Influencing a mining process

Abstract

A material and energy requirements analysing arrangement for influencing a mining process performed at a production site by a mining system using energy generated by an energy supply system obtains (S100) measurements of the mining process, applies (S110), in a soft sensor, available data of at least a part of the mining process in at least one dynamic mining model, the available data comprising the measurements, determines (S120), in the soft sensor, at least one key performance indicator based on the application of the available data in the at least one mining model together with dynamic properties of the mining process, the at least one key performance indicator comprising a sustainability key performance indicator, applies (S130) the at least one key performance indicator in a control problem linked to the production of the mining process and energy available from the energy supply system, optimizes (S140) the control problem using production goals and energy supply limitations as constraints and influences (S150) the mining process based on the optimized control problem.

Description

[0001] INFLUENCING A MINING PROCESS

[0002] FIELD OF THE INVENTION

[0003] The present invention relates to a method, material and energy analysing arrangement, computer program and computer program product for influencing a mining process performed at a production site by a mining system using energy generated by an energy supply system as well as to an operations system comprising a mining system, energy supply system and material and energy analysing arrangement.

[0004] BACKGROUND

[0005] Industrial processes, such as mining processes performed by mining systems, do in many cases require a lot of energy.

[0006] For this and other reasons, it maybe of interest to provide the mining process with energy from an energy supply system, which energy supply system may comprise renewable energy sources generating renewable energy.

[0007] Furthermore, it may be of interest to influence the operation of the mine using an optimizing function and Key Performance Indicators obtained via soft sensors. One such control is disclosed in US 2020 / 0132882. However, the document is silent regarding how this can be linked to the energy supply system or the use of renewable energy sources.

[0008] There is thus a need for an improvement in the field.

[0009] SUMMARY OF THE INVENTION

[0010] The invention therefore aims at providing an improvement in the operation of a mining system with energy from an energy supply system. This object is according to a first aspect achieved through a method of influencing a mining process performed at a production site by a mining system using energy generated by an energy supply system, the method being performed by a material and energy analysing arrangement and comprising: obtaining measurements of the mining process; applying, in a soft sensor, available data of at least a part of the mining process in at least one dynamic mining model, the available data comprising the measurements; determining, in the soft sensor, at least one key performance indicator based on the application of the available data in the least one mining model together with dynamic properties of the mining process, the at least one key performance indicator comprising a sustainability key performance indicator; applying the at least one key performance indicator in a control problem linked to the production of the mining process and energy available from the energy supply system; optimizing the control problem using production goals and energy supply limitations as constraints; and influencing the mining process based on the optimized control problem.

[0011] This object is according to a second aspect achieved by a material and energy analysing arrangement for influencing a mining process performed at a production site by a mining system using energy generated by an energy supply system, the material and energy analysing arrangement comprising one or more processors with computer program code causing the one or more processors to: obtain measurements of the mining process; apply, in a soft sensor, available data of at least a part of the mining process in at least one dynamic mining model, the available data comprising the measurements; determine, in the soft sensor, at least one key performance indicator based on the application of the available data in the at least one mining model together with dynamic properties of the mining process, the at least one key performance indicator comprising a sustainability key performance indicator; apply the at least one key performance indicator in a control problem linked to the production of the mining process and energy available from the energy supply system; optimize the control problem using production goals and energy supply limitations as constraints; and

[0012] - influence the mining process based on the optimized control problem.

[0013] The object is according to a third aspect achieved by a computer program for influencing a mining process performed at a production site by a mining system using energy generated by an energy supply system, the computer program comprising computer program code causing, when being loaded into one or more processors of a material and energy analysing arrangement, the one or more processors to: obtain measurements of the mining process; apply, in a soft sensor, available data of at least a part of the mining process in at least one dynamic mining model, the available data comprising the measurements; determine, in the soft sensor, at least one key performance indicator based on the application of the available data in the at least one mining model together with dynamic properties of the mining process, the at least one key performance indicator comprising a sustainability key performance indicator; apply the at least one key performance indicator in a control problem linked to the production of the mining process and energy available from the energy supply system; optimize the control problem using production goals and energy supply limitations as constraints; and influence the mining process based on the optimized control problem. The object is according to a fourth aspect achieved by a computer program product for influencing a mining process performed at a production site by a mining system using energy generated by an energy supply system, the computer program comprising a computer-readable storage medium comprising the computer program code of the third aspect.

