Leaderboards/Mineral Processing and Grinding

Mineral Processing and Grinding

Last updated July 7, 2026

Rising Stars

New and hot

Rising Stars Researchers

1
Husnain Ali
Punjab Medical College
2
Mahsa Hajihosseinlou
Amirkabir University of Technology
3
Rajeevan Arunthavanathan
University of Regina
4
Shaokang Hou
Beijing Solar Energy Research Institute
5
Chunjie Zhang
Association for the Development of Earthquake Prediction
6
Fasikaw Kibrete
Addis Ababa Science and Technology University
7
Qinwen Zheng
China University of Geosciences (Beijing)
8
Tsisilile Igogo
National Laboratory of the Rockies
9
Muhammad Abdul Munnaf
Ghent University
10
Zijing Luo
The University of Sydney

Rising Stars Papers

1
Environmental hazards and comprehensive utilization of solid waste coal gangue
Progress in Natural Science Materials International|2024
212 cit.
2
Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches
Journal of Geochemical Exploration|2024
65 cit.
3
A comprehensive evaluation of OPTICS, GMM and K-means clustering methodologies for geochemical anomaly detection connected with sample catchment basins
Geochemistry|2024
57 cit.
4
Robust statistical industrial fault monitoring: A machine learning-based distributed CCA and low frequency control charts
Chemical Engineering Science|2024
45 cit.
5
Intelligent machine learning-based multi-model fusion monitoring: application to industrial physio-chemical systems
Control Engineering Practice|2025
44 cit.
6
Advance industrial monitoring of physio-chemical processes using novel integrated machine learning approach
Journal of Industrial Information Integration|2024
42 cit.
7
Fault detection using machine learning based dynamic ICA-distributed CCA: Application to industrial chemical process
Digital Chemical Engineering|2024
41 cit.
8
A novel dynamic machine learning-based explainable fusion monitoring: application to industrial and chemical processes
Machine Learning Science and Technology|2024
38 cit.
9
Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithms
Earth Science Informatics|2024
37 cit.
10
Process safety 4.0: Artificial intelligence or intelligence augmentation for safer process operation?
AIChE Journal|2024
33 cit.

Emerging Leaders

Gaining momentum

Emerging Leaders Researchers

1
Hoang Nguyen
Duy Tan University
2
Mahdi Hasanipanah
Duy Tan University
3
Salaheldin Elkatatny
King Fahd University of Petroleum and Minerals
4
Xuan-Nam Bui
Hanoi University of Mining and Geology
5
Xanthoula-Eirini Pantazi
Aristotle University of Thessaloniki
6
Wenbao Liu
Guizhou Academy of Sciences
7
Yingui Qiu
Central South University
8
Reza Ghezelbash
University of Tehran
9
Ngoc-Luan Mai
Unknown
10
Yongcun Guo
Anhui University of Science and Technology

Emerging Leaders Papers

1
A Novel Scheme for Mapping of MVT-Type Pb–Zn Prospectivity: LightGBM, a Highly Efficient Gradient Boosting Decision Tree Machine Learning Algorithm
Natural Resources Research|2023
162 cit.
2
Optimization of random forest through the use of MVO, GWO and MFO in evaluating the stability of underground entry-type excavations
Tunnelling and Underground Space Technology|2022
120 cit.
3
Short-term rockburst prediction in underground project: insights from an explainable and interpretable ensemble learning model
Acta Geotechnica|2023
118 cit.
4
Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential
Artificial Intelligence Review|2022
117 cit.
5
Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran
Expert Systems with Applications|2023
101 cit.
6
Optimized AI-MPM: Application of PSO for tuning the hyperparameters of SVM and RF algorithms
Computers & Geosciences|2024
99 cit.
7
Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity
Neural Computing and Applications|2022
97 cit.
8
Short-Term Rockburst Damage Assessment in Burst-Prone Mines: An Explainable XGBOOST Hybrid Model with SCSO Algorithm
Rock Mechanics and Rock Engineering|2023
79 cit.
9
Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches
Journal of Geochemical Exploration|2024
65 cit.
10
Reliability and availability artificial intelligence models for predicting blast-induced ground vibration intensity in open-pit mines to ensure the safety of the surroundings
Reliability Engineering & System Safety|2022
63 cit.

Current Senior Leaders

Established & currently active

Current Senior Leaders Researchers

1
Aibing Yu
Southeast University
2
S. Joe Qin
Lingnan University
3
John F. MacGregor
McMaster University
4
Furong Gao
Hong Kong University of Science and Technology
5
Xuefeng Yan
East China University of Science and Technology
6
Steven X. Ding
University of Duisburg-Essen
7
Biao Huang
University of Alberta
8
Jesse Zhu
Shanghai University of Engineering Science
9
Age K. Smilde
University of Amsterdam
10
Zhihuan Song
Zhejiang University of Science and Technology

Current Senior Leaders Papers

1
A survey of industrial model predictive control technology
Control Engineering Practice|2003
5k cit.
2
A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches
IEEE Transactions on Industrial Electronics|2015
3k cit.
3
Principal component analysis
Analytical Methods|2014
2.9k cit.
4
Discrete particle simulation of particulate systems: Theoretical developments
Chemical Engineering Science|2007
2k cit.
5
Statistical process monitoring: basics and beyond
Journal of Chemometrics|2003
1.6k cit.
6
Monitoring batch processes using multiway principal component analysis
AIChE Journal|1994
1.5k cit.
7
Survey on data-driven industrial process monitoring and diagnosis
Annual Reviews in Control|2012
1.4k cit.
8
Multivariate SPC Charts for Monitoring Batch Processes
Technometrics|1995
1.4k cit.
9
Statistical process control of multivariate processes
Control Engineering Practice|1995
1.2k cit.
10
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
IEEE Transactions on Industrial Electronics|2016
1.2k cit.

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