The Experts below are selected from a list of 585 Experts worldwide ranked by ideXlab platform
Magnus Evertsson - One of the best experts on this subject based on the ideXlab platform.
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Applying linear model predictive control to Crushing Circuit simulations
2019Co-Authors: Marcus Johansson, Magnus EvertssonAbstract:In minerals processing control is vital to increasing performance and output of a plant. Model predictive control (MPC) is one of the most effective control strategies used in process industry. Sbarbaro (Sbarbaro and Del Villar, 2010), Asbjornsson (Asbjornsson, 2015), and Legare (Legare et al., 2016) have for example highlighted the possibility to simulate, develop and tune control systems using plant simulators within minerals processing. In more recent years the use of model predictive controllers have increased, and many process industries now utilize more advanced control methods to regulate their processes, raising the question regarding how to, build, test and verify a proposed control solution successfully. In this research, a linear model predictive controller is formulated and applied to a simulator of an existing tertiary Crushing Circuit. The Circuit has in previous work by Johansson (Johansson, 2017) been modeled in MATLAB Simulink and calibrated relative plant data to an average error of less than 10% over 8 hours of production. The work demonstrates how to formulate the controller model structure to describe conveyor belts, storage components, screens, crushers, and control objectives for a minerals processing Circuit. The work illustrates how to apply and successfully evaluate possible upsides in a Circuit. The advantage of a Crushing Circuit simulator is that it can be configured, tuned and modified in any possible way, which is of great benefit to developers and engineers. Additionally, examples of how to use estimated measures to improve the performance of the plant are demonstrated. The control performance is finally evaluated for three different controller configurations.
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Modelling and simulation of dynamic Crushing plant behavior with MATLAB/Simulink
Minerals Engineering, 2013Co-Authors: Gauti Asbjörnsson, Erik Hulthén, Magnus EvertssonAbstract:Every process is subjected to changes in performance and efficiency over time. These dynamics can originate upstream and be inherent through the process or occur anywhere in the downstream process. Traditional plant simulations are performed with steady-state simulation, which are limited to give the performance in an ideal situation. However, plant performance usually tends to deviate away from the predicted plant performance. These dynamics are usually consequences of an altered state of the plant due to factors such as natural variation, unmatched, inappropriate or degrading equipment performance and/or stochastic events. This paper presents a novel approach for simulating dynamic plant behavior and evaluating effects from process modification through dynamic simulations with MATLAB/Simulink. An example of an existing Crushing Circuit is used to illustrate the functionality and the advantage of using a dynamic simulator. The results and knowledge gained from the simulation can provide a base for optimizing a robust production output in the form optimal utilization, energy efficiency or higher product quality.
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modelling and dynamic simulation of gradual performance deterioration of a Crushing Circuit including time dependence and wear
Minerals Engineering, 2012Co-Authors: Gauti Asbjörnsson, Erik Hulthén, Magnus EvertssonAbstract:The use of steady-state models in process simulation is a well-established method in many process industries. Designing a large Crushing plant by relying on steady-state simulations alone will not generally provide the full picture of possible operational performance. The dynamics and variation between equipment and stochastic events can significantly reduce predicted plant performance. In order to dynamically simulate the Crushing Circuit, models for process equipment need to be further developed. The purpose of this paper is to create a wear function for an existing Particle Size Distribution model (i.e., a Swebrec-function) with data obtained from a real crusher operating at gradually increasing closed side settings. This is done to create an accurate and updated model of the crusher in which the transient consequences of wear are captured. The Swebrec-function and correlation model were implemented into simulation software with simulated events; this simulation was validated with actual process readings. Improved simulations were then attained with the developed functions.
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Modelling and dynamic simulation of gradual performance deterioration of a Crushing Circuit – Including time dependence and wear
Minerals Engineering, 2012Co-Authors: Gauti Asbjörnsson, Erik Hulthén, Magnus EvertssonAbstract:The use of steady-state models in process simulation is a well-established method in many process industries. Designing a large Crushing plant by relying on steady-state simulations alone will not generally provide the full picture of possible operational performance. The dynamics and variation between equipment and stochastic events can significantly reduce predicted plant performance. In order to dynamically simulate the Crushing Circuit, models for process equipment need to be further developed. The purpose of this paper is to create a wear function for an existing Particle Size Distribution model (i.e., a Swebrec-function) with data obtained from a real crusher operating at gradually increasing closed side settings. This is done to create an accurate and updated model of the crusher in which the transient consequences of wear are captured. The Swebrec-function and correlation model were implemented into simulation software with simulated events; this simulation was validated with actual process readings. Improved simulations were then attained with the developed functions.
