A Study of the Influence of Measurement Volume, Blending Ratios and Sensor Precision on Real-Time Reconciliation of Grade Control Models

Wambeke, T.; Benndorf, Jörg

doi:10.1007/s11004-018-9740-3

Cited by 6 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, this can be easily extended to multiple SMUs observed simultaneously. In this case, assumptions about the sensor precision, measurement volumes, update intervals and blending ratios should be made following Wambeke andBenndorf (2017, 2018). The classical Kalman filter implements the Kalman gain matrix W t+1 as…”

Section: Implementation Strategymentioning

confidence: 99%

See 1 more Smart Citation

Resource Model Updating For Compositional Geometallurgical Variables

et al. 2020

View full text Add to dashboard Cite

In the field of mineral resources extraction, one main challenge is to meet production targets in terms of geometallurgical properties. These properties influence the processing of the ore and are often represented in resource modeling by coregionalized variables with a complex relationship between them. Valuable data are available about geometalurgical properties and their interaction with the beneficiation process given sensor technologies during production monitoring. The aim of this research is to update resource models as new observations become available. A popular method for updating is the ensemble Kalman filter. This method relies on Gaussian assumptions and uses a set of realizations of the simulated models to derive sample covariances that can propagate the uncertainty between real observations and simulated ones. Hence, the relationship among variables has a compositional nature, such that updating these models while keeping the compositional constraints is a practical requirement in order to improve the accuracy of the updated models. This paper presents an updating framework for compositional data based on ensemble Kalman filter which allows us to work with compositions that are transformed into a multivariate Gaussian space by log-ratio transformation and flow anamorphosis. This flow anamorphosis, transforms the distribution of the variables to joint normality while reasonably keeping the dependencies between components. Furthermore, the positiveness of those variables, after updating the simulated models, is satisfied. The method is implemented in a bauxite deposit, demonstrating the performance of the proposed approach.

show abstract

Section: Implementation Strategymentioning

confidence: 99%

“…The reported parameter defines the measurement error on a scale of a single SMU. The influence of this parameter on the overall performance of the algorithm has been addressed by Wambeke and Benndorf (2018). In this paper, only this error has been considered to test the proposed framework.…”

Section: Updating Processmentioning

confidence: 99%

Resource Model Updating For Compositional Geometallurgical Variables

et al. 2020

View full text Add to dashboard Cite

show abstract

“…New digital technologies, including the development of advanced sensors and monitoring devices, have enabled the acquisition of new information about the performance of the different components of a mining complex that affect the flow of materials in a value chain. Sensors installed on drills, shovels, trucks, conveyor belts, crushers, and mineral processing mills (Dalm et al 2014(Dalm et al , 2018Goetz et al 2009;Iyakwari et al 2016;Wambeke and Benndorf 2018) continuously measure the performance of the mining equipment and processing streams (processing and handling facilities), as well as different pertinent properties of the materials being handled. In addition to the new sensor information, conventional sources of new information include blasthole sampling that determines the pertinent properties of materials extracted (Rossi and Deutsch 2013), monitoring devices that measure the performance of equipment (Koellner et al 2004), and tracking devices that track the location of materials (Brewer et al 1999;Rosa et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex

Kumar

Dimitrakopoulos

Maulen³

2020

J Intell Manuf

View full text Add to dashboard Cite

A mining complex is an integrated value chain where the materials extracted from a group of mineral deposits are sent to different processing streams to produce sellable products. A major short-term decision in a mining complex is to determine the flow of materials that first includes deciding which handling facilities to send the extracted materials and then determining how to utilize the processing facilities. The flow of materials through the mining complex is significantly dependent on the performance of and interaction between its different components. New digital technologies, including the development of advanced sensors and monitoring devices, have enabled a mining complex to acquire new information about the performance of its different components. This paper proposes a new continuous updating framework that combines policy gradient reinforcement learning and an extended ensemble Kalman filter to adapt the short-term flow of materials in a mining complex with incoming information. The framework first uses a new extended ensemble Kalman filter to update the uncertainty models of the different components of a mining complex with new incoming information. Then, the updated uncertainty models are fed to a neural network trained using a policy gradient reinforcement learning algorithm to adapt the short-term flow of materials in a mining complex. The proposed framework is applied to a copper mining complex and shows its ability to efficiently adapt the short-term flow of materials in an operational mining environment with new incoming information. The framework better meets the different production targets while improving the cumulative cash flow compared to industry standard approaches.

show abstract

Data Assimilation for Resource Model Updating

Benndorf

2020

SpringerBriefs in Applied Sciences and Technology

View full text Add to dashboard Cite

A Study of the Influence of Measurement Volume, Blending Ratios and Sensor Precision on Real-Time Reconciliation of Grade Control Models

Cited by 6 publications

References 5 publications

Resource Model Updating For Compositional Geometallurgical Variables

Resource Model Updating For Compositional Geometallurgical Variables

Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex

Data Assimilation for Resource Model Updating

Contact Info

Product

Resources

About