WO2022194358A1 - Procédé d'entraînement d'un modèle de prédiction de qualité pour un dispositif de traitement d'un processus industriel continu, procédé de commande d'un processus industriel continu comprenant un dispositif de traitement, et dispositif de traitement - Google Patents

Procédé d'entraînement d'un modèle de prédiction de qualité pour un dispositif de traitement d'un processus industriel continu, procédé de commande d'un processus industriel continu comprenant un dispositif de traitement, et dispositif de traitement Download PDF

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WO2022194358A1
WO2022194358A1 PCT/EP2021/056706 EP2021056706W WO2022194358A1 WO 2022194358 A1 WO2022194358 A1 WO 2022194358A1 EP 2021056706 W EP2021056706 W EP 2021056706W WO 2022194358 A1 WO2022194358 A1 WO 2022194358A1
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processing
data
processing device
model
quality
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PCT/EP2021/056706
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English (en)
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Arzam Kotriwala
Nuo LI
Jan-Christoph SCHLAKE
Prerna JUHLIN
Felix Lenders
Matthias Biskoping
Benjamin KLOEPPER
Kalpesh BHALODI
Andreas POTSCHKA
Dennis Janka
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Abb Schweiz Ag
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Priority to PCT/EP2021/056706 priority Critical patent/WO2022194358A1/fr
Publication of WO2022194358A1 publication Critical patent/WO2022194358A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining

Definitions

  • Method for training a quality prediction model for a processing device of a continuous industrial process comprising a processing device, and a processing device
  • aspects of the invention relate to a method for training a quality prediction model for a processing device of a continuous industrial process, in particular a mining process, a method for controlling of a continuous industrial process comprising a processing device, and a processing device.
  • an operator or controller of the industrial process may adapt subsequent process steps such as blending to ensure a defined quality of the final product.
  • additional sensors may be used at the processing stations to determine quality parameters such as ore content, grindability and hardness.
  • document WO 2019053261 A 1 describes a method for operating an ore comminution circuit comprising at least one comminution device. At least one sensor signal related to an ore feed to the comminution circuit is obtained. The at least one sensor signal typically results from at least one of the following methods: ore tracking, stockpile management, a particle size measurement, an optical analysis and/or refiectometry in the visible range, optical analysis and/or reflectometry in the UV, optical analysis and/or reflectometry in the NIR and/or MIR, acoustical method, machine vision, imaging, hyperspectral imaging, multispectral imaging, LIBS, PGNA, XRF, XRL, LIF, a color measurement, a photothermal measurement, visible/UV/NIR/MIR spectroscopy, THz spectroscopy, and electromagnetic spectroscopy in at least one frequency range from 1 kHz to 10 GHz.
  • ore tracking stockpile management
  • a particle size measurement an optical analysis and/or re
  • a first ore grindability parameter of the ore feed is determined from the at least one sensor signal.
  • a second ore grindability parameter is determined using at least one parameter of the comminution circuit and/or of the at least one comminution device in the comminution circuit.
  • the model is updated with the second ore grindability parameter and the at least one sensor signal.
  • the first ore grindability parameter may be employed by a control unit for controlling the comminution circuit.
  • document KR 20160155793 A describes a quality prediction apparatus including a support unit supporting a material to be processed, a measurement unit for obtaining magnetic force information from the material to be processed, and a calculation unit for predicting the quality of the material to be processed by using the magnetic force information.
  • sensor-based ore sorting machines may be used to early eliminate low quality ore from the material flow.
  • a combination of a conveyor belt and some purpose-depending measurement device is used.
  • document CN 107321646 A describes an atomic ore separation system using a nuclear radiation detector.
  • a mechanical device containing a grid to filter low-sized raw material is used in mining industries.
  • the quality information is at most only available at a specific point in the material flow of the industrial process, an additional mechanical device is to be integrated into the material flow, and/or a special sensor is to be selected and provided to identify specific quality properties.
  • an additional mechanical device is to be integrated into the material flow, and/or a special sensor is to be selected and provided to identify specific quality properties.
  • both development and integration of the respective sensor and/or device is usually time consuming and/or expensive. Even further, lab measurements are typically needed for sensor validation. This may also be time consuming and/or expensive.
  • the method includes receiving processing data generated and/or used by the processing device during processing a material, determining, for the processing data, quality data of the processed material, wherein determining the quality data comprises modeling the processing of the material (in the processing device), and using the processing data and the quality data to train or retrain the quality prediction model to determine predicted quality data for the processing data.
  • the (corresponding) processing data and quality data may be used as primary datasets for training (or retraining) the quality prediction model to output (predicted) product quality data for an input of respective processing data.
  • datasets may include respective data for a given time.
  • the datasets may include a time sequence and/or a batch of respective data.
  • Using the trained (or retrained) quality prediction model allows for predicting quality data for current processing data even without using additional sensors.
  • the quality prediction model may be trained (or retrained) only using previously obtained (historic) processing data which may have been stored / are determinable anyways.
  • training/retraining can be done offline, e.g. even in parallel to the continuous industrial process.
  • a powerful cloud service and/or a computing center may be used for (offline) training the quality prediction model.
  • an existing controller of a processing device may be capable (power full enough) to additionally perform the quality data prediction.
  • an edge device e.g. ABB Edgenius
  • ABB Edgenius may be used to perform the calculations required for the quality prediction model of the processing device, and facilitate the data transfer to a remote computer system or cloud used for (re-) training the quality prediction model.