Cortez Cruz, Carmen Rosa - One of the best experts on this subject based on the ideXlab platform.
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Estudio de la dureza de la caliza para la selección de una planta de chancado en la cantera Italo, minera P’huyu -Yuraq II EIRL, Cajamarca 2016
'Dipartimento di Economia Universita di Perugia (IT)', 2017Co-Authors: Miranda Condori, Richard Mario, Cortez Cruz, Carmen RosaAbstract:ABSTRACT This research began with continuous field visits and sampling of limestone to find the characteristics of the structure of the quarry and physical characteristics of limestone.The RQD 85.90% indicating a good quality rock, limestone hardness Pi (78.6 MPa) and Bond work index of 14.1 with Wi she found Data collection was performed in the field to estimate the production of limestone by manual methods and manual production cost of 8.6 soles per ton was determined additionally a machining cost of 1.0 soles per ton of limestone was estimated. With the data structure of the quarry and physical properties of the limestone could define a Crushing Circuit that the cost of a hopper, feeder, Crushing jaws and vibrating screen, additional equipment was selected for a Crushing plant 25 to 60 tons per hour. Finally, the economic evaluation of the project was conducted and economic indicators show that it is feasible to install a limestone Crushing plant, the initial investment would be S / 189.720 including installation costs and time that could recover the investment would be in 9 months after the operation start of the Crushing plant. Economic indicators that support a decision to make the investment are an NPV of S / 14.774 with an IRR of 11% and a return period of 9 months, considering a discount rate of 10%.TesisEste trabajo de investigación se inició con continuas visitas a campo y toma de muestras de caliza para encontrar las características de las discontinuidades de la roca caliza. Se encontró un RQD de 85.90% que indica una calidad de roca buena, la dureza de la caliza Pi (78.6 MPa) y el índice de trabajo de Bond con un Wi de 14.1 KW/h TC. Se realizó una toma de datos en campo para estimar la producción de caliza por métodos manuales, y se determinó un costo de producción manual de 8.6 soles por tonelada, adicionalmente se estimó un costo mecanizado de 1.0 soles por tonelada de caliza. Con los datos de las características de las discontinuidades se pudo definir un Circuito de chancado que consta de una tolva, alimentador, chancado de quijadas y zaranda vibratoria, adicionalmente se seleccionó los equipos para una planta de chancado de 25 a 60 toneladas por hora. Finalmente se realizó la evaluación económica del proyecto y los indicadores económicos muestran que es factible instalar una planta de chancado de caliza, la inversión inicial sería de S/.189, 720 incluyendo los costos de instalación y el tiempo que se podría recuperar la inversión sería en 9 meses después de que inicie la operación de la planta de chancado. Los indicadores económicos que soportan una decisión para hacer la inversión con un VAN de S/.14, 774 con un TIR de 10% y un periodo de retorno de 9 meses, considerando una tasa de descuento de 10%
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Estudio de la dureza de la caliza para la selección de una planta de chancado en la cantera Italo, minera P’huyu -Yuraq II EIRL, Cajamarca 2016.