[0014] The object is according to a fifth aspect achieved by an operations system comprising a mining system operating a mining process at a production site, an energy supply system generating energy for the mining system and a material and energy analysing arrangement, the material and energy analysing arrangement being operative to: obtain measurements of the mining process; apply, in a soft sensor, available data of at least a part of the mining process in at least one dynamic mining model, the available data comprising the measurements; determine, in the soft sensor, at least one key performance indicator based on the application of the available data in the at least one mining model together with dynamic properties of the mining process, the at least one key performance indicator comprising a sustainability key performance indicator; apply the at least one key performance indicator in a control problem linked to the production of the mining process and energy available from the energy supply system; optimize the control problem using production goals and energy supply limitations as constraints; and influence the mining process based on the optimized control problem.

[0015] The at least one or more processors of the material and energy analysing arrangement may implement a soft sensor module, a mining model module and a control problem optimizing module, where the soft sensor module is operative to obtain the measurement of the mining process, apply them and determine the at least one key performance indicator using at least one mining model of the mining model module and the control problem optimizing module is operatable to apply the key performance indicator in the control problem and to optimize the control problem.

[0016] The control problem optimizing module may additionally be operative to use at least one mining model of the mining model module in the control problem. Furthermore, the control problem optimizing module may also use at least one energy supply system model of the mining model module in the control problem.

[0017] The optimizing of the control problem may involve optimizing one or more of the key performance indicators.

[0018] According to one variation of the first aspect, the obtaining of sensor measurements, application of available data comprising them in at least one mining model, determining at least one key performance indicator, applying the key performance indicator on a control problem and optimizing the control problem are performed in a present time interval and the influencing of the mining operation comprises scheduling or providing a strategy for the production of the mine in a future time interval.

[0019] According to a corresponding variation of the second aspect, the material and energy requirements analysing arrangement is operative to obtain sensor measurements, apply available data comprising them in at least one mining model, determine at least one key performance indicator, apply the key performance indicator on a control problem and optimize the control problem in a present time interval and the influencing of the mining operation comprises scheduling or providing a strategy for the production of the mine in a future time interval. The mining process may comprise a number of operations and the influencing of the mining operation may comprise providing a strategy for the production at the mining site in respect of the entire mining process including all operations, i.e. on a plant level.

[0020] The influencing of the mining process may comprise monitoring the mining process.

[0021] The influencing of the mining process may comprise controlling the entire mining process, i.e. performing control on a plant level. This control may for instance be feedforward control. Alternatively, the influencing may comprise controlling at least one operation of the mining process.

[0022] Furthermore, the influencing of the mining process may comprise determining operational commands for at least one piece of mining equipment and providing the operational commands to the mining process for controlling the piece of mining equipment.

[0023] The piece of mining equipment maybe employed in one of the operations of the mining process.

[0024] It is additionally possible that the at least one piece of equipment is controlled based on a process model of the operation in which the equipment is used, which process model is built from process data of the operation and / or a soft sensor in which the process data is applied.

[0025] One mining operation may be a transporting operation in which the one piece of mining equipment comprises an electrical load haul and dump vehicle and the operational command comprises a change of the vehicle speed, which change has been determined based on a prediction of the availability of renewable energy in the energy supply system. The electrical load haul and dump vehicle may comprise a battery, the charging of which is scheduled. In this case one soft sensor may estimate the charging status of the battery based on measurements of the electrical load haul and dump vehicle, such as speed, load and driving time measurements, and the vehicle speed maybe increased or decreased based on an understanding that the availability of renewable energy will increase or decrease.

[0026] The at least one key performance indicator may be determined with the sensor measurements as inputs to simulations in one or more models of one or more operations in the mine.

[0027] The sustainability key performance indicator may be based on emissions, such as based on an emissions rate. The emissions may be carbon oxide emission or NOx emissions.

[0028] The energy constraints may comprise renewable energy usage ratio, availability of conventional non-renewable energy sources and / or energy supply system stability.

[0029] The control problem may additionally consider at least one mining model of the mining operations. Furthermore, the control problem may be a constrained mixed discrete-continuous stochastic optimization problem.

