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A dynamic crushing plant simulator implemented in MATLAB/SIMULINK has been connected to a human machine interface at the Chalmers University of Technology in Gothenburg, Sweden. Monitors show sequences from the process connected to the simulations, creating a realistic control room for training. Bearing failure on a conveyor is used as an example., 11 refs., A dynamic crushing plant simulator implemented in MATLAB/SIMULINK has been connected to a human machine interface at the Chalmers University of Technology in Gothenburg, Sweden. Monitors show sequences from the process connected to the simulations, creating a realistic control room for training. Bearing failure on a conveyor is used as an example.

A model and an algorithm are presented for the control of cone crushers, which takes into account the increase in cone crusher settings, and compensates by changing the eccentric speed. The algorithm works on feedback from monitoring and control systems, with the product flows being monitored by mass flow meters. Control parameters are set by management to take into account production targets and market requirements. The system incorporates a previously developed Finite State Machine (FSM) algorithm, which was also tested. The system was tested on an aggregate crushing plant producing 400 000 t/y. The new algorithm increased production by 6.9%, while the FSM increased production by 5.3%., A model and an algorithm are presented for the control of cone crushers, which takes into account the increase in cone crusher settings, and compensates by changing the eccentric speed. The algorithm works on feedback from monitoring and control systems, with the product flows being monitored by mass flow meters. Control parameters are set by management to take into account production targets and market requirements. The system incorporates a previously developed Finite State Machine (FSM) algorithm, which was also tested. The system was tested on an aggregate crushing plant producing 400 000 t/y. The new algorithm increased production by 6.9%, while the FSM increased production by 5.3%.

The generation of optimisation models is presented for three aspects of crushing and their integration is discussed. The three aspects are: selection and operation of a fragmentation process or crushing unit operation, taking into account rock breakage characteristics, definition of fitness functions, optimisation of fragmentation and robustness of optimisation results; optimisation of the plant, considering simultaneous production of several products, several circulating loads, performance analysis, variation of raw material, wear, and variation in product demand over time; and real-time optimisation of a crushing circuit. The interaction of these and their integration into an overall optimisation framework is also considered., The generation of optimisation models is presented for three aspects of crushing and their integration is discussed. The three aspects are: selection and operation of a fragmentation process or crushing unit operation, taking into account rock breakage characteristics, definition of fitness functions, optimisation of fragmentation and robustness of optimisation results; optimisation of the plant, considering simultaneous production of several products, several circulating loads, performance analysis, variation of raw material, wear, and variation in product demand over time; and real-time optimisation of a crushing circuit. The interaction of these and their integration into an overall optimisation framework is also considered.

Control systems for cone crusher settings are widely used to compensate for wear and to protect the machines from high pressure, but they focus on the crusher rather than on the crushed products. Measurement devices such as mass flow meters, including conveyor belt scales, may be used for optimum operation of a crusher from the saleable products point of view. An algorithm was developed for analysing data from the process and converting them to a desired constant set-point value for the closed side setting (CSS), the distance between the rotating inner mantle and the outer concave shell. The algorithm makes a series of step changes in order to determine and maintain an optimum setting. The algorithm was tested at an aggregate crushing plant in Sweden, where it was loaded into a computer that was able to communicate with the crusher control system, to read data from three mass flow meters and to interact with the operators. The algorithm was capable of providing CSS set-points to the automatic setting regulation system. The amount of saleable product from the crushing stage increased by 3.5% over the case in which a fixed closed side setting was used. The algorithm automatically compensates for changes in the feed material and also reduces the need for calibration of the underlying system., Control systems for cone crusher settings are widely used to compensate for wear and to protect the machines from high pressure, but they focus on the crusher rather than on the crushed products. Measurement devices such as mass flow meters, including conveyor belt scales, may be used for optimum operation of a crusher from the saleable products point of view. An algorithm was developed for analysing data from the process and converting them to a desired constant set-point value for the closed side setting (CSS), the distance between the rotating inner mantle and the outer concave shell. The algorithm makes a series of step changes in order to determine and maintain an optimum setting. The algorithm was tested at an aggregate crushing plant in Sweden, where it was loaded into a computer that was able to communicate with the crusher control system, to read data from three mass flow meters and to interact with the operators. The algorithm was capable of providing CSS set-points to the automatic setting regulation system. The amount of saleable product from the crushing stage increased by 3.5% over the case in which a fixed closed side setting was used. The algorithm automatically compensates for changes in the feed material and also reduces the need for calibration of the underlying system.