  • the (trained) quality prediction model is configured to determine (and output) the product quality data in real time, i.e. within less than about 1 s or even less than 0.5 s or even 0.2 s, or in near real-time, i.e. within less than about 10 s or even less than 5 s.
  • the industrial process may be adapted during operation (runtime, online) using the predicted quality data of the processing station, even before the processed material is forwarded to a subsequent processing step.
  • an operator or a supervising controller may adapt the operation(s) of a respective processing device itself, for example a comminution process, and/or operations of other processing devices of the process chain (upstream or downstream), for example a downstream blending process and/or an upstream hauling process.
  • the quality prediction model is typically based on machine learning, in particular a regression technique, more particular based on linear regression or support vector machines (SVM) and/or deep learning.
  • the quality prediction model may be based on a deep neural network such as a recurrent neural network (RNN) which are particularly suited for sequences of input signals.
  • RNN recurrent neural network
  • NN neural network
  • ANN artificial neural network
  • connectionist system including a plurality of connected units or nodes called artificial neurons.
  • the output signal of an artificial neuron is calculated by a (non-linear) activation function of the weighted sum of its inputs signal(s).
  • the connections between the artificial neurons typically have respective weights (gain factors for the transferred output signal(s)) that are adjusted during one or more learning phases.
  • Other parameters of the NN that may or may not be modified during learning may include parameters of the activation function of the artificial neurons such as a threshold.
  • the artificial neurons are organized in layers which are also called modules.
  • the most basic NN architecture which is known as a “Multi- Layer Perceptron”, is a sequence of so called fully connected layers.
  • a layer consists of multiple distinct units (neurons) each computing a linear combination of the input followed by a nonlinear activation function. Different layers (of neurons) may perform different kinds of transformations on their respective inputs.
  • Neural networks may be implemented in software, firmware, hardware, or any combination thereof.
  • a machine learning method in particular a supervised, unsupervised or semi-supervised (deep) learning method may be used.
  • a deep learning technique in particular a gradient descent technique such as backpropagation may be used for training of (feedforward) NNs having a layered architecture.
  • Modem computer hardware e.g. GPUs makes backpropagation efficient for many-layered neural networks.
  • a recurrent neural network is an ANN where connections between the neurons (nodes) form a directed graph along a temporal sequence. RNNs exhibit temporal dynamic behavior.
  • LSTM Long short-term memory
  • LSTMs include feedback connections and can, depending on implementation, process single input signals, for example numbers, vectors or arrays for a given time, but also (time) sequences of such input signals.
  • LSTMs may be used for classifying, processing and making predictions based on time series input signals.
  • the architecture of the quality prediction model for example the network structure including the arrangement of neural network layers, the sequence of information processing in an NN, the number of input neurons, the number of output neurons, the number of hidden layers etc. typically depend on the industrial process and may be tuned accordingly.
  • the architecture of the quality prediction model may be defined by so-called hyperparameters of the quality prediction model.
  • the term “parameter of the quality prediction model” as used herein intends to describe data of the quality prediction model that may be changed during training or retraining.
  • parameters of a neural network may be data representing or consisting of connection weights within fully connected layers and kernel weights within convolutional layers.
  • characterizing parameter set of the quality prediction model intends to describe a set of data fully defining a specific implementation of the quality prediction model to be operable in software and/or hardware.
  • the characterizing parameter set of the quality prediction model may include and/or consist of all data that may be changed during training or retraining and hypeiparameters of the quality prediction model.
  • Training and/or retraining may include using the processing data as input of the quality prediction model (to be trained or retrained) to determine intermediate predicted quality data, comparing the intermediate predicted quality with the quality data, and using the intermediate predicted quality data and the product quality data for changing at least one parameter of the quality prediction model.
  • the quality prediction model may be based on a physical model and/or a numerical method, in particular a finite element method.
  • the training or retraining may be performed at least once, typically iteratively.
  • the method may further include validating the trained or retrained quality prediction model.
  • second datasets not used for training may be used to provide an unbiased evaluation of the fit provided by the quality prediction model on the training dataset while tuning the model's hyperparameters (e.g. the number of hidden neurons, layers and layer widths etc. for an NN).
  • Validation datasets may in particular be used for regularization by early stopping (stopping training when the error on the validation dataset increases, as this is a sign of overfitting to the training dataset) and or choosing/selecting a final quality prediction model.
  • the method may include testing the trained or retrained quality prediction model, in particular the final quality prediction model.
  • further test datasets that have never been used before (often also referred to as holdout datasets) may be used to provide a final unbiased evaluation of the final model on the datasets.
  • Further training (retraining) processes may be performed (offline) from time to time, typically using new datasets collected in the meantime.
  • a plurality of corresponding processing data and quality data are used for training or retraining the quality prediction model.
  • the processing data typically represent and/ or include at least one parameter referring to a respective state of the processing device during operation and a material processing part of the processing device, respectively, more particular a respective state during processing the material.
  • Sensor data as described in documents CN 107321646 A, KR 20160155793 A and WO 2019053261 A1 are not required for predicting the quality data (at least not during runtime), but may be part of the processing data.
  • processing data represent and/ or include at least one parameter directly referring to the respective state of the processing device during processing the material.
