Dr. Xiaolin Yang | Image analysis | Best Researcher Award
Dr at China university of mining and technology, China
Xiaolin Yang is a skilled Business Analyst and Postdoctoral Researcher at Henan Investment Group. With a solid background in mineral process engineering, his expertise spans industry research, project management, and production optimization. Xiaolin holds a Bachelor’s and a Ph.D. in Mineral Process Engineering from the China University of Mining and Technology, specializing in mineral processing, machine learning, and image analysis. His dedication to academic excellence and practical application makes him a valuable asset in the mineral industry.
Publication Profile
Education🎓
.Bachelor of Mineral Process Engineering | China University of Mining and Technology, 2015–2019 | Focus: Mineral separation methods and equipment. Doctor of Mineral Process Engineering | China University of Mining and Technology, 2019–2024 | Research areas: Mineral processing, machine learning, image analysis. Xiaolin’s academic journey emphasized innovation in mineral separation, blending engineering with data science to improve mineral processing efficiency and accuracy.
Experience💼
Postdoctoral Researcher | Henan Investment Group, 2024–Present | Xiaolin’s role involves comprehensive industry research, preparing assessment reports, and offering investment insights and recommendations. His project management tasks focus on feasibility assessments and evaluating the effectiveness of production processes, aiming to optimize industrial production and implement innovative solutions in mineral processing.
Awards and Honors🏆
Published Author | Xiaolin has authored notable academic articles, such as in Journal of Materials Research and Technology (2021), Energy (2022), and Expert Systems with Applications (2024). His work, recognized for its significance in mineral processing and machine learning, highlights his expertise in utilizing advanced algorithms for practical industry challenges.
Research Focus🔍
Research Interests | Xiaolin’s research delves into mineral processing, machine learning applications, and image analysis. His studies, including deep learning for ash determination in coal flotation, explore novel algorithms to enhance mineral processing accuracy, bridging engineering and artificial intelligence for industrial optimization.
Multi-scale neural network for accurate determination of ash content in coal flotation concentrate
Authors: Yang, X., Zhang, K., Thé, J., Tan, Z., Yu, H.
Journal: Expert Systems with Applications, 2025, 262, 125614
Description: This paper presents a multi-scale neural network model that accurately determines ash content in coal flotation concentrate using froth images, leveraging deep learning to enhance mineral processing efficiency.
STATNet: One-stage coal-gangue detector for real industrial applications
Authors: Zhang, K., Wang, T., Yang, X., Tan, Z., Yu, H.
Journal: Energy and AI, 2024, 17, 100388
Description: The STATNet model is introduced as a coal-gangue detection system using a one-stage deep learning algorithm, tailored for industrial application with a focus on real-time processing.
COFNet: Predicting surface area of covalent-organic frameworks
Authors: Wang, T., Yang, X., Zhang, K., Tan, Z., Yu, H.
Journal: Chemical Physics Letters, 2024, 847, 141383
Description: COFNet utilizes deep learning to predict the specific surface area of covalent-organic frameworks, combining structural image analysis with statistical features for accurate predictions.
Enhancing coal-gangue detection with GAN-based data augmentation
Authors: Zhang, K., Yang, X., Xu, L., Tan, Z., Yu, H.
Journal: Energy, 2024, 287, 129654
Description: This study employs GAN-based data augmentation and a dual attention mechanism to improve coal-gangue object detection, aiming to refine accuracy in complex industrial environments.
Multi-step carbon price forecasting using hybrid deep learning models
Authors: Zhang, K., Yang, X., Wang, T., Tan, Z., Yu, H.
Journal: Journal of Cleaner Production, 2023, 405, 136959
Description: A hybrid deep learning model for multi-step forecasting of carbon prices is proposed, integrating multivariate decomposition to enhance predictive reliability.
PM2.5 and PM10 concentration forecasting with spatial–temporal attention networks
Authors: Zhang, K., Yang, X., Cao, H., Tan, Z., Yu, H.
Journal: Environment International, 2023, 171, 107691
Description: This article introduces a spatial–temporal attention mechanism for PM2.5 and PM10 forecasting, using convolutional neural networks with residual learning to tackle air quality predictions.
Ash determination of coal flotation concentrate using hybrid deep learning model
Authors: Yang, X., Zhang, K., Ni, C., Tan, Z., Yu, H.
Journal: Energy, 2022, 260, 125027
Description: This work features a hybrid model that utilizes deep learning and attention mechanisms to determine ash content in coal flotation, contributing to process optimization.
Influence of cation valency on flotation of chalcopyrite and pyrite
Authors: Yang, X., Bu, X., Xie, G., Chehreh Chelgani, S.
Journal: Journal of Materials Research and Technology, 2021, 11, pp. 1112–1122
Description: This comparative study explores how different cation valencies affect chalcopyrite and pyrite flotation, contributing to better separation techniques in mineral processing.
Conclusion
Xiaolin Yang is a compelling candidate for the Best Researcher Award. His strengths in applying AI and image analysis to mineral processing reflect a unique skill set that is highly relevant for advancing research and industry practices. With further interdisciplinary work and expanded research visibility, Xiaolin is well-positioned to make impactful contributions and earn recognition in his field.