Universidad Privada del Norte, 2017Co-Authors: Cortez Cruz, Carmen Rosa, Miranda Condori, Richard MarioAbstract:RESUMEN Este trabajo de investigación se inició con continuas visitas a campo y toma de muestras de caliza para encontrar las características de las discontinuidades de la roca caliza. Se encontró un RQD de 85.90% que indica una calidad de roca buena, la dureza de la caliza Pi (78.6 MPa) y el índice de trabajo de Bond con un Wi de 14.1 KW/h TC. Se realizó una toma de datos en campo para estimar la producción de caliza por métodos manuales, y se determinó un costo de producción manual de 8.6 soles por tonelada, adicionalmente se estimó un costo mecanizado de 1.0 soles por tonelada de caliza. Con los datos de las características de las discontinuidades se pudo definir un Circuito de chancado que consta de una tolva, alimentador, chancado de quijadas y zaranda vibratoria, adicionalmente se seleccionó los equipos para una planta de chancado de 25 a 60 toneladas por hora. Finalmente se realizó la evaluación económica del proyecto y los indicadores económicos muestran que es factible instalar una planta de chancado de caliza, la inversión inicial sería de S/.189, 720 incluyendo los costos de instalación y el tiempo que se podría recuperar la inversión sería en 9 meses después de que inicie la operación de la planta de chancado. Los indicadores económicos que soportan una decisión para hacer la inversión con un VAN de S/.14, 774 con un TIR de 10% y un periodo de retorno de 9 meses, considerando una tasa de descuento de 10%.ABSTRACT This research began with continuous field visits and sampling of limestone to find the characteristics of the structure of the quarry and physical characteristics of limestone.The RQD 85.90% indicating a good quality rock, limestone hardness Pi (78.6 MPa) and Bond work index of 14.1 with Wi she found Data collection was performed in the field to estimate the production of limestone by manual methods and manual production cost of 8.6 soles per ton was determined additionally a machining cost of 1.0 soles per ton of limestone was estimated. With the data structure of the quarry and physical properties of the limestone could define a Crushing Circuit that the cost of a hopper, feeder, Crushing jaws and vibrating screen, additional equipment was selected for a Crushing plant 25 to 60 tons per hour. Finally, the economic evaluation of the project was conducted and economic indicators show that it is feasible to install a limestone Crushing plant, the initial investment would be S / 189.720 including installation costs and time that could recover the investment would be in 9 months after the operation start of the Crushing plant. Economic indicators that support a decision to make the investment are an NPV of S / 14.774 with an IRR of 11% and a return period of 9 months, considering a discount rate of 10%
Miranda Condori, Richard Mario - One of the best experts on this subject based on the ideXlab platform.
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Estudio de la dureza de la caliza para la selección de una planta de chancado en la cantera Italo, minera P’huyu -Yuraq II EIRL, Cajamarca 2016
'Dipartimento di Economia Universita di Perugia (IT)', 2017Co-Authors: Miranda Condori, Richard Mario, Cortez Cruz, Carmen RosaAbstract:ABSTRACT This research began with continuous field visits and sampling of limestone to find the characteristics of the structure of the quarry and physical characteristics of limestone.The RQD 85.90% indicating a good quality rock, limestone hardness Pi (78.6 MPa) and Bond work index of 14.1 with Wi she found Data collection was performed in the field to estimate the production of limestone by manual methods and manual production cost of 8.6 soles per ton was determined additionally a machining cost of 1.0 soles per ton of limestone was estimated. With the data structure of the quarry and physical properties of the limestone could define a Crushing Circuit that the cost of a hopper, feeder, Crushing jaws and vibrating screen, additional equipment was selected for a Crushing plant 25 to 60 tons per hour. Finally, the economic evaluation of the project was conducted and economic indicators show that it is feasible to install a limestone Crushing plant, the initial investment would be S / 189.720 including installation costs and time that could recover the investment would be in 9 months after the operation start of the Crushing plant. Economic indicators that support a decision to make the investment are an NPV of S / 14.774 with an IRR of 11% and a return period of 9 months, considering a discount rate of 10%.TesisEste trabajo de investigación se inició con continuas visitas a campo y toma de muestras de caliza para encontrar las características de las discontinuidades de la roca caliza. Se encontró un RQD de 85.90% que indica una calidad de roca buena, la dureza de la caliza Pi (78.6 MPa) y el índice de trabajo de Bond con un Wi de 14.1 KW/h TC. Se realizó una toma de datos en campo para estimar la producción de caliza por métodos manuales, y se determinó un costo de producción manual de 8.6 soles por tonelada, adicionalmente se estimó un costo mecanizado de 1.0 soles por tonelada de caliza. Con los datos de las características de las discontinuidades se pudo definir un Circuito de chancado que consta de una tolva, alimentador, chancado de quijadas y zaranda vibratoria, adicionalmente se seleccionó los equipos para una planta de chancado de 25 a 60 toneladas por hora. Finalmente se realizó la evaluación económica del proyecto y los indicadores económicos muestran que es factible instalar una planta de chancado de caliza, la inversión inicial sería de S/.189, 720 incluyendo los costos de instalación y el tiempo que se podría recuperar la inversión sería en 9 meses después de que inicie la operación de la planta de chancado. Los indicadores económicos que soportan una decisión para hacer la inversión con un VAN de S/.14, 774 con un TIR de 10% y un periodo de retorno de 9 meses, considerando una tasa de descuento de 10%
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Estudio de la dureza de la caliza para la selección de una planta de chancado en la cantera Italo, minera P’huyu -Yuraq II EIRL, Cajamarca 2016.