  • the processing data may represent and/or include a parameter referring to a stress of the processing device and a material processing part of the processing device, respectively, during processing the material, a tension of the processing device and a material processing part of the processing device, respectively, during processing the material, a torque of the processing device and a material processing part of the processing device, respectively, during processing the material, a vibration characteristics of the processing device and a material processing part of the processing device, respectively, during: processing the material, a voltage of the processing device and a material processing part of the processing device, respectively, during processing the material, a (electric) power (consumption) of the processing device and a material processing part of the processing device, respectively, during processing the material, a (electric) current of the processing device and a material processing part of the processing device, respectively, during processing the material, a temperature of the processing device and a material processing part of the processing device, respectively, during processing the material, a flux of the processing device and a material processing part of the processing device, respectively, during processing the material,
  • the processing data may represent aed/or include a parameter referring to a stress of the material, a tension of the material, a torque of the material, a pressure exerted on the material, a vibration characteristics of the material the material, a weight of the material, an electromagnetic spectrum of the material, an acoustical spectrum of the material, a magnetic flux, a temperature of the material and/or a value derived therefrom, such as a function of one or more of these time values, and a change or a derivative of one of these values.
  • processing data may be provided by a built-in (on-board) sensor or an integrated sensor unit of the processing device.
  • the method for training a quality prediction model is also referred to as training method and computer-implemented training method, respectively.
  • the industrial process typically includes a respective material flow between the processing stations.
  • Continuous in the context of this disclosure, can be understood as a process involving a continuous flow of material within the process.
  • a processing station may include one or more processing devices, A processing station may also be provided by a processing device (or several processing devices).
  • a processing station and a processing device can receive material, process the material, and dispense the processed material.
  • a processing station/device can transfer material.
  • a processing station/deviee can store the material. Combinations of such processes in one processing station are possible.
  • Modelling the processing of the material typically includes modeling the processing device.
  • modelling the processing of the material includes modelling the (whole) continuous industrial process, e.g. modelling a mining process.
  • a model of the continuous industrial process including a model of the processing device may be used for modelling the processing ofthe material of the industrial process. Accordingly, modelling the processing of the material in the (whole) industrial process can be used as a soft sensor (replacing real sensor data as e.g. described in in documents CN 107321646 A, KR 20160155793 A and WO 2019053261 Al).
  • This may include forward and/or backward calculations/simulations of the industrial process with respect to the flow of material.
  • quality data measured for the final product may be used to determine quality data of a processing station / a processing device using backward calculations/simulations.
  • quality data measured for a raw material may be used to determine quality data of a processing station / a processing device using forward calculations/simulations with respect to the flow of material,
  • model shall describes a representation of a device or a system and a respective instance of the representation, respectively, which enables to determine an output value (or set of values) from at least one input value which at least substantially correspond to respective input/outputs of the real device or system.
  • the model is typically realized as a form of software for a computer, which can include, or be used together with, a database comprising data which is used by the software.
  • the model of the continuous industrial process may include one or more, typically all of the following data, a data model of the continuous industrial process, one or more simulation models of the continuous industrial process, one or more atribute models of the continuous industrial process and analytics for the continuous industrial process.
  • the industrial process is typically a mine process (herein also referred to as mining process).
  • mining process a mine process
  • the material flow of the process, particularly the mine process can include, in one example, the transfer, transport and processing of ore, chemicals for processing, waste material, fuel, water or further materials.
  • the industrial process, particularly a mine process can include several processing stations / devices.
  • Processing stations / devices can be physical processing stations / devices which process a material, e.g. ore processing stations such as crushers, mixers or the like. Processing stations / devices, in the sense of this disclosure, can also be virtual stations that relate to the mine process by providing information about the mine process, such as planning stations, e.g, for geological planning, logistic planning, market integration or process planning. Processing stations / devices, in the sense of this disclosure, can perform physical operations with no permanent or stationary character, such as blasting for providing raw material, or the hauling or transport of material, e.g. via processing devices such as conveyors, trains, diesel or electric tracks, or boats. Processing stations, in the sense of this disclosure, may also be stations which do not involve a chemical or mechanical processing of the material, such as warehouses or stockyards.
  • the material may be a geological material, in particular an ore
  • the quality data typically correspond to and/or include at least one parameter referring to a physical and/or chemical property of the material.
  • the quality data may refer to a hardness of the material, a composition of the material, a purity of the material, a density of the material and/ or an ore grade of the material
  • the quality data may refer to a lab analysis report, may refer to a time series, may refer to a material batch, and/or include a batch of data.
  • the processing data and quality data may be obtained from a state-machine indicating special operating conditions and/or a digital twin of the industrial process, more particular a digital twin referring to the material flow including all processing steps of the material during the industrial process.
  • digital twin as used herein shall embrace the terms “process digital twin” and “material flow digital twin”.
  • a plurality of corresponding datasets referring to the past may be obtained from the digital twin, and may be used as datasets for training, retraining, validating and/or testing the quality prediction model.
  • a digital twin of an industrial process such as mining may process and store a huge amount of data delivered by many processing stations at comparatively high sampling rate. Accordingly, simulating the processes and estimating the product quality is tedious and often at least not feasible during runtime as this may require extremely high computing capacities on site and/or reliably power full data connections to a powerful cloud service and/or a computing center.
  • Determining quality data (corresponding to processing data of a processing device) and providing datasets comprising corresponding processing data and quality data, respectively, typically one of, more typically several of, even more typically all of the steps:
  • processing station layout comprises: a representation of a physical layout of the processing station, and a representation of material flow-paths to and from the processing station, wherein the processing station layout is configured for enabling a mapping of the material flow (flow of the material) to and from the processing station;
  • the interface model comprises: a representation of data input ports and data output ports of the processing station, wherein the interface model is configured for enabling a mapping of a data flow to the data input ports and from the data output ports of the processing station;
  • the information metamodel may be based on a markup language such as XML, JSON or AAS, in particular the international standard automation markup language and/or comprises: a process layout model, the process layout model comprising the processing station layouts of the processing stations, and a process interface model, the process interface model comprising the interface models of the processing stations,
  • the quality data Prior to training or retraining, the quality data may be filtered. Accordingly, an uncertainty may be addressed.
