METHOD AND SYSTEM FOR ONLINE MONITORING AND OPTIMIZATION OF MINING AND MINERAL PROCESSING OPERATIONS
First Claim
1. A processor implemented method for online monitoring and optimization of mining and mineral processing operations, the processor implemented method comprising:
- fetching from a plurality of data sources, by one or more hardware processors, data for a set of variables corresponding to a set of sensors associated with the mining and mineral processing operations;
pre-processing, by the one or more hardware processors, the fetched data corresponding to the set of variables by discarding outliers, performing imputation for adding artificial values at missing positions, organizing data collected at different frequencies to one common frequency, identifying and selecting data based on steady state operation of a process or a sub-process associated with the mining and mineral processing operations;
determining, by the one or more hardware processors, standard operating condition for the mining and mineral processing operations using the pre-processed data corresponding to the set of variables;
segregating each variable among the set of variables into one of drilling-blasting operations, hauling operations, comminution operations, and flotation and concentration operations, wherein segregation is performed in accordance with a master tag list;
generating, by the one or more hardware processors, a set of models based on the segregated set of variables and preprocessed data, wherein the set of models is generated for a set of Key Performance Indicators (KPIs) of interest and a plurality of process parameters of interest associated with the mining and mineral processing operations, wherein the generated set of models comprise a set of Machine learning (ML) models or a set of individualized physics based models or hybrid models, and wherein the set of individualized physics based or hybrid models are generated for the plurality of process parameters based on known physics based models by determining model parameters using non-linear curve fitting;
simulating, by the one or more hardware processors, current operating condition of the mining and mineral processing operations corresponding to the set of variables based on at least one of;
the generated ML models, the set of individualized physics based or hybrid models of the KPIs and the plurality of process parameters, wherein the simulated current operating condition provides current values for KPIs and the plurality of process parameters of interest; and
hierarchically optimizing, by the one or more hardware processors, the KPIs of interest of the mining and mineral processing operations to update the current operating conditions of a subset of variables among the set of variables of the mining and mineral processing operations, wherein the KPIs of interest are from at least one of mining operations, a comminution circuit, and a flotation and concentration circuit.
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Abstract
Monitoring and analysis of plurality of operations in mining and mineral processing is critical to achieve optimized performance. Existing tools are specific to one or other individual operations and this individuality introduces limitations for end to end monitoring of entire mining to mineral processing operations. Method and system for online monitoring and optimization of mining and mineral processing operations providing an integrated approach utilizing short-term mining plan data, information generated using established drill and blast design software, simulation models of fragmentation, crushing, screening, grinding and flotation to arrive at an optimized charge plan and set points for controllers is disclosed. The proposed method and system improves key performance indicators such as cost of mining operations, specific energy consumption in comminution circuit, maximizes yield of desired particle size, and maximizes grade and recovery of mineral of interest while considering operational constraints.
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Citations
17 Claims
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1. A processor implemented method for online monitoring and optimization of mining and mineral processing operations, the processor implemented method comprising:
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fetching from a plurality of data sources, by one or more hardware processors, data for a set of variables corresponding to a set of sensors associated with the mining and mineral processing operations; pre-processing, by the one or more hardware processors, the fetched data corresponding to the set of variables by discarding outliers, performing imputation for adding artificial values at missing positions, organizing data collected at different frequencies to one common frequency, identifying and selecting data based on steady state operation of a process or a sub-process associated with the mining and mineral processing operations; determining, by the one or more hardware processors, standard operating condition for the mining and mineral processing operations using the pre-processed data corresponding to the set of variables; segregating each variable among the set of variables into one of drilling-blasting operations, hauling operations, comminution operations, and flotation and concentration operations, wherein segregation is performed in accordance with a master tag list; generating, by the one or more hardware processors, a set of models based on the segregated set of variables and preprocessed data, wherein the set of models is generated for a set of Key Performance Indicators (KPIs) of interest and a plurality of process parameters of interest associated with the mining and mineral processing operations, wherein the generated set of models comprise a set of Machine learning (ML) models or a set of individualized physics based models or hybrid models, and wherein the set of individualized physics based or hybrid models are generated for the plurality of process parameters based on known physics based models by determining model parameters using non-linear curve fitting; simulating, by the one or more hardware processors, current operating condition of the mining and mineral processing operations corresponding to the set of variables based on at least one of;