Universidad Privada del Norte, 2017Co-Authors: Cortez Cruz, Carmen Rosa, Miranda Condori, Richard MarioAbstract:RESUMEN Este trabajo de investigación se inició con continuas visitas a campo y toma de muestras de caliza para encontrar las características de las discontinuidades de la roca caliza. Se encontró un RQD de 85.90% que indica una calidad de roca buena, la dureza de la caliza Pi (78.6 MPa) y el índice de trabajo de Bond con un Wi de 14.1 KW/h TC. Se realizó una toma de datos en campo para estimar la producción de caliza por métodos manuales, y se determinó un costo de producción manual de 8.6 soles por tonelada, adicionalmente se estimó un costo mecanizado de 1.0 soles por tonelada de caliza. Con los datos de las características de las discontinuidades se pudo definir un Circuito de chancado que consta de una tolva, alimentador, chancado de quijadas y zaranda vibratoria, adicionalmente se seleccionó los equipos para una planta de chancado de 25 a 60 toneladas por hora. Finalmente se realizó la evaluación económica del proyecto y los indicadores económicos muestran que es factible instalar una planta de chancado de caliza, la inversión inicial sería de S/.189, 720 incluyendo los costos de instalación y el tiempo que se podría recuperar la inversión sería en 9 meses después de que inicie la operación de la planta de chancado. Los indicadores económicos que soportan una decisión para hacer la inversión con un VAN de S/.14, 774 con un TIR de 10% y un periodo de retorno de 9 meses, considerando una tasa de descuento de 10%.ABSTRACT This research began with continuous field visits and sampling of limestone to find the characteristics of the structure of the quarry and physical characteristics of limestone.The RQD 85.90% indicating a good quality rock, limestone hardness Pi (78.6 MPa) and Bond work index of 14.1 with Wi she found Data collection was performed in the field to estimate the production of limestone by manual methods and manual production cost of 8.6 soles per ton was determined additionally a machining cost of 1.0 soles per ton of limestone was estimated. With the data structure of the quarry and physical properties of the limestone could define a Crushing Circuit that the cost of a hopper, feeder, Crushing jaws and vibrating screen, additional equipment was selected for a Crushing plant 25 to 60 tons per hour. Finally, the economic evaluation of the project was conducted and economic indicators show that it is feasible to install a limestone Crushing plant, the initial investment would be S / 189.720 including installation costs and time that could recover the investment would be in 9 months after the operation start of the Crushing plant. Economic indicators that support a decision to make the investment are an NPV of S / 14.774 with an IRR of 11% and a return period of 9 months, considering a discount rate of 10%
Marcus Johansson - One of the best experts on this subject based on the ideXlab platform.