  • so-called particle filtering may be applied, More particular, the following predictor-corrector schema may be used: a) Initiate with many particles (each representing a possible system state), b) Use the model of the continuous industrial process to predict how these particles (states) change, c) Compare the predicted particles (states) with measurements, d) Determine a probabilities that a specific particle (state) represents a real system state.
  • processing data may refer to and/or be obtained from at least one of, more typically several or even all of the following processing stations blasting, hauling, storage, and ore processing.
  • processing data may refer to and/or be obtained from planning and/ or shipping.
  • the respective processing data may include a processing point of a processing station / device, a parameter of the processing station / device, and/or a (even a complete) processing configuration of the processing station / device.
  • processing stations may include several processing devices for the material that operate in parallel and/or in a chain.
  • ore processing stations may include different processing devices referring to crushing, separating, concentrating and the like arranged in a chain.
  • the method includes processing a material with the processing device, feeding processing data generated and/or used by the processing device during the processing of the material to a trained quality prediction model to determine predicted quality data corresponding to the processing data, and using the predicted quality data for controlling the processing device and/or for controlling a further processing device of the continuous industrial process.
  • controlling method the method for controlling of the continuous industrial process is also referred to as controlling method and computer- implemented controlling method, respectively.
  • the controlling method uses an instance of a quality prediction model trained with a corresponding training method as explained herein.
  • the controlling method is typically performed in real-time or near real time.
  • the controlling method typically uses as input processing data referring to a running industrial process.
  • Each processing station/device may be configured to dynamically provide respective processing data representing a state of the processing station/device.
  • the processing data can be dynamically provided, such as when requested by an external receiver, periodically according to predefined intervals, or when certain predefined conditions apply.
  • the processing device is typically provided by a mining device such as a separator, a conveyor belt, or a comminution device, such as a mill or a crusher, in particular a gearless mill drive.
  • a mining device such as a separator, a conveyor belt, or a comminution device, such as a mill or a crusher, in particular a gearless mill drive.
  • the gearless mill drive is the part of a mill that breaks solid materials into smaller pieces.
  • Gearless mill drives have a relative high number of onboard sensor devices and less mechanical devices to fulfil the grinding purpose.
  • the already integrated sensors record for example the following measurements I stator winding temperatures per phase, ring motor heat exchanger temperatures, distance measurements of airgaps at various angles, ring motor stator and excitation currents and voltages, cooling system water temperatures, torque, speed, accelerometer measurements of vibrations, and magnetic flux.
  • the ore hardness may be determined based on measured airgap distances, and ring motor stator and excitation currents and voltages and using a model of the gearless mill drive, in particular a (complete) model of the material flow and/or the material processing of the continuous industrial process (which includes the model of the gearless mill drive).
  • a model of the material flow and/or the material processing of the continuous industrial process may be provided by a digital twin of the continuous industrial process, which may be available anyways.
  • the comminution device may be at least one of an ore mill, a semi-autogenous grinding (SAG) mill, an autogenous grinding (AG) mill, a ball mill, a rod mill, a tumbling mill, a gearless mill, a geared mill, a crusher, and high-pressure grinding rolls.
  • SAG semi-autogenous grinding
  • AG autogenous grinding
  • ball mill a ball mill
  • rod mill a rod mill
  • a tumbling mill a gearless mill
  • a geared mill a crusher
  • high-pressure grinding rolls high-pressure grinding rolls.
  • the processing device(s) may at least in part be different, and e.g. provided by a wet end, a quality control system, a press section, a dryer section or the like in a continuous industrial process for pulp and paper manufacturing.
  • the controlling method may further include using the predicted quality data as feedback for the model of the continuous industrial process, e.g. the mining process, in particular a respective digital twin.
  • the model of the continuous industrial process may be improved with respect to accuracy and/or reliability.
  • the feedback may be used for iteratively improving the information metamodel and/or the simulation accuracy based on the information metamodel.
  • the feedback can be provided in the form of a feedback loop.
  • the feedback can involve defining an element to be varied within the information metamodel, and/or a value by which the defined element is to be varied.
  • the analytics application can detect or simulate that a variation of one element of the industrial process, such as e.g. a throughput of a processing station, does not correspond to the observed variation and adapt the information metamodel to beter represent the industrial process.
  • the processing station layout typically includes a representation of a physical layout of the processing station.
  • the processing station can be a crusher, and the representation of the physical layout of the crusher can include the location of the crusher, the performance of the crusher, the connection with other components of the mine, e.g, a conveyor or a downstream machine, the power source of the crusher or such. If the processing station is a virtual or non-physical processing station, such as a planning stage or a logistic path, the representation of the physical layout of the processing station can be empty or undefined.
  • the processing station layout further includes a representation of material flow-paths to and from the processing station. Material flow-paths can, in one example, include the type of expected material, the minimum or maximum amounts of material, the expected output material flow in dependence of the respective input material flow, the required time for processing the material or such.
  • the representation of material flow-paths can be suitable for creating a material flow map of material between processing stations.
  • the processing station layout enables a mapping of the material flow to and from each processing station.
  • the mapped material flow typically does not represent the actual flow, i.e. transfer of material.
