Designing Comminution Circuits with a Multi-Objective Evolutionary Algorithm

Mining is an important industry in Australia, contributing billions of dollars to the economy. The performance of a processing plant has a large impact on the profitability of a mining operation, yet plant design decisions are typically guided more by intuition and experience than by analysis. In this paper, we motivate the use of an evolutionary algorithm to aid in the design of such plants. We formalise plant design in terms suitable for application in a multi-objective evolutionary algorithm and create a simulation to assess the performance of candidate solutions. Results show the effectiveness of this approach with our algorithm producing designs superior to those used in practice today, promising significant financial benefits

Design of comminution circuits for improved productivity using a multi-objective evolutionary algorithm (MOEA)

The performance of a processing plant has a large impact on the profitability of a mining operation, yet plant desig optimisation decisions are based on past experience and intuition rather than on scientific analysis. Genetic algorithms as a tool for circuit analysis in plant design and optimisation was considered. The multi-objective evolutionary algorithm initialises the plant design and optimisation based on experimental results, which are used to formulate and determine the objective function values. A simulation was conducted to assess the performance of candidate solutions. The two optima are then traded-of using cost objective, which is sought to be minimized. Once an optimum was selected, the circuit mass balance and equipment design was performed, bringing the theory of network design and genetic algorithms into unison. Results of the study provide financial benefits, optimal parameter settings for the comminution equipment and ultimate better plant performance

Optimization Capabilities for Crushing Plants

Responsible production and minimal consumption of resources are becoming competitive factors in the industry. The aggregates and minerals processing industries consist of multiple heavy mechanized industrial processes handling large volumes of materials and are energy-intensive. One such process is a crushing plant operation consisting of rock size reduction (comminution) and particle size separation (classification) processes. The objective of the crushing plant operation for the aggregates industry is to supply specific size fractions of rock material for infrastructure development, while the objective in minerals processing is to maximize material ore throughput below a target size fraction for the subsequent process. The operation of a crushing plant is complex and suffers variabilities during the process operation, resulting in a drive for optimization functionality development. Process knowledge and understanding are needed to make proactive decisions to enable operations to maintain and elevate performance levels. To examine the complex relationships and interdependencies of the physical processes of crushing plants, a simulation platform can be used at the design stage. Process simulation for crushing plants can be classified as either steady-state simulation or dynamic simulation. The steady-state simulation models are based on instantaneous mass balancing while the dynamic simulation models can capture the process change over time due to non-ideal operating conditions. Both simulation types can replicate the process performance at different fidelities for industrial applications but are limited in application for everyday operation. Most companies operating crushing plants are equipped with digital data-collection systems capturing continuous production data such as mass flow and power draw. The use of the production data for the daily decision-making process is still not utilized to its full potential. There are opportunities to integrate optimization functions with the simulation platform and digital data platforms to create decision-making functionality for everyday operation in a crushing plant. This thesis presents a multi-layered modular framework for the development of the optimization capabilities in a crushing plant aimed at achieving process optimization and process improvements. The optimization capabilities for crushing plants comprise a system solution with the two-fold application of 1) Utilizing the simulation platform for identification and exploration of operational settings based on the stakeholder’s need to generate knowledge about the process operation, 2) Assuring the reliability of the equipment model and production data to create validated process simulations that can be utilized for process optimization and performance improvements.During the iterative development work, multiple optimization methods such as multi-objective optimization (MOO) and multi-disciplinary optimization (MDO) are applied for process optimization. An adaptation of the ISO 22400 standard for the aggregates production process is performed and applied in dynamic simulations of crushing plants. A detailed optimization method for calibration and validation of process simulation and production data, especially for mass flow data, is presented. Standard optimization problem formulations for each of the applications are demonstrated, which is essential for the replicability of the application. The proposed framework poses a challenge in the future development of a large-scale integrated digital solution for realizing the potential of production data, simulation, and optimization. In conclusion, optimization capabilities are essential for the modernization of the decision-making process in crushing plant operations