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Efficient modeling and control of Crushing processes in minerals processing
2019Co-Authors: Marcus JohanssonAbstract:Modeling and simulation is a tool to explore and increase the understanding of a phenomenon. This thesis focuses on developing models of crushers and equipment used in the mining industry. Specifically, the focus is on a branch of modeling called time dynamic modeling which is a model that gives an output as a function of time. The work is divided into three areas: physical modeling, control modeling, and Circuit modeling. Physical modeling deals with how to develop high fidelity unit models of equipment, in this thesis, a model of a jaw crusher and of an HPGR are presented. These models are aimed to be predictive and should predict the process variables under a specific set of operating conditions. The models are developed with the process parameters that are used in the physical unit, in the case of the HPGR, roller speed, and hydraulic pressure. The parameters within the models are parameters with units and have real physical meaning; for example, a dimension of the machine. The topic of control modeling focuses on how to apply the knowledge from modeling in the control domain to improve operations. An example of setting up a model predictive controller and using it to control a Crushing Circuit simulation is demonstrated. Model predictive control is an optimal control strategy that can be used to drive the Circuit towards a specific goal. As the demand is increased on the mining companies to perform better these types of controllers and operation improving actions are important. This thesis aims to target some of the challenges involved in improving plant operation and control. Within Circuit modeling, a broader perspective is taken to study the operations of an entire Circuit or plant. The study presented in this thesis focuses on how sensitive a plant is to variations and how the plant design itself will affect the plant's ability to cope with variations. The approach has been to simulate faster and to use less complex models many times to determine limits and ranges. The method shows potential to understand a Circuit better before it is built. The outcome of the research is a better understanding of how to model machinery, such as the HPGR and the jaw crusher. By developing high fidelity models, insights are gained on how to move between the different modeling domains. The knowledge is useful for studies of Circuits, and how to set up optimal controllers. Especially controllers that require models of a specific type or models that have to be fast to simulate.
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Applying linear model predictive control to Crushing Circuit simulations
2019Co-Authors: Marcus Johansson, Magnus EvertssonAbstract:In minerals processing control is vital to increasing performance and output of a plant. Model predictive control (MPC) is one of the most effective control strategies used in process industry. Sbarbaro (Sbarbaro and Del Villar, 2010), Asbjornsson (Asbjornsson, 2015), and Legare (Legare et al., 2016) have for example highlighted the possibility to simulate, develop and tune control systems using plant simulators within minerals processing. In more recent years the use of model predictive controllers have increased, and many process industries now utilize more advanced control methods to regulate their processes, raising the question regarding how to, build, test and verify a proposed control solution successfully. In this research, a linear model predictive controller is formulated and applied to a simulator of an existing tertiary Crushing Circuit. The Circuit has in previous work by Johansson (Johansson, 2017) been modeled in MATLAB Simulink and calibrated relative plant data to an average error of less than 10% over 8 hours of production. The work demonstrates how to formulate the controller model structure to describe conveyor belts, storage components, screens, crushers, and control objectives for a minerals processing Circuit. The work illustrates how to apply and successfully evaluate possible upsides in a Circuit. The advantage of a Crushing Circuit simulator is that it can be configured, tuned and modified in any possible way, which is of great benefit to developers and engineers. Additionally, examples of how to use estimated measures to improve the performance of the plant are demonstrated. The control performance is finally evaluated for three different controller configurations.
Evertsson Magnus - One of the best experts on this subject based on the ideXlab platform.
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Applying linear model predictive control to Crushing Circuit simulations
2019Co-Authors: Johansson Marcus, Evertsson MagnusAbstract:In minerals processing control is vital to increasing performance and output of a plant. Model predictive control (MPC) is one of the most effective control strategies used in process industry. Sb\ue1rbaro (Sb\ue1rbaro and Del Villar, 2010), Asbj\uf6rnsson (Asbj\uf6rnsson, 2015), and L\ue9gar\ue9 (L\ue9gar\ue9 et al., 2016) have for example highlighted the possibility to simulate, develop and tune control systems using plant simulators within minerals processing. In more recent years the use of model predictive controllers have increased, and many process industries now utilize more advanced control methods to regulate their processes, raising the question regarding how to, build, test and verify a proposed control solution successfully. In this research, a linear model predictive controller is formulated and applied to a simulator of an existing tertiary Crushing Circuit. The Circuit has in previous work by Johansson (Johansson, 2017) been modeled in MATLAB Simulink and calibrated relative plant data to an average error of less than 10% over 8 hours of production. The work demonstrates how to formulate the controller model structure to describe conveyor belts, storage components, screens, crushers, and control objectives for a minerals processing Circuit. The work illustrates how to apply and successfully evaluate possible upsides in a Circuit. The advantage of a Crushing Circuit simulator is that it can be configured, tuned and modified in any possible way, which is of great benefit to developers and engineers. Additionally, examples of how to use estimated measures to improve the performance of the plant are demonstrated. The control performance is finally evaluated for three different controller configurations