  • the mapped material flow can be a mapped material flow-path, i.e. representing potential routes for the material, particularly between processing stations.
  • the interface model typically includes a representation of data input ports and data output ports of the processing station.
  • Data input and output ports can be addresses of the processing station, particularly addresses for sending or receiving data, such as network addresses of the processing station.
  • Data input and output ports can be configured for directed communication.
  • the representation of data input and output ports can include target ports, e.g. of a further processing station, a controlling tool or an analysis tool or such.
  • the input and output ports of the processing station can be the data ports through which the processing station provides the data representing the state of the processing station, as described above.
  • the processing station is a crusher, and the data input and output ports are provided by a control module of the crusher, e.g. by a connection between the control module and a data network such as a local data network or the internet.
  • the interface model enables a mapping of a data flow to the data input ports and from the data output ports of the processing station.
  • the mapped data flow can be a list, map or model of the available data ports, i.e. addresses, of the respective processing station.
  • the mapped data flow can include information about the type, structure, source, format, expected interval, and/or underlying sensor type of data, such that from the mapped data flow, the connectivity of the processing stations becomes clear.
  • the mapped data flow can include further data for defining aspects of the mapped data flow.
  • the mapped data flow typically does not represent exchanged data, but connectivity of processing stations, particularly of processing stations with other components of the industrial process, such as further processing stations,
  • the process layout model can be a model representing the layout of the industrial process, particularly in terms of processing stations within the industrial process, particularly including the aspects of the processing station layout as previously discussed.
  • the process layout model includes the processing station layouts of the number of processing stations.
  • the process layout model can include information representing links and/or connections between processing stations, such as material flow-paths.
  • the links and/or connections can be a network of material flow-paths, and the process layout model can include a topology of the network of material flow-paths. Said links can be incorporated as elements within processing station layouts, or can be generated during the generation of the information metamodel.
  • the process interface model can be a model representing the layout of the industrial process, particularly in terms of data interfaces available within the industrial process, particularly including the aspects of the interface model of a processing station as previously discussed.
  • the process interface model includes the interface models of the number of processing stations.
  • the interface model can include information representing links and/or connections between processing stations, such as data connections, ports or addresses. Said links can be incorporated as elements within the interface model of the processing stations, or can be generated during the generation of the information metamodel.
  • Generating the information metamodel may be accomplished by utilizing exporters.
  • Exporters can be tools, such as software tools, provided between the processing station and the information metamodel.
  • Exporters can be configured for interpreting data provided by the processing station, such as by a control module of the processing station, e.g. as a log-file, as a data stream, or such, particularly via a data input or a data output port of the processing stations, such as the data input and data output ports described earlier.
  • An exporter can provide the processing station layout of the processing station.
  • An exporter can provide the interface model of the processing station.
  • the exporter can be a resource type exporter for exporting the resource type of a resource, such as a processing station.
  • the exporter can be a data model type exporter for exporting the data model type provided by a resource, such as a processing station, particularly a software or control module monitoring the processing station.
  • Type exporters such as resource type exporters or data model type exporters, can be used for building a type library of the processing stations within the industrial process.
  • the type library can be built according to AutomationML’s System Unit Class Libraries,
  • the exporter can be a process layout exporter for importing the layout of the industrial process into an instance model, the instance model comprising all instances, e.g. all processing stations, within the industrial process.
  • the process layout exporter can, together with the type library of the processing stations within the industrial process, be utilized for building an instance model of the industrial process.
  • the instance model can be included in the process layout model of the industrial process, i.e. the information metamodel.
  • the instanee model can be built according to AutomationML’s Instance Hierarchy.
  • the exporter can further be a data value address exporter for importing a data value address, such as a representation of a data input port of a processing station.
  • the data value address exporter can, in some examples, be a data value importer, providing the functionality of a data value address exporter but from the information metamodel, particularly an instance model included in the information metamodel, to the processing station.
  • the data value address exporters and importers can be used for building a process interface model, such as the process interface model described earlier, included in the instance model and the information metamodel of the process.
  • Generating the information metamodel can include generating a type library from the processing station layout and the interface model of the number of processing station, Generating the type library can be performed by utilizing an exporter, as described earlier.
  • generating the information metamodel can include generating an instance model comprising the processing station layout of at least one of the processing stations as defined by the type library. Generating the type library can be performed by utilizing an exporter, as described earlier. Furthermore, generating the information metamodel can include generating an instance model comprising an interface model of at least one of the processing stations as defined by the type library. Generating the instance model can be performed by utilizing an exporter, as described earlier.
  • generating the information metamodel can include generating a process layout model and including the process layout model into the instance model by using a process layout exporter comprising the material flow-path to and from the at least one of the processing stations included in the instance model.
  • Generating the process layout model can be performed by utilizing an exporter, as described earlier.
  • generating the information metamodei can include importing data input ports for importing data representing the state of the processing station provided by the number of processing stations into the instance model. Importing the data input ports can be performed by utilizing an exporter, as described earlier.
  • Generating the information metamodel can include enabling a data flow between interfaces of the number of processing stations by using a data value exporter/importer linked via the instance model.
  • the interfaces can be data ports of the processing stations, such as data input ports and data output ports.
  • the data flow can be enabled such that it is still available for a significant duration after the creation of the information metamodei. Enabling the data flow between interfaces of the number of processing stations can include importers or exporters or a combination thereof, as described above.
  • generating the adaptive simulation model of the industrial process typically includes importing the data representing the state of the processing station provided by the number of processing stations into the adaptive simulation model.