[0030] The energy supply system may comprise renewable energy sources, such as wind and / or solar energy sources like wind and / or photovoltaic generators. In this case the energy generated by the local energy supply system may comprise renewable energy. Also geothermal energy sources are possible.

[0031] BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention will in the following be described with reference being made to the accompanying drawings, where Fig. 1 schematically shows a mining system performing a mining process, Fig. 2 schematically shows an energy supply system for powering the mining system,

[0033] Fig. 3 schematically shows a first realization of a a material and energy analysing arrangement,

[0034] Fig. 4 schematically shows a second realization of the material and energy analysing arrangement,

[0035] Fig. 5 schematically shows a computer-readable storage medium with computer program code, in the form of a CD-ROM disc, for implementing the material and energy analysing arrangement, and

[0036] Fig. 6 shows a flow chart of number of method steps performed by the material and energy analysing arrangement in a method of influencing the mining process.

[0037] DETAILED DESCRIPTION OF THE INVENTION

[0038] In the following there will be given a detailed description of embodiments of a method, material and energy analysing arrangement, computer program and computer program product for influencing a mining process performed at a production site by a mining system using energy generated by an energy supply system as well as to an operations system comprising a mining system, energy supply system and material and energy analysing arrangement.

[0039] Fig. 1 schematically shows a mining system MS io. The mining system io is a local mining system provided at a production site, which site may thus be a mining site.

[0040] The mining system io comprises a number of operations. As an example, there is a first ore handling operation OHO1, which may be a first ore processing operation that as an example is a blasting operation. This is followed by a second ore handling operation 0H02, which may be a first ore transporting operation involving the transporting of ore using equipment such as Load Haul and Dump (LHD) vehicles 16. The second ore handling operation OHO2 is followed by a first intermediate ore storing operation IOSO1, which first intermediate storing operation IOSO1 may involve storing of ore in one or more ore passes of a first intermediate ore storage system 18. After the first intermediate ore storing operation IOSO1 follows a third ore handling operation OHO3, which may be a second ore transporting operation involving a transporting by ore transporting equipment 20 such as trucks, trains, wagons or conveyor belts. Thereafter follows a second intermediate ore storing operation IOSO2, which may likewise be carried out using ore passes in a second intermediate ore storage system 22. After the second intermediate ore storing operation IOSO2 follows a fourth ore handling operation OHO4, which maybe a second ore processing operation such as an ore crushing operation using equipment such as one or more ore crushers 24. Thereafter may follow a third intermediate ore storing operation IOSO3, which maybe carried out using ore passes in a third intermediate ore storage system 26. The third intermediate ore storing operation IOSO3 is finally followed by a fifth ore handling operation OHO5, which maybe a third ore transporting operation that as an example is a hoisting operation using hoisting equipment 28. A hoisting operation may also be considered to be an ore handling operation. In the mining system there may also be sensors, such as cameras and near infrared (NIR) sensors. One such sensor Si 14 is shown at the first ore handling operation OHO1. Another sensor may be provided on an LDH truck 16.

[0041] It should here be realized that the system in fig. 1 is a mere example and that further or fewer operations may be included. It is also possible with additional types of operations, such as charging, drilling and comminution.

[0042] Furthermore, there may additionally exist one or more auxiliary parts involved in auxiliary operations associated with the production. In the figure one such auxiliary part in the form of a dewatering system DW 30 is shown at the third ore handling operation OHO3. The dewatering system 30 provides a dewatering operation at one or more of the shown operations. Another possible auxiliary operation is ventilation.

[0043] In order to control and / or plan at least some of the operations there is a process system controller PSC 32 controlling or providing planning and operation instructions to equipment used in the first ore handling operation OHO1, the second ore handling operation OHO2, the third ore handling operation OHO3, the fourth ore handling operation OHO4, the fifth ore handling operation OHO5, and the dewatering system 30. Also equipment used in the first, second and third intermediate or storing operations IOSO1, IOSO2, IOSO3 maybe controlled or provided with operating instructions.

[0044] The mining system 10 needs energy in order to operate properly. For this reason there may be provided an energy supply system, which is a local energy supply system at the production site.

[0045] Fig. 2 shows one example of the local energy supply system ESS 34 for the mining system at the production site.

