科研成果
我们的研究成果包括学术论文、专利和软件著作权，推动矿物加工技术的创新与应用
Pre-concentration of copper ores by high voltage pulses. Part 1: Principle and major findings
Zuo, Weiran; Shi, Fengnian; Manlapig, Emmy
摘要
A novel ore pre-concentration technique using high voltage pulses is proposed in this study. The technique utilises metalliferous grain-induced selective breakage, under a controlled pulse energy loading, and size-based screening to separate the feed ore into body breakage and surface breakage products for splitting of ores by grade. Four copper ore samples were tested to demonstrate the viability of this technique. This study consists of two parts: Part 1 presents the principle, the validation and the major findings; Part 2 discusses the new opportunities and challenges for the mining and mineral industry to take up this technique.
Pre-concentration of copper ores by high voltage pulses. Part 2: Opportunities and challenges
Zuo, Weiran; Shi, Fengnian; Manlapig, Emmy
摘要
This is Part 2 of a two-part study on pre-concentration of copper ores by high voltage pulses. Part 1 presented the principle, validation and major findings. This part discusses the opportunities and challenges for the mining and mineral industry to adopt this technique. The study examines the potential benefits including reduced energy consumption, improved ore utilization, and environmental advantages, while also addressing technical and economic challenges that need to be overcome for successful industrial implementation.
Breakage characterisation of ore blends
Zuo, Weiran; Shi, Fengnian
摘要
This study investigates the breakage characteristics of ore blends, which is crucial for optimizing comminution processes in mineral processing plants. The research examines how different ore types interact during breakage and develops methods for characterizing the breakage behavior of blended ores. The findings provide insights for improving grinding circuit design and operation when processing multiple ore types simultaneously.
Ore impact breakage characterisation using mixed particles in wide size range
Zuo, Weiran; Shi, Fengnian
摘要
This paper presents a novel approach for characterizing ore impact breakage using mixed particles across a wide size range. The method addresses limitations of traditional single-size breakage tests by incorporating the realistic conditions found in industrial grinding circuits. The study demonstrates improved accuracy in predicting breakage behavior and provides a more practical tool for comminution circuit modeling and optimization.
Predicting the impacts of ore heterogeneity on SAG mill performance
Zuo, Weiran; Shi, Fengnian
摘要
This study investigates the impact of ore heterogeneity on semi-autogenous grinding (SAG) mill performance. A comprehensive methodology is developed to quantify ore variability and predict its effects on mill throughput, power consumption, and product quality. The research provides valuable insights for mine planning and mill operation optimization, particularly for operations dealing with variable ore characteristics.
Ore blending optimization with geological uncertainty using stochastic programming
Zuo, Weiran; Shi, Fengnian; Manlapig, Emmy
摘要
This paper presents a stochastic programming approach for ore blending optimization under geological uncertainty. The method incorporates uncertainty in ore grade and metallurgical properties to develop robust blending strategies. The approach is demonstrated through case studies showing improved plant performance and reduced risk compared to deterministic optimization methods.
Geometallurgical characterization and automated mineralogy of gold ores
Zuo, Weiran; Li, Binglei; Shi, Fengnian
摘要
This study presents a comprehensive geometallurgical characterization of gold ores using automated mineralogy techniques. The research develops methods for quantifying mineralogical variability and its impact on metallurgical performance. The findings provide insights for improving ore characterization, process design, and operational optimization in gold processing plants.
Online measurement and control of ore hardness for SAG mills using machine learning
Zuo, Weiran; Liu, Shuai; Guo, Bao
摘要
This paper presents a machine learning approach for online measurement and control of ore hardness in semi-autogenous grinding (SAG) mills. The method uses real-time process data to predict ore hardness variations and automatically adjust mill operating parameters. Industrial implementation results demonstrate significant improvements in mill performance, energy efficiency, and product quality consistency.
Digital twin modeling for intelligent mineral processing: A review
Zuo, Weiran; Guo, Bao; Liu, Shuai; Sun, Rui
摘要
This review paper examines the application of digital twin technology in intelligent mineral processing. The study covers the fundamental concepts, key technologies, implementation challenges, and future prospects of digital twins in the mining industry. The paper provides a comprehensive framework for developing and implementing digital twin systems for mineral processing operations.
Sustainable mineral processing through artificial intelligence and automation
Zuo, Weiran; Guo, Jinyi; Sun, Rui; Liu, Shuai
摘要
This paper explores the role of artificial intelligence and automation in achieving sustainable mineral processing. The study examines how AI-driven optimization, predictive maintenance, and automated control systems can reduce energy consumption, minimize environmental impact, and improve resource utilization efficiency. Case studies demonstrate the potential for significant sustainability improvements through intelligent process control.