  • the importing of the data is performed via the information metamodei.
  • the information metamodei can function as a hub, e.g. for aggregating, routing, converting and transferring the data provided by the processing stations.
  • the informalion metamodei does not directly hold data values, i,e. the information metamodei includes the relevant process layout together with the relevant process interface model, the process interface model defining from where data can be retrieved by the adaptive simulation model.
  • the information metamodei can utilize the process layout model for providing a map of the processing stations within the process, and the physical connections between the processing stations, particularly material flow-paths.
  • the process layout model can be utilized for mapping a material flow within the industrial process, particularly all possible material flows.
  • the information metamodei can further utilize the process interface model for linking data provided by the processing stations to the process layout model.
  • the information metamodei particularly by utilizing the process interface model, can further be used for converting the data provided by the processing stations into a common and/or interchangeable format, particularly by using a number of exporters or importers as described above.
  • the information metamodei can provide the data provided by the number of processing stations to the adaptive simulation model.
  • the data provided to the adaptive simulation model via the information metamodel can be structured, linked, enhanced, formatted, marked-up or expanded such that the data provided by the number of processing stations can be evaluated, by the adaptive simulation model, in the context of the industrial process, particularly in the context of the process layout model, particularly in the context of the material flow-paths of the industrial process, and/or stored in a database that may be used for the training method as explained herein.
  • the adaptive simulation model can be adaptive, Adaptive, in the context of this disclosure, can include the property of the adaptive simulation model to respond to changes in the properties of the industrial process, such as such as changes in the data provided by the number of processing stations, which is to be expected at all times due to e.g. a change in operation parameters, as well as changes in the process layout model or the process interface model.
  • a change in the process layout model or the process interface model can e.g. include the removal of a processing station due to maintenance, and a resulting change of material flow-paths to and from said processing station.
  • the information metamodel and the adaptive simulation model can be configured for automatically re-evaluating the industrial process and generating a new information metamodel and/or a new adaptive simulation model.
  • the changes can be virtual to enable what-if analyses, e.g. introduce virtual changes, such as adding or removing virtual processing stations.
  • an analysis can involve the addition of a virtual conveyor belt to assess the possible effect of adding the conveyor belt.
  • Providing adaptivity can include a selection of an appropriate simulation model from a library of simulation models.
  • the model can be a previously generated model which best represents a current state of the industrial process.
  • the chosen model can emphasize one aspect of the process more than another process, e.g. if an emphasis is put on optimizing a set of conveyors, an adaptive simulation model might be selected from the library of simulation models which simulates the set of conveyors with a higher resolution than other models. Selecting the appropriate adaptive simulation model from the library of adaptive simulation models may be performed automatically or as a result of user input.
  • the adaptive simulation model can be initiated using status information of the number of processing stations of the industrial process provided by the information metamodel. Furthermore, the adaptive simulation model can act as a "soft-sensor" to generate missing status information not provided by the information meta model, e.g. by simulating the missing data or by replacing the missing data with expected values, standard values or the likes. Simulating the missing data can involve the creation of a soft sensor to provide soft sensor data. The soft sensor data can then again be made available via the information metamodel to other applications.
  • the output processing data can be provided to an analytics application, such as one or a combination of the analytics application described above, and analysed by the analytics application.
  • the analysed data can be utilized to provide feedback
  • the information metamodel and the adaptive simulation model can be comprised in a digital twin of the industrial process.
  • the information metamodel and the adaptive simulation model together form a digital twin of the process.
  • the digital twin can be a model of a system of components or a system of systems, such as a model of an industrial process comprising processing stations.
  • the digital twin can be used to evaluate the current condition of the industrial process, and predict future behavior, optimize operation and refine control aspects of the industrial process.
  • the digital twin can reflect the industrial process’ current configuration, age and environment and/or material flow. Data of the industrial process, such as data provided by a number of processing stations, can, via the digital twin, be directly streamed into tuning algorithms or analytics applications, such as the analytics applications described above.
  • Die interface model of each processing station can be configured for providing connectivity between the information metamodel and/or the adaptive simulation model and the processing station.
  • the interface models of two processing stations can be configured for enabling, via the information metamodel and/or the adaptive simulation model, an exchange of information between two processing stations, or a higher number of processing stations, such as all processing stations included in the adaptive simulation model.
  • the process layout model can link (a number of) processing station layouts according to the material flow-paths between the processing stations.
  • the linking of the processing stations according to the material flow-paths between the processing stations can result in a map of the network of material flow-paths.
  • the processing stations can be nodes within the network of material flow-paths.
  • the process interface model of the information metamodel can link the data representing a state of the processing station provided by a processing station of the number of processing stations to the process layout model,
  • the process interface model can be configured such that data sent from an outgoing data port can be correlated to an entity represented in the process layout model, such as a processing station.
  • the correlation can be based on information comprised within the process interface model which links the data port, from which data was sent, to the entity represented in the process layout model.
  • the adaptive simulation model may be used to identify material blobs in the material flow and to export an event log file referring to the material blobs and representing the material flow.
  • the material blobs can be virtual,
  • a material blob can be a representation of an arbitrary amount of material within an industrial process, particularly a continuous industrial process.
  • a material blob can be, in one example, an amount of material processed in a process between two arbitrary events, such as an amount of material processed between two timepoints.
  • a material blob typically corresponds with a quantity of a real material (e.g. a truck load) having the same material properties.
  • a material blob does not have to be separate from other material blobs with regards to material, process or such, i.e.