[0046] The energy supply system 34 comprises a bus 52 and a number of subsystems connected to the bus 52. There is first subsystem 36 connected to the bus 52 via first converter 38, which first subsystem 36 comprises fuel-operated generators FOG, such as diesel or gas generators. These generators maybe based on diesel engines and / or gas turbines. There is also a second subsystem 40 connected to the bus 52 via a second converter 42, which second subsystem is a photovoltaic or solar energy SE subsystem comprising photovoltaic generators. There is a third subsystem 44 connected to the bus 52 via a third converter 46, which third subsystem 44 is a wind energy WE subsystem comprising wind turbines. Finally, there is a fourth subsystem 48 connected to the bus 52 via a fourth converter 50, which fourth subsystem 48 is an energy storage ES subsystem, comprising energy storage elements such as batteries and / or flywheels. It can thereby be seen that the energy supply system 34 comprises renewable energy sources, such as wind and / or solar energy sources like wind and / or photovoltaic generators. Further types of renewable energy sources may exist, like geothermal energy sources (not shown). There are also nonrenewable or conventional energy sources such as fuel operated generators based on diesel generators and gas turbines and the operation of these may cause various types of emissions, such as carbon oxide emissions.

[0047] The bus 52 is in the example of fig. 1 an Alternating Current (AC) bus. The first and third subsystems 36, 44 typically operate with AC and for this reason the first and third converters 38, 46 are AC / AC converters. On the other hand, the second and fourth subsystems 40, 48 typically operate with direct current (DC) and for this reason the second and fourth converters 42, 50 are AC / DC converters. However, it is possible that the bus is a Direct Current (DC) bus, in which case the first and third converters 38, 46 may instead be AC / DC converters, while the second and fourth converters 42, 50 may instead be DC / DC converters. The bus 52 may also be connected to a grid 56, such as a utility grid. This is indicated in fig. 1 through the bus 36 being connected to the grid 56 via a further converter 54, which in this case is also an AC / AC converter.

[0048] An energy supply system controller ESSC 58 is also provided for controlling or planning the operation of the energy supply system 34, which maybe done through controlling the subsystems 36, 40, 44, 48 and converters 38, 42, 46, 50.

[0049] Aspects of the present disclosure are concerned with analysing production and energy requirements of a mining process operated at the production site by the mining system using energy generated by the energy supply system. For this reason there is provided a material and energy analysing arrangement.

[0050] Fig. 3 shows a first realization of the material and energy analysing arrangement MEAA 60. The material and energy analysing arrangement 60 comprises one or more processors with computer program code causing the one or more processors to implement a material and energy analysing function. In the present example, the material and energy analysing arrangement 60 comprises one processor PR 62 and one memory M 64 or data storage with computer program instructions CI 66 that, when executed by the processor 62, implements the material and energy analysing function.

[0051] The material and energy analysing arrangement 60 may thus comprise a processor 62 with associated program memory 64 including computer program code or computer program instructions 66 for implementing the material and energy analysing function.

[0052] Fig. 4 shows a second realization of the material and energy analysing arrangement MEAA 60. It maybe realized as a soft sensor module SSM 68, a mining model module MMM 70 and a control problem optimizing module CPOM 72, which modules may in a similar way be realized using one or more processors and one or more memories with computer program code. The material and energy analysing arrangement 60 may thus be provided through one or more processors implementing the soft sensor module 68, the mining model module 70 and the control problem optimizing module 72.

[0053] The computer program code used to implement the material and energy analysing arrangement may also be provided via a computer program product, for instance in the form of a computer readable storage medium or data carrier, like a CD ROM or a memory stick, carrying a computer program with the computer program code, which will implement the material and energy analysing function when being loaded into one or more processor of the material and energy analysing arrangement. One such computer program product in the form of a CD ROM 74 with the above-mentioned computer program code 66 is schematically shown in fig.

[0054] 5-

[0055] The center of a mine is to process materials, e.g., extract rocks from a borrow and grinding them into smaller pieces, in the end separating target materials and waste materials, all process equipment are served this purpose. Hence, if the efficiency of a mine is discussed, it can be considered as exploring an amount of target material produced per an amount of consumed energy.

[0056] Sustainability in mining attracts much attention because the mining industry accounts for 6.2% of total global energy consumption. Therefore, more and more green energy sources are integrated into the power generation of mines to reduce greenhouse gas emissions.

