Method and system for online monitoring and optimization of mining and mineral processing operations

A system for online monitoring and optimization of mining and mineral processing operations, the system comprising:

• 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 for the drilling-blasting operations, hauling operations, comminution operations, and flotation and concentration operations based on the segregated set of variables and preprocessed data, wherein the set of models are utilized to evaluate effect of a plurality to process parameters of interest on a set of Key Performance Indicators (KPIs) of interest associated with the drilling-blasting operations, hauling operations, comminution operations, and flotation and concentration 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 the generated set of models for 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;
• and
• hierarchically 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, wherein optimizing the KPIs of interest of the mining and mineral processing operations is in accordance with a phase-wise and hierarchical procedure comprises:
• creating drilling pattern and a preliminary charge pattern based on short term mining plan;
• performing production drilling operations to collect Monitoring While Drilling (MWD) data, wherein the MWD data comprise information of drill rod revolution rate, rotary torque, pull down force, drilling rate, angular velocity and penetration rate;
• using the MWD data and drilling pattern to estimate the geotechnical parameters comprising specific fracture energy and blasting parameters, further comprising, air deck length, stemming height for each hole to be blasted;
• designing a preliminary charge plan, comprising information of charge mix for each of the hole, based on the estimated geotechnical parameters and drilling pattern;
• estimating fragment size distribution (FSD) using the generated models, wherein the generated models utilize the estimated blasting parameters and the designed charge plan, wherein the estimated FSD along with relevant process parameters are used to simulate the expected KPIs of mining operations comprising hauling costs, stockpile throughput, flyrock size, velocity and range and cost of explosives;
• utilizing the estimated FSD and the process parameters relevant for the mining and mineral processing operations to estimate KPIs of mineral processing operations comprising specific energy consumption, screening efficiency, particle size distribution at important points during comminution circuit along with recovery and grade of the mineral of interest in flotation and concentration circuit, using ML models, physics based and hybrid models;
• and
• recursively performing either one or all of the above steps to obtain optimized charge plan and set points of process parameters of mining and mineral processing operations that globally minimize a customized objective function expressed in terms of KPIs of interest.

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:

• 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, by the one or more hardware processors, a set of models for the drilling-blasting operations, hauling operations, comminution operations, and flotation and concentration operations based on the segregated set of variables and preprocessed data, wherein the set of models are utilized to evaluate effect of a plurality to process parameters of interest on a set of Key Performance Indicators (KPIs) of interest associated with the drilling-blasting operations, hauling operations, comminution operations, and flotation and concentration 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 the generated set of models for 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 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, wherein optimizing the KPIs of interest of the mining and mineral processing operations is in accordance with a phase-wise and hierarchical procedure comprises:
• creating drilling pattern and a preliminary charge pattern based on short term mining plan;
• performing production drilling operations to collect Monitoring While Drilling (MWD) data, wherein the MWD data comprise information of drill rod revolution rate, rotary torque, pull down force, drilling rate, angular velocity and penetration rate;
• using the MWD data and drilling pattern to estimate the geotechnical parameters comprising specific fracture energy and blasting parameters, further comprising, air deck length, stemming height for each hole to be blasted;
• designing a preliminary charge plan, comprising information of charge mix for each of the hole, based on the estimated geotechnical parameters and drilling pattern;
• estimating fragment size distribution (FSD) using the generated models, wherein the generated models utilize the estimated blasting parameters and the designed charge plan, wherein the estimated FSD along with relevant process parameters are used to simulate the expected KPIs of mining operations comprising hauling costs, stockpile throughput, flyrock size, velocity and range and cost of explosives;
• utilizing the estimated FSD and the process parameters relevant for the mining and mineral processing operations to estimate KPIs of mineral processing operations comprising specific energy consumption, screening efficiency, particle size distribution at important points during comminution circuit along with recovery and grade of the mineral of interest in flotation and concentration circuit, using ML models, physics based and hybrid models;
• and
• recursively performing either one or all of the above steps to obtain optimized charge plan and set points of process parameters of mining and mineral processing operations that globally minimize a customized objective function expressed in terms of KPIs of interest.