  • a virtual material blob can be virtual in that the separation of one batch of material into material blobs does not mean a corresponding separation of the actual material into distinct batches corresponding to the blobs.
  • a virtual material blob can further be a material blob that does not represent physical material, i.e. is entirely virtual, e.g. for simulating material flow, analysing the process, performing a what-if analysis or such.
  • the adaptive simulation model may provide an event log file referring to the material blobs and representing the material flow, particularly in the form of events related to material blobs.
  • the event log file can include information corresponding to events in the industrial process.
  • the event log file can be a file, a data stream, a transmission or such, i.e. the event log file does not need to be a file stored in a file system.
  • the event log file can include information from which material blobs can be identified.
  • the event log file can include information from which attributes of the material blobs can be correlated with the identified material blobs.
  • the event log file can include key parameter indicators (KPIs) related to the material blobs.
  • the KPIs can be KPIs as described above in relation to analytics applications.
  • Attributes of the material blobs can correlate to atributes of the material from which the material blobs are derived.
  • attributes of a material blob can include information such as material quality, quantity, energy spent for processing, history of the material blob or such.
  • the adaptive simulation model can provide the event log file in a unitary format, such that material blobs can be identified and tracked for different processing stations, particularly different types of processing stations, or different instances of processing stations of identical, similar or different types.
  • event logs provided by processing stations in different, e.g. process station specific file formats can be converted and provided in a unitary format by the adaptive simulation model and/or an exporter.
  • the adaptive simulation model and the information metamodel can be utilized in a manner such as one described above.
  • Identifying material blobs and exporting the event log file referring to the material blobs can include pre-processing the event log file before processing the event log file. Pre-processing the event log file can be performed by the adaptive simulation model or a separate tool. Pre- processing the event log file can exclude unnecessary data and/or reformat the data in the log file. Excluding unnecessary data can include identifying the unnecessary data, i.e. according to a predefined set of rules, such as a program. Unnecessary data can be data that does not carry any information or redundant information, such as double entries. Unnecessary data can be data for which it is known that it cannot be evaluated, understood, parsed or otherwise processed or analyzed by a downstream application, such as an analytics application described above.
  • Pre- processing the event log file can reformat the event log file such that it can be processed by a downstream application. Reformatting can include providing the event log file in a specific file format, such as in a specific markup language, such as ISON, XML, AutomationML or such, in particular a markup-language supported by a cloud provider, e.g. MS Azure. Accordingly, training the quality prediction model in a cloud is facilitated. Reformatting can include reformatting only parts of the event log file, e.g. parte of the event log file which do not correspond to a specified format. Reformatting can include providing the event log file in several different formats, such as a specific file format required for each downstream application.
  • Processing the event log file can further include determining case identifiers required by the process mining technique.
  • Case identifiers can be attributes of an event. Case identifiers can identify events and/or link events to entities referred to within the event log file, such as aspects relating to the material flow, particularly material blobs. Case identifiers can be identifiers of material blobs, such as unique identifiers.
  • Processing the event log file can further include filtering and/or reducing noise. Filtering and/or reducing noise can be performed together with pre-processing the event log file, or be performed in tandem, or be performed in a separate step. Typically, filtering and/or reducing noise is a separate function to pre-processing.
  • filtering and/or reducing noise can be a function or set of functions perfonned on the data level of the event log file, i.e. include an evaluation of data.
  • Filtering the event log file can include filtering events that do not contain meaningful data, such as data that will not contribute to a more accurate description of a material blob, such as attributes that are redundant or are not included in the process map.
  • Reducing noise can include reducing noise of the event log file, such as by filtering the event log file as described above.
  • Reducing noise can further include aggregating multiple events into groups of events or single events, particularly if the frequency of an event is high compared to other events within the event log file.
  • Filtering and/or reducing noise can, for example, include removing, condensing or grouping status events which only indirectly relate to material blobs, such as events that represent common sensor readings, such as e.g. a periodic temperature reading at a processing station.
  • a system is provided which is configured to perform one or more of the methods explained herein.
  • the system can be implemented in a computer or a number of computers, such as an offline machine, a computer network, a control station or the likes, for example in an edge device such as ABB Bdgenius, and/or in a cloud such as ABB Genix.
  • the system can further comprise a cloud-based application, the cloud forming part of the system for performing the method.
  • the system or components of the system may comprise a network interface for connecting the device to a data network, in particular a global data network.
  • the data network may be a TCP/IP network such as Internet.
  • the device e.g, the processing station/device, is operatively connected to the network interface for carrying out commands received from the data network.
  • the commands may include a control command for controlling the device to carry out a task such as performing an operation described herein in relation to the method.
  • the device is adapted for carrying out the task in response to the control command.
  • the commands may include a status request.
  • the device may be adapted for sending a status information to the network interface, and the network interface is then adapted for sending the status information over the network.
  • the commands may include an update command including update data.
  • the device may be adapted for initiating an update in response to the update command and using the update data.
  • the data network may be an Ethernet network using TCP/TP such as LAN, WAN or Internet.
  • Tire data network may comprise distributed storage units such as Cloud.
  • the Cloud can be in form of public, private, hybrid or community Cloud.
  • the device further comprises a network interface for connecting the device to a network, wherein the network interface is configured to transceive digital signal/data between the device and the data network, wherein the digital signal/data include operational command and/or information about the device or the network.
  • the system can include one or more processing stations and/or processing devices as explained herein, and include control modules or control units, such as control modules/units capable of performing aspects of the methods.