[0057] The currently used process control in mine has a strong focus on equipment level, because often large number of independent pieces of equipment exist in a mine. The operators often need to examine multiple disconnected screens to coordinate each part of the mining process. As a matter of principle, those “isolated” units are connected by the material flow within a mine. In order to better employ renewable energy, one needs to provide process control that can dynamically adjust the mine production for maximizing green energy usage rate. Process control should provide optimal scheduling and production strategies with respect to certain constraints, for instance, maintaining minimum production throughput or minimum percentage of renewable energy usage.

[0058] On the other hand, building such a process control requires firstly a dynamic view of considered mining process, for example, during a time interval how much material is processed by crusher, or at a timestamp how much material is held by each processing unit. Second, it may be necessary with access to the energy consumption at each mining process unit and be able to predict the future energy consumption usage by taking into account the material type, production throughputs or other conditions. Third, specific key performance indicators (KPIs) maybe needed focusing on sustainability, quality, operation etc., which can be used for constructing objective functions in optimization problems. In the end, specific and efficient optimization algorithms may also be needed for obtaining the optimal production scheduling and process controls.

[0059] Aspects of the present disclosure are directed towards addressing one or more of the above-mentioned issues.

[0060] In order to reach these requirements, there should exist some awareness with respect to material and energy, such that appropriate and optimized planning and / or control can be provided.

[0061] Aspects of the present disclosure provides an energy and material aware control strategy. This strategy has two aspects: 1) material and energy awareness which is achieved by using advanced-sensors (e.g., NIR, Hyper spectral imaging, camera, etc.) and soft-sensors (e.g., physical model based as well as data-driven) that measure key performance indicators (KPIs) regarding productivity, quality, sustainability, etc. These can be used for process monitoring and input to advance control 2) use these measured KPIs to do Feedforward control. This can be done in two levels: 1) plantwide control level where overall mine is considered 2) equipment / process level where a single equipment or process is considered. In this way, it is possible to cover both the entire mine and individual pieces of equipment at the same time.

[0062] How this may be done, will now be described with reference also being made to fig. 6, which shows a flow chart of number of method steps performed by the material and energy analysing arrangement in a method of influencing the mining process performed by the mining system 10 using energy generated by the energy supply system 34.

[0063] The mining model module 70, which may be a digital twin of the mining system 10 performing the mining process, may include one or more models of the mining process, where the models may include models of one or more of the mining operations and perhaps of one or more of the auxiliary systems. Models may in the same way be used for the different operations, such as the blasting OHO1, crushing OHO4 and various transporting operations OHO2, OHO3, OHO5. The models of each ore handling operation various may model the throughput or production of material and the energy required for the throughput or production of material.

[0064] There may also be one or more models of the local energy supply system, which defines the number and operation of energy generation sources, renewable and fuel operated, as well as the number and operation of energy storage elements. This model may also take account of the emissions from the operation of the fuel operated generators.

[0065] The models maybe simulation or time-series data models. Simulation models can be built by Simulation Experts in specialized simulation tools. These models can for example be exported using FMUs (Functional Mockup Units) or as C functions. Time series data models can be extracted by process engineers from reference plants and stored as csv files or in a time series database.

[0066] A digital twin is a real-time virtual representation of a real-world physical system or process (a physical twin) that serves as the indistinguishable digital counterpart of it for practical purposes, such as system simulation, integration, testing, monitoring, and maintenance. Like the real mining process, a Digital Twin may provide a meta-model process description of the process. The meta- model process description can be provided using AutomationML, Asset Administration Shell, or OPC UA models.

[0067] The mining models may be defined utilizing process data from the mining processes and physical and dynamical properties of the related operations.

[0068] The soft sensor module 68 implements one or more soft sensors, which sensors determine one or more KPIs based on measurements made in the mining process, where the KPIs include at least one sustainability KPI and perhaps one or more other types of KPIs such as productivity and quality. The sustainability KPI may be related to or based on emissions such as carbon oxide emissions. It may also be expressed as an emission rate. Other types of emissions than carbon oxide are feasible such as NOx emissions. A production KPI may be related to the amount of material being throughput by an operation or the mining process while a quality KPI may be linked to the quality of the produced material. The sustainability KPI may be based on emissions, such as based on an emissions rate.