the generated ML models, the set of individualized physics based or hybrid models of the KPIs and the plurality of process parameters, wherein the simulated current operating condition provides current values for KPIs and the plurality of process parameters of interest; andhierarchically optimizing, by the one or more hardware processors, the KPIs of interest of the mining and mineral processing operations to update the current operating conditions of a subset of variables among the set of variables of the mining and mineral processing operations, wherein the KPIs of interest are from at least one of mining operations, a comminution circuit, and a flotation and concentration circuit. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system for online monitoring and optimization of mining and mineral processing operations, the system comprising:
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a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to; fetch from a plurality of data sources data for a set of variables corresponding to a set of sensors associated with the mining and mineral processing operations; pre-process the fetched data corresponding to the set of variables by discarding outliers, performing imputation for adding artificial values at missing positions, organizing data collected at different frequencies to one common frequency, identifying and selecting data based on steady state operation of a process or a sub-process associated with the mining and mineral processing operations; determine standard operating condition for the mining and mineral processing operations using the pre-processed data corresponding to the set of variables; segregate each variable among the set of variables into one of a drilling-blasting operations, hauling operations, comminution operations, and flotation- and concentration operations, wherein segregation is performed in accordance with a master tag list; generate a set of models based on the segregated set of variables and preprocessed data, wherein the set of models is generated for a set of Key Performance Indicators (KPIs) of interest and a plurality of process parameters of interest associated with the mining and mineral processing operations, wherein the generated set of models comprise a set of Machine learning (ML) models, or a set of individualized physics based models or hybrid models, and wherein the set of individualized physics based models or hybrid models are generated for the plurality of process parameters based on known physics based models by determining model parameters using non-linear curve fitting; simulate current operating condition of the mining and mineral processing operations corresponding to the set of variables based on at least one of;
the generated ML models, the individualized physics based, the set of hybrid models of the KPIs and the plurality of process parameters, wherein the simulated current operating condition provides current values for KPIs and the plurality of process parameters of interest; andhierarchically optimize the KPIs of interest of the mining and mineral processing operations to update the current operating conditions of a subset of variables among the set of variables of the mining and mineral processing operations, wherein the KPIs of interest are from at least one of mining operations, a comminution circuit, and a flotation and concentration circuit. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. One or more non-transitory machine readable information storage media storing instructions which, when executed by one or more hardware processors, causes the one or more hardware processors to execute a method comprising:
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fetching from a plurality of data sources data for a set of variables corresponding to a set of sensors associated with the mining and mineral processing operations; pre-processing the fetched data corresponding to the set of variables by discarding outliers, performing imputation for adding artificial values at missing positions, organizing data collected at different frequencies to one common frequency, identifying and selecting data based on steady state operation of a process or a sub-process associated with the mining and mineral processing operations; determining standard operating condition for the mining and mineral processing operations using the pre-processed data corresponding to the set of variables; segregating each variable among the set of variables into one of drilling-blasting operations, hauling operations, comminution operations, and flotation and concentration operations, wherein segregation is performed in accordance with a master tag list; generating a set of models based on the segregated set of variables and preprocessed data, wherein the set of models is generated for a set of Key Performance Indicators (KPIs) of interest and a plurality of process parameters of interest associated with the mining and mineral processing operations, wherein the generated set of models comprise a set of Machine learning (ML) models or a set of individualized physics based models or hybrid models, wherein the set of individualized physics based or hybrid models are generated for the plurality of process parameters based on known physics based models by determining model parameters using non-linear curve fitting; simulating current operating condition of the mining and mineral processing operations corresponding to the set of variables based on at least one of;
the generated ML models, the set of individualized physics based or hybrid models of the KPIs and the plurality of process parameters, wherein the simulated current operating condition provides current values for KPIs and the plurality of process parameters of interest; andhierarchically optimizing the KPIs of interest of the mining and mineral processing operations to update the current operating conditions of a subset of variables among the set of variables of the mining and mineral processing operations, wherein the KPIs of interest are from at least one of mining operations, a comminution circuit, and a flotation and concentration circuit.
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Specification