Optimization Framework for Crushing Plants

Optimization is a decision-making process to utilize available resources efficiently. The use of optimization methods provide opportunities for continuous improvements, increasing competitiveness, trade-off analysis and as a support tool for the decision-making process in industrial applications. One of such industrial applications where optimization methods are needed is coarse comminution and classification processes for aggregates and minerals processing industries. The coarse comminution and classification process, consisting of crushing and screening, is a heavy industrial process characterized by continuous operations. The processes handle large material volumes, are energy intensive, and suffer large variabilities during process operations. To understand the complexity and to replicate the process performance of the coarse comminution and classification processes, process simulation models have been under development for the past few decades. There are two types of process simulation models: steady-state simulation and dynamic simulation. The steady-state simulation models are based on instantaneous mass balancing while the dynamic simulation models are capable of capturing the process change over time due to non-ideal operating conditions. Both simulation types are capable of capturing the process performance, although the dynamic process simulations have been proven to have a higher fidelity for industrial applications. Both the steady-state and dynamic simulation models lack the capability of optimization methods which can potentially increase the utilization of the developed process simulation models. The optimization capabilities can further increase the functionality of the process simulation models and provide decision-making support. The thesis presents a modular optimization framework for carrying out process optimization and process improvements in a coarse comminution and classification process using process simulation models. The thesis describes the results of explorative studies carried out for developing the application of optimization methods and key performance indicators for the coarse comminution and classification process. The application of the optimization methods can generate new insights about the process performance with respect to the operating parameters, and non-intuitive results. The application of the key performance indicators can be used to carry out process diagnostics and process improvement activities. As a conclusion, a conceptual framework for carrying out optimization procedure within the coarse comminution and classification process is presented. The development of the optimization system and performance measuring system can be useful for process optimization and process improvements for industrial applications

Applied Calibration and Validation Method of Dynamic Process Simulation for Crushing Plants

There is a need within the production industry for digitalization and the development of meaningful functionality for production operation. One such industry is aggregate production, characterized by continuous production operation, where the digital transformation can bring operational adaptability to customer demand. Dynamic process simulations have the ability to capture the change in production performance of aggregate production over time. However, there is a need to develop cost-efficient methodologies to integrate calibrations and validation of models. This paper presents a method of integrating an experimental and data-driven approach for calibration and validation for crushing plant equipment and a process model. The method uses an error minimization optimization formulation to calibrate the equipment models, followed by the validation of the process model. The paper discusses various details such as experimental calibration procedure, applied error functions, optimization problem formulation, and the future development needed to completely realize the procedure for industrial use. The validated simulation model can be used for performing process planning and process optimization activities for the crushing plant’s operation

Application of Optimization Method for Calibration and Maintenance of Power-Based Belt Scale

Process optimization and improvement strategies applied in a crushing plant are coupled with the measurement of such improvements, and one of the indicators for improvements is the mass flow at different parts of the circuit. The estimation of the mass flow using conveyor belt power consumption allows for a cost-effective solution. The principle behind the estimation is that the power draw from a conveyor belt is dependent on the load on the conveyor, conveyor speed, geometrical design, and overall efficiency of the conveyor. Calibration of the power-based belt scale is carried out periodically to ensure the accuracy of the measurement. In practical implementation, certain conveyors are not directly accessible for calibration to the physical measurement as these conveyors have limited access or it is too costly to interrupt the ongoing production process. For addressing this limitation, a better strategy is needed to calibrate the efficiency of the power-based belt scale and maintain the reliability of such a system. This paper presents the application of an optimization method for a data collection system to calibrate and maintain accurate mass flow estimation. This includes calibration of variables such as the efficiency of the power-based belt scale. The optimization method uses an error minimization optimization formulation together with the mass balancing of the crushing plant to determine the efficiency of accessible and non-accessible conveyors. Furthermore, a correlation matrix is developed to monitor and detect deviations in the estimation for the mass flow. The methods are applied and discussed for operational data from a full-scale crushing plant

Quantification of uncertainty of geometallurgical variables for mine planning optimisation

Interest in geometallurgy has increased significantly over the past 15 years or so because of the benefits it brings to mine planning and operation. Its use and integration into design, planning and operation is becoming increasingly critical especially in the context of declining ore grades and increasing mining and processing costs. This thesis, comprising four papers, offers methodologies and methods to quantify geometallurgical uncertainty and enrich the block model with geometallurgical variables, which contribute to improved optimisation of mining operations. This enhanced block model is termed a geometallurgical block model. Bootstrapped non-linear regression models by projection pursuit were built to predict grindability indices and recovery, and quantify model uncertainty. These models are useful for populating the geometallurgical block model with response attributes. New multi-objective optimisation formulations for block caving mining were formulated and solved by a meta-heuristics solver focussing on maximising the project revenue and, at the same time, minimising several risk measures. A novel clustering method, which is able to use both continuous and categorical attributes and incorporate expert knowledge, was also developed for geometallurgical domaining which characterises the deposit according to its metallurgical response. The concept of geometallurgical dilution was formulated and used for optimising production scheduling in an open-pit case study.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 201