[0069] The soft sensors may be based on one or more of the mining models. A soft sensor may additionally employ sensor fusion techniques. The soft sensors are sensor modules based on the dynamical model of the mine or the available process data, as well as employment of sensor fusion techniques, whenever needed, to extract the required mine (process equipment) information, to derive the most beneficial compound sensory information whenever needed.

[0070] A KPI may be determined based on the input of the sensor measurement into one or more mining models and determine an amount of produced material based on the application of the sensor in the mining model. A sustainability KPI maybe determined based on the energy required for producing the above-mentioned amount of material. It may more particularly be based on the amount of non-renewable energy sources used for providing the required material, the operation of these non-renewable energy sources and the emissions such operation causes. For instance, the non-renewable energy generators may have a rotational movement. In this case emissions may be linked to the speed of this movement.

[0071] It is also possible to receive energy from a utility grid. There may also be an emission value associated with the energy of the grid depending on the type of energy sources that the grid uses.

[0072] The operation in the method may start through the soft sensor module 68 obtaining measurements of the mining process, Sioo, which as an example may include sensor measurements from the first sensor 14.

[0073] The soft sensor module 68 then applies, in a soft sensor, available data of at least a part of the mining process in at least one dynamic mining model, the available data comprising the measurements, S110, determines, in the soft sensor, at least one KPI based on the application of the available data comprising the obtained measurements in the at least one mining model together with dynamic properties of the mining process, S120, where the KPIs comprise a sustainability KPI as well as possibly production and quality KPIs. One or more of the KPIs may additionally be compatible with ISO 50001. The soft sensor module 68 may also determine at least one KPI based on at least one mining model of the mining model module 70 and / or at least one model of the energy supply system 34

[0074] As a soft sensor may use simulations in a mining model, at least one KPI may be determined with the sensor measurements as inputs to simulations in one or more models of one or more operations in the mine and / or simulations in one or more models of energy supply system to the operations of the mine.

[0075] Thus, a soft sensor may provide a relationship between the sensor measurement and a corresponding KPI, which relationship may employ one or more of the mining models and / or one or more of the energy supply system models.

[0076] The KPIs maybe computed based on the soft sensor outputs as well as the outputs of the advanced built-in sensors, such the first sensor 14, i.e. information needed for KPIs can be obtained either from recorded process data or a soft sensor built upon mine information, or even both.

[0077] The KPIs are then applied, by the control problem optimizing module 72, in a control problem linked to the production of the mining process and energy available from the energy supply system, S130. The control problem optimizing module may additionally use at least one mining model and possibly also at least one energy supply system model of the mining model module 70 in the control problem. The control problem may thus consider at least one mining model of the mining operations.

[0078] The control problem may be defined as a cost function, which cost function may be based on the models of the different mine operations, auxiliary systems and energy supply system. The cost function may additionally consider the dynamic of the mine, namely the energy consumption of each mine operation and auxiliary process, e.g., hoisting, pumping, dewatering, etc., on a specific time range, as well as a corresponding energy sources composition of the energy supply system, i.e. the composition of the energy sources being used to supply the energy for the mining process, such as the mixture of fuel operated or conventional generators and renewable generators being used in the operation. The KPIs and the imposed requirements from the energy generation side and the optimization requirement are thus used to formulate the control problem.

[0079] The control problem optimizing module 72 then optimizes the control problem using production goals and energy supply limitations as constraints, S140. The production goals maybe defined by the mine managers and can be influenced by the market or other factors. The energy constraints may be set with the renewable energy usage ratio, the amount of conventional energy sources, as well as the stability criteria of the energy supply system. The energy constraints may thus comprise renewable energy usage ratio, availability of conventional energy sources and / or energy supply system stability.

[0080] Within the control function optimization module 72, the optimization algorithms provide control strategies by optimizing the control function over different variable factors of production under combined constraints. The control problem may be a constrained mixed discrete-continuous stochastic optimization problem, where process constraints (energy demand side) and grid constraints (energy generation side) are identified and taken into account. The optimizing of the control problem may involve optimizing one or more of the key performance indicators.

[0081] Thereafter the control function optimization module 72 influences the mining process based on the optimized control problem, S150. The optimized control problem may be used to obtain a control strategy, which may be used to influence the mining process.

[0082] Thereby the control problem may be an optimal control strategy problem, which is formulated by considering the mining model, optimization constraints, and defined KPIs, where the constraints comprise production as well as energy generation constraints, such as maintaining a minimum production throughput and a minimum percentage of renewable energy usage.

