Dynamic Modeling and Simulation of SAG Mill Circuits with Pebble Crushing
Doctoral thesis, 2024
Grinding is one of the most energy-consuming processes in the mining industry. As a critical part of the comminution process, autogenous grinding (AG) or semi-autogenous grinding (SAG) mills are often used for primary grinding. However, the breakage mechanism of a SAG mill is inefficient in grinding particles of a certain size, typically in the range of 25-55 mm, known as pebbles. Therefore, cone crushers are often used as pebble crushers and integrated into SAG mill circuits to break the critical-size particles that accumulate in the mill, which increases the performance of the primary grinding circuits.
Many studies have been carried out, mainly focusing on optimizing SAG mills and cone crushers, respectively. However, only a few have investigated the dynamic interactions between a SAG mill and its pebble crushers. The scope of this thesis is to examine the dynamic relationships and interdependencies between the SAG mill and the pebble crusher in a closed circuit. This knowledge and understanding of the process can help optimize grinding efficiency by selecting a suitable pebble crusher during the circuit design stage and by controlling the crusher during operation.
In this thesis, fundamental modeling methods and data-driven methods are presented for simulating the dynamics of the grinding process. The fundamental modeling method considers the underlying physics of the comminution process. A population balance framework is implemented for the mill with sub-models that estimate breakage and transportation. Dynamic models for cone crushers, conveyors, bins, and screens are also formulated with mass balance, material tracking, and size reduction.
Several data-driven methods are presented to find the nonlinear relations between some input variables and certain target outputs, such as the mill power, the mill pebble rate, and particle size estimation. Since the accuracy of such data-driven models depends not only on high-quality training data but also on the selection of appropriate signals as input features to the models, the understanding and knowledge of the process are essential.
The results from this work show that significant dynamic impacts can be induced by altering the pebble crusher operational settings. Thus, the performance of a SAG closed circuit can be improved with the optimized utilization of its recycle pebble crusher.
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Many studies have been carried out, mainly focusing on optimizing SAG mills and cone crushers, respectively. However, only a few have investigated the dynamic interactions between a SAG mill and its pebble crushers. The scope of this thesis is to examine the dynamic relationships and interdependencies between the SAG mill and the pebble crusher in a closed circuit. This knowledge and understanding of the process can help optimize grinding efficiency by selecting a suitable pebble crusher during the circuit design stage and by controlling the crusher during operation.
In this thesis, fundamental modeling methods and data-driven methods are presented for simulating the dynamics of the grinding process. The fundamental modeling method considers the underlying physics of the comminution process. A population balance framework is implemented for the mill with sub-models that estimate breakage and transportation. Dynamic models for cone crushers, conveyors, bins, and screens are also formulated with mass balance, material tracking, and size reduction.
Several data-driven methods are presented to find the nonlinear relations between some input variables and certain target outputs, such as the mill power, the mill pebble rate, and particle size estimation. Since the accuracy of such data-driven models depends not only on high-quality training data but also on the selection of appropriate signals as input features to the models, the understanding and knowledge of the process are essential.
The results from this work show that significant dynamic impacts can be induced by altering the pebble crusher operational settings. Thus, the performance of a SAG closed circuit can be improved with the optimized utilization of its recycle pebble crusher.
Image Processing
Grinding
Modeling
Machine Learning
SAG Mill
Digital Twin
Dynamic Simulation
Cone crusher
Data-driven Method
Crushing
Author
Haijie Li
Chalmers, Industrial and Materials Science, Product Development
Included papers
Image Process of Rock Size Distribution Using DexiNed-Based Neural Network
Minerals,;Vol. 11(2021)
Journal article
Isolating the impact of pebble recycling on AG/SAG circuit: a data-driven method
Other conference contribution
DYNAMIC MODELLING OF A SAG MILL-PEBBLE CRUSHER CIRCUIT BY DATA-DRIVEN METHODS
Other conference contribution
Investigating Dynamic Behavior in SAG Mill Pebble Recycling Circuits: A Simulation Approach
Minerals,;Vol. 14(2024)
Journal article
Popular science description
English
SAG mills (Semi-Autogenous Grinding mills) have been a key innovation in the mining industry since the mid-20th century. Developed to handle harder and larger ore, they became popular in the 1970s for their ability to increase throughput and reduce the need for secondary crushing. Over time, technological advancements have made SAG mills even more efficient, and they are now a crucial part of many mineral processing circuits. Today, they are widely used in large-scale mining operations, contributing significantly to the industry's productivity.
A pebble crusher is a machine used in the mining and minerals processing industry, typically as part of a grinding circuit. Its primary purpose is to crush oversized rocks, known as "pebbles," that are exported from a SAG mill. These pebbles are often sent to the pebble crusher to be broken down into smaller sizes that can be returned to the mill for further grinding.
This thesis examines the dynamic interactions between SAG mills and their pebble crushers within a closed grinding circuit. While much research has focused on optimizing SAG mills and pebble crushers individually, fewer studies have explored how these machines influence each other when working together. Understanding these interactions is crucial for enhancing the efficiency of the grinding process.
The study employs both fundamental and data-driven modeling techniques. The fundamental models are based on the underlying physics of the comminution process, while the data-driven models analyze real-world data to uncover complex relationships between variables. The findings demonstrate that adjusting the operational settings of the pebble crusher can impact the overall performance of the grinding circuit. As a result, optimizing the use of the pebble crusher, both during the design stage and in operation, can lead to improved efficiency in the SAG mill circuit.
A pebble crusher is a machine used in the mining and minerals processing industry, typically as part of a grinding circuit. Its primary purpose is to crush oversized rocks, known as "pebbles," that are exported from a SAG mill. These pebbles are often sent to the pebble crusher to be broken down into smaller sizes that can be returned to the mill for further grinding.
This thesis examines the dynamic interactions between SAG mills and their pebble crushers within a closed grinding circuit. While much research has focused on optimizing SAG mills and pebble crushers individually, fewer studies have explored how these machines influence each other when working together. Understanding these interactions is crucial for enhancing the efficiency of the grinding process.
The study employs both fundamental and data-driven modeling techniques. The fundamental models are based on the underlying physics of the comminution process, while the data-driven models analyze real-world data to uncover complex relationships between variables. The findings demonstrate that adjusting the operational settings of the pebble crusher can impact the overall performance of the grinding circuit. As a result, optimizing the use of the pebble crusher, both during the design stage and in operation, can lead to improved efficiency in the SAG mill circuit.
Categorizing
Subject Categories (SSIF 2011)
Other Mechanical Engineering
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
ISBN
978-91-8103-092-1
Other
Series
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5550
Publisher
Chalmers
Public defence
2024-09-12 09:00 -- 12:00
Virtual Development Laboratory (VDL), Hörsalsvägen 7A.
Opponent: Professor Stefan Heinrich, Director, Institute of Solids Process Engineering and Particle Technology, Hamburg University of Technology (TUHH)