Crushing Plant Dynamics

The performance of a crushing plant is an essential element in achieving efficient production of aggregates or metals. A crushing plant\ub4s operating performance depends on the design and configuration of each individual process unit, the configuration of the plant, the design of the control system, events occurring in the process and the physical properties of the incoming feed. The production process is a continuous process and as such it is also subjected to variations and changes in performance depending on the condition of the process. Crushing plants however, are traditionally simulated with steady-state simulation models which are not capable of predicting these conditions. A different technique is therefore necessary in order to estimate the actual behaviour of the plant with respect to time.Crushing plants are affected by both gradual and discrete changes in the process over time which alters the performance of the entire system, making it dynamic. A dynamic simulation is defined in this thesis as continuous simulations with sets of differential equations with static equations to reproduce the dynamic performance of a system. In this thesis multiple operational issues have been identified in order to achieve adequate process fidelity for simulation purposes. These operational issues have been addressed by introducing methods and models for representing different dynamic aspects of the process. These include: different types of bins to handle misaligned feeding, segregation and different flow behaviour, the use of system identification to measure actuator response to accurately estimate unit response, wear estimation for crushers, mechanistic models for crushers and screens for more accurate estimation of unit dynamics, segmented conveyors that can estimate material flow for conveyors with variable speed drives, parameter selection for optimum process performance, discrete events that occur within the process and different control strategies to capture the process dynamics.Different applications for dynamic simulation have been explored and demonstrated in this thesis. These include: process evaluation, control development, process optimization, operational planning, maintenance scheduling and operator training. Each of these areas puts different constraints on the modelling of crushing plants and the level of fidelity, which is determined by the purpose of the simulation.In conclusion, dynamic simulation of production processes has the ability to provide the user with in-depth understanding about the simulated process, details that are usually not available with static simulations. Multiple factors can affect the performance of a crushing plant, factors that need to be included in the simulation to be able to estimate the actual plant performance

Crushing Plant Dynamics

The performance of a crushing plant is an essential element in achieving efficient production of aggregates or metals. A crushing plant\ub4s operating performance depends on the design and configuration of each individual process unit, the configuration of the plant, the design of the control system, events occurring in the process and the physical properties of the incoming feed. The production process is a continuous process and as such it is also subjected to variations and changes in performance depending on the condition of the process. Crushing plants however, are traditionally simulated with steady-state simulation models which are not capable of predicting these conditions. A different technique is therefore necessary in order to estimate the actual behaviour of the plant with respect to time.Crushing plants are affected by both gradual and discrete changes in the process over time which alters the performance of the entire system, making it dynamic. A dynamic simulation is defined in this thesis as continuous simulations with sets of differential equations with static equations to reproduce the dynamic performance of a system. In this thesis multiple operational issues have been identified in order to achieve adequate process fidelity for simulation purposes. These operational issues have been addressed by introducing methods and models for representing different dynamic aspects of the process. These include: different types of bins to handle misaligned feeding, segregation and different flow behaviour, the use of system identification to measure actuator response to accurately estimate unit response, wear estimation for crushers, mechanistic models for crushers and screens for more accurate estimation of unit dynamics, segmented conveyors that can estimate material flow for conveyors with variable speed drives, parameter selection for optimum process performance, discrete events that occur within the process and different control strategies to capture the process dynamics.Different applications for dynamic simulation have been explored and demonstrated in this thesis. These include: process evaluation, control development, process optimization, operational planning, maintenance scheduling and operator training. Each of these areas puts different constraints on the modelling of crushing plants and the level of fidelity, which is determined by the purpose of the simulation.In conclusion, dynamic simulation of production processes has the ability to provide the user with in-depth understanding about the simulated process, details that are usually not available with static simulations. Multiple factors can affect the performance of a crushing plant, factors that need to be included in the simulation to be able to estimate the actual plant performance