[0083] There are a number of ways in which the mining process may be influenced. The influencing of the mining operation may comprise providing a strategy for the production at the mining site in respect of the mining process including all operations, i.e. a strategy for the plant level. Additionally or instead, the influencing of the mining process may comprise monitoring the mining process. The influencing of the mining process may comprise controlling the entire mining process, i.e. on the plant level, for instance in feedforward control. Alternatively, the influencing may comprise controlling at least one operation of the mining process. This may also be done using feed-forward control.

[0084] Thereby, the mining process may be influenced on a plant-wide control level where the overall mine is considered as well as on equipment / process level where a single piece of equipment or a single operation is considered. The piece of mining equipment may thus be employed in one of the operations of the mining process. The influencing may have two objectives 1) provide optimal scheduling or production strategies of the mine for a next time interval with a special consideration on sustainability, operation, quality, etc, and 2) based on derived key performance indicators (KPIs) to control different process units on equipment level. A control strategy may determine an operation scheduling which may be forwarded from the control problem optimising module 72 to the process system controller 32 for controlling equipment. It is additionally possible that feedforward control is employed on a plant level where the overall mine is considered as well as on an equipment / process level where a single equipment or process is considered.

[0085] By solving the control problem, an updated mine process scheduling and / or a control action is provided which will be forwarded as an input to the mine process at the overall mine control level as well as the equipment / process level, where some specific process is considered.

[0086] Thus when providing a scheduling, the obtaining of sensor measurements, determining at least one key performance indicator, applying the key performance indicator on a control problem and optimizing the control problem are performed in a present time interval and the influencing of the mining operation comprises scheduling or providing a strategy for the production of the mine in a future or next time interval.

[0087] On the other hand, when controlling different process units on the equipment level, operational commands for at least one piece of mining equipment may be determined and provided to the mining process for controlling the piece of mining equipment.

[0088] Moreover, the control of the at least one piece of equipment being performed by the process system controller 32 may in this case be based on a process model of a mining operation in which the equipment is used, which process model may have been built from process data of the operation and / or a soft sensor in which the process data is applied.

[0089] For instance, if LHD vehicles 16 are E-trucks, i.e. trucks with batteries and electric motors, for transporting raw materials in a transporting operation such as the second ore handling operation OHO2, it may be necessary to schedule the battery charging. It is possible to build a soft sensor to estimate the battery or charging status of each E-truck based on information or measurements collected from sensors, e.g., speed, driving time, load, etc. If it is known that more renewable energy is coming into the energy supply system, i.e. that the availability of renewable energy will increase, it is possible to accelerate E-trucks in order to maximize production speed. It is thus possible to make a change of the vehicle speed, which change has been determined based on knowledge of the availability of renewable energy in the energy supply system.

[0090] Moreover, the process system controller may be set to monitor all process units within the mine and control the equipment based on process models, which are built directly from process data or soft sensor, or even both.

[0091] Above the material and energy analysing, arrangement was described as being separated from the process system controller. However, it should be realized that it may in fact be a part of the process system controller. It is additionally possible that the energy supply system controller is a part of the process system controller.

Claims

CLAIMS1. A method of influencing a mining process performed at a production site by a mining system (10) using energy generated by an energy supply system (34), the method being performed by a material and energy analysing arrangement (60) and comprising:- obtaining (S100) measurements of the mining process;- applying (S110), in a soft sensor, available data of at least a part of the mining process in at least one dynamic mining model, the available data comprising the measurements;- determining (S120), in the soft sensor (68), at least one key performance indicator based on the application of the available data in the at least one mining model together with dynamic properties of the mining process, said at least one key performance indicator comprising a sustainability key performance indicator;- applying (S130) the at least one key performance indicator in a control problem linked to the production of the mining process and energy available from the energy supply system (34);- optimizing (S140) the control problem using production goals and energy supply limitations as constraints; and- influencing (S150) the mining process based on the optimized control problem.

2. The method according to claim 1, wherein the obtaining of measurements, application of the available data in at least one mining model, determining at least one key performance indicator, applying the key performance indicator on a control problem and optimizing the control problem are performed in a present time interval and the influencing of the mining operation comprises scheduling or providing a strategy for the production of the mine in a future time interval.