Cone crushers are commonly used for secondary and tertiary crushing stages in the aggregate and mining industry. It has previously been demonstrated that the discrete element method (DEM) can be used to simulate rock breakage in crushers using a variety of modelling techniques. In order to provide confidence in the simulation results the discrete element method models need to be validated against experimental data. Such validation efforts are scarcely reported in the existing literature and there are no standardized procedures defined. In this paper a laboratory cone crusher is simulated using discrete element method and the results are compared with laboratory experiments. The rock material is modelled using the Bonded Particle Model approach calibrated against single particle breakage experiments. Two case simulations have been performed investigating the influence of eccentric speed. The laboratory crusher is a Morgårdshammar B90 cone crusher that has been equipped with custom machined liners, variable speed drive and a National Instruments data acquisition system. The results provide novel insight regarding the stochastic flow behaviour of particles when exited by the mantle at high frequency. The estimated product size distribution matches the experimental results relatively well when evaluating the corresponding coarse region that is feasible to calculate from the DEM product discharge data. (Authors)., Cone crushers are commonly used for secondary and tertiary crushing stages in the aggregate and mining industry. It has previously been demonstrated that the discrete element method (DEM) can be used to simulate rock breakage in crushers using a variety of modelling techniques. In order to provide confidence in the simulation results the discrete element method models need to be validated against experimental data. Such validation efforts are scarcely reported in the existing literature and there are no standardized procedures defined. In this paper a laboratory cone crusher is simulated using discrete element method and the results are compared with laboratory experiments. The rock material is modelled using the Bonded Particle Model approach calibrated against single particle breakage experiments. Two case simulations have been performed investigating the influence of eccentric speed. The laboratory crusher is a Morgårdshammar B90 cone crusher that has been equipped with custom machined liners, variable speed drive and a National Instruments data acquisition system. The results provide novel insight regarding the stochastic flow behaviour of particles when exited by the mantle at high frequency. The estimated product size distribution matches the experimental results relatively well when evaluating the corresponding coarse region that is feasible to calculate from the DEM product discharge data. (Authors).

In this study, an analytical perspective is used to develop a fundamental model of a jaw crusher. Previously, jaw crushers were modelled in regard to certain aspects, for example, energy consumption (Legendre and Zevenhoven, 2014) or kinematics (Oduori et al., 2015). Approaches to date have been mainly property-specific. In this work a physical modelling approach has been used to derive the modules, which are based on established facts of comminution machines, from the literature. A modelling methodology mainly inspired by Evertsson has been applied (Evertsson, 2000). The modules are divided into kinematics, flow, breakage, capacity, pressure and power. Each module has been derived and tested decoupled from the other modules to provide increased transparency of the module and its behaviour. The results of the modelling are presented for a baseline case of one industrial-scale jaw crusher and compared to manufacturer data. Future work will include validation and DEM simulations.(Authors)., In this study, an analytical perspective is used to develop a fundamental model of a jaw crusher. Previously, jaw crushers were modelled in regard to certain aspects, for example, energy consumption (Legendre and Zevenhoven, 2014) or kinematics (Oduori et al., 2015). Approaches to date have been mainly property-specific. In this work a physical modelling approach has been used to derive the modules, which are based on established facts of comminution machines, from the literature. A modelling methodology mainly inspired by Evertsson has been applied (Evertsson, 2000). The modules are divided into kinematics, flow, breakage, capacity, pressure and power. Each module has been derived and tested decoupled from the other modules to provide increased transparency of the module and its behaviour. The results of the modelling are presented for a baseline case of one industrial-scale jaw crusher and compared to manufacturer data. Future work will include validation and DEM simulations.(Authors).

There is an increasing demand to optimise performance and profit of comminution circuits. Research in this area has resulted in the development of numerous optimisation tools, and recent research has shown that the quality aspects of the production have a great influence on the optimisation results. The quality, cost, profit and capacity of a product are influenced by several parameters, and in order to control all of these parameters it is necessary to use some sort of optimisation algorithm. In this paper, a novel approach to apply e.g. cost, revenue, capacity and quality in order to perform a multi-objective optimisation with the ability to handle dynamic variations of a comminution is presented. The problem with optimisations in general is that the objective function used for optimisation is reduced in complexity in order to save computational time. In a comminution process performance varies with time and in order to perform a correct optimisation the objective function used must be able to handle this type of dynamic behaviour. The process has a given set of constraints that represents the conditions normal in these type of comminution applications. The first step in this paper is to identify if the range of the constraints can cause undesirable production costs when reaching for a given product property. In this step a dynamic response model is described that will be able to address the difficulties with optimising dynamic systems. The next step in the optimisation is the definition of the multi-objective optimisation formulation including constraints for the optimisation. Evaluating the result of the optimisation in combination with a strategy for relaxing constraint can show how to increase overall productivity and still reach certain product properties. The conclusions made in this work are that multi objective optimisation is essential when optimising a comminution circuit against multiple objectives. (Authors)., There is an increasing demand to optimise performance and profit of comminution circuits. Research in this area has resulted in the development of numerous optimisation tools, and recent research has shown that the quality aspects of the production have a great influence on the optimisation results. The quality, cost, profit and capacity of a product are influenced by several parameters, and in order to control all of these parameters it is necessary to use some sort of optimisation algorithm. In this paper, a novel approach to apply e.g. cost, revenue, capacity and quality in order to perform a multi-objective optimisation with the ability to handle dynamic variations of a comminution is presented. The problem with optimisations in general is that the objective function used for optimisation is reduced in complexity in order to save computational time. In a comminution process performance varies with time and in order to perform a correct optimisation the objective function used must be able to handle this type of dynamic behaviour. The process has a given set of constraints that represents the conditions normal in these type of comminution applications. The first step in this paper is to identify if the range of the constraints can cause undesirable production costs when reaching for a given product property. In this step a dynamic response model is described that will be able to address the difficulties with optimising dynamic systems. The next step in the optimisation is the definition of the multi-objective optimisation formulation including constraints for the optimisation. Evaluating the result of the optimisation in combination with a strategy for relaxing constraint can show how to increase overall productivity and still reach certain product properties. The conclusions made in this work are that multi objective optimisation is essential when optimising a comminution circuit against multiple objectives. (Authors).