports, providing strategic investment guidance, managing project feasibility studies, and enhancing industrial production processes.
Research Focus
š¬ Xiaolinās research focuses on mineral processing, applying machine learning and image analysis to improve separation processes and equipment. His studies advance understanding of mineral properties and optimization techniques, contributing to the fieldās progression toward smarter, data-driven methodologies.
Awards and Honors
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Xiaolin has been recognized for his contributions to mineral process engineering, having published in prominent journals like Journal of Materials Research and Technology and Expert Systems with Applications. His work on froth image analysis and coal flotation ash determination highlights his dedication to innovation in mineral processing.
Publication Highlights
A comparative study on the influence of mono, di, and trivalent cations on chalcopyrite and pyrite flotation (2021). Published in Journal of Materials Research and Technology [Cited by 50 articles].
Ash determination of coal flotation concentrate by analyzing froth image using a novel hybrid model based on deep learning algorithms and attention mechanism (2022). Published in Energy [Cited by 35 articles].
Multi-scale neural network for accurate determination of the ash content of coal flotation concentrate using froth images (2024). Published in Expert Systems with Applications [Cited by 20 articles].