3. The method according to claim 1 or 2, wherein the influencing of the mining process comprises determining operational commands for atleast one piece of mining equipment and providing the operational commands to the mining process for controlling the piece of mining equipment.

4. The method according to claim 3, wherein the at least one piece of equipment is controlled based on a process model of an operation in which the equipment is used, which process model is built from process data of the operation and / or a soft sensor in which the process data is applied.

5. The method according to claim 3 or 4, wherein one mining operation is a transporting operation (OHO2), the one piece of mining equipment comprises an electrical load haul and dump vehicle (16) and the operational command comprises a change of the vehicle speed, which change has been determined based on a prediction of the availability of renewable energy in the energy supply system (34).

6. The method according to any previous claim, wherein the at least one key performance indicator is determined with the sensor measurements as inputs to simulations in one or more models of one or more operations in the mine.

7. The method according to any previous claim, wherein the sustainability key performance indicator is based on emissions, such as based on an emissions rate.

8. The method according to any previous claim, wherein the energy constraints comprise renewable energy usage ratio, availability of conventional energy sources and / or energy supply system stability.

9. The method according to any previous claim, wherein the control problem considers at least one mining model of the mining operations.io. The method according to any previous, wherein the control problem is a constrained mixed discrete-continuous stochastic optimization problem. n. A material and energy analysing arrangement (6o) for influencing a mining process performed at a production site by a mining system (io) using energy generated by an energy supply system (34), the material and energy analysing arrangement (60) comprising one or more processors (62) with computer program code causing said one or more processors to:- obtain measurements of the mining process;- apply, in a soft sensor, available data of at least a part of the mining process in at least one dynamic mining model, the available data comprising the measurements;- determine, in the soft sensor, at least one key performance indicator based on the application of the available data in the at least one mining model together with dynamic properties of the mining process, said at least one key performance indicator comprising a sustainability key performance indicator;- apply the at least one key performance indicator in a control problem linked to the production of the mining process and energy available from the energy supply system (34);- optimize the control problem using production goals and energy supply limitations as constraints; and- influence the mining process based on the optimized control problem.

12. The material and energy analysing arrangement according to claim 11, said at least one or more processors (62) implementing a soft sensor module (68), a mining model module (70) and an control problem optimizing module(72), wherein the soft sensor module (68) is operative to obtain said measurement of the mining process, apply said available data and determine said at least one key performance indicator using atleast one mine model of the mine model module (70) and the control problem optimizing module (72) is operatable to apply the at least one key performance indicator in the control problem and to optimize the control problem.

13. A computer program for influencing a mining process performed at a production site by a mining system (10) using energy generated by an energy supply system (34), the computer program comprising computer program code (66) causing, when being loaded into one or more processors (62) of a material and energy analysing arrangement (60), said one or more processors (62) to:- obtain measurements of the mining process;- apply, in a soft sensor, available data of at least a part of the mining process in at least one dynamic mining model, the available data comprising the measurements;- determine, in the soft sensor, at least one key performance indicator based on the application of the available data in the at least one mining model together with dynamic properties of the mining process, said at least one key performance indicator comprising a sustainability key performance indicator;- apply the at least one key performance indicator in a control problem linked to the production of the mining process and energy available from the energy supply system (34);- optimize the control problem using production goals and energy supply limitations as constraints; and- influence the mining process based on the optimized control problem.

14. A computer program product for influencing a mining process performed at a production site by a mining system (10) using energy generated by an energy supply system (34), the computer program comprising a computer-readable storage medium (74) comprising the computer program code (66) of claim 13.15- An operations system comprising a mining system (io) operating a mining process at a production site, an energy supply system (34) generating energy for the mining system and a material and energy analysing arrangement (60), said material and energy analysing arrangement (60) being operative to:- obtain measurements of the mining process;- apply, in a soft sensor, available data of at least a part of the mining process in at least one dynamic mining model, the available data comprising the measurements;- determine, in the soft sensor, at least one key performance indicator based on the application of the available data in the at least one mining model together with dynamic properties of the mining process, said at least one key performance indicator comprising a sustainability key performance indicator;- apply the at least one key performance indicator in a control problem linked to the production of the mining process and energy available from the energy supply system (34);- optimize the control problem using production goals and energy supply limitations as constraints; and- influence the mining process based on the optimized control problem.

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