  • a processing device includes a processing unit for processing a material, and a control unit connected with the processing unit and comprising a prediction unit configured to determine predicted quality data for processing data of the processing unit during processing the material.
  • control unit is configured to run an instance of a trained quality prediction model as explained herein.
  • the processing device may be a mining device such as a separator, a conveyor belt, or a comminution device, such a mill or a crusher, in particular a gearless mill drive.
  • a mining device such as a separator, a conveyor belt, or a comminution device, such a mill or a crusher, in particular a gearless mill drive.
  • the processing device is typically configured to dynamically provide the processing data (during processing the material).
  • the processing device may include an internal sensor unit for measuring at least a part of the processing data.
  • the processing device further includes an interface for sending the processing data and/or the determined predicted quality data to a higher-level controller for monitoring and/or operating the continuous industrial process.
  • a higher-level controller for monitoring and/or operating the continuous industrial process.
  • the system includes at least one of, typically both of a processing device as explained herein, and the higher-level controller for supervising, monitoring and/or operating the processing device and the continuous industrial process, respectively, and/or the system is configured to perform the method as explained herein.
  • the methods, devices and systems described herein allow for improved continuous quality monitoring and even improved controlling of continuous industrial processes, such as mining processes, without requiring additional sensors.
  • FIG. 1 is a flow chart of a method for training a quality prediction model for a processing device of a continuous industrial process for training a quality prediction model for a processing device of a continuous industrial process according to an embodiment.
  • Fig. 2 is a flow chart of a method for training a quality prediction model for a processing device of a continuous industrial process for training a quality prediction model for a processing device of a continuous industrial process according to an embodiment.
  • Fig. 3 is a flow chart of a method for controlling of a continuous industrial process comprising a processing device according to an embodiment.
  • Fig.. 4 is a schematic view of a processing device according to an embodiment.
  • Fig. 5 is a schematic view of a system for monitoring and operating an industrial process according to an embodiment.
  • Fig. 6 is a schematic view of a mine process representative for an industrial process as described herein.
  • Fig. 7 is a schematic view of a material blob representation within a processing station as described herein.
  • a mine process is used as an example for illustrating the general aspects described above.
  • the described method and system can be equally applicable for other types of industrial process.
  • FIG. 1 an exemplary computer-implemented training method 1000 is explained.
  • processing data ⁇ PD ⁇ generated and/or used by a processing device during processing a material are received.
  • the brackets ⁇ ⁇ shall indicate that the respective data may refer to several or even a plurality of respective data, for example a batch of data.
  • the processing data ⁇ PD ⁇ may also be a sequence of respective data typically including a time information (time stamp).
  • quality data ( ⁇ QD ⁇ ) of the material processed by the processing device are determined. Determining the quality data ( ⁇ QD ⁇ ) may be achieved by modeling the processing of the material (by the processing device, e.g. in by the processing device).
  • corresponding processing data ⁇ PD ⁇ and quality data ⁇ PQD ⁇ may be received in one block, e.g. as respective datasets ⁇ PD, QD ⁇ .
  • corresponding processing data ⁇ PD ⁇ and quality data ⁇ PQD ⁇ may refer to historic data and/or be determined using a model of the continuous industrial process including a model of the processing device.
  • a digital twin of the industrial process may be used for this purpose.
  • the corresponding processing data ⁇ PD ⁇ and quality data ⁇ PQD ⁇ may be used to train (or later to retrain) a quality prediction model for the industrial process to output predicted product quality data ⁇ pQD ⁇ for a respective input of processing data ⁇ PD ⁇ .
  • training may be done iteratively using a plurality of data and datasets ⁇ PD, PQD ⁇ , respectively.
  • fig. 2 illustrates a computer-implemented training method 1001 which is typically similar to training method 1000 and also includes respective blocks 1101, 1201 and 1301 referring to receiving processing data ⁇ PD ⁇ , determining quality data ⁇ QD ⁇ and training a quality prediction model. However, block 1301 is more specific compared to block 1300.
  • the processing data ⁇ PD ⁇ are used as input of the quality prediction model to determine intermediate predicted quality data ⁇ ipQD ⁇ in a block 1311 .
  • the intermediate predicted quality data ⁇ ipQD ⁇ and the product quality data ⁇ PQD ⁇ may be used to change one or more parameters of the quality prediction model in a block 1301.
  • This may be based on a comparison of the intermediate predicted quality data ⁇ ipQD ⁇ and the quality data ⁇ QD ⁇ , e.g. based on a difference between the two values.
  • FIG. 3 an exemplary computer-implemented controlling method 2000 is explained.
  • a block 2100 material is processed with a processing device of a continuous industrial process.
  • processing data ⁇ PD ⁇ generated and/or used by the processing device (50, 50’) during the processing of the material in block 2100 are used as input of a trained quality prediction model which outputs corresponding predicted quality data ⁇ pQD ⁇ .
  • the trained quality prediction model may in particular be (a runtime instance of) a quality prediction model trained with one of the methods 1000, 1001 explained above with regard to Figs. 1, 2.
  • predicted quality data fpQD ⁇ may be determined in real time and of near real time, i.e. with a short delay only.
  • the determined predicted quality data ⁇ pQD ⁇ may be monitored, further processed, and/or in particular be used for controlling the continuous industrial process, i.e, controlling the material flow and/or the material processing of the continuous industrial process, even more particular for respective controlling the processing device and/or a further processing device of the continuous industrial process, in a block 2300.
  • Fig. 4 schematically illustrates a processing device 50 that may be used in a continuous industrial process.
  • processing device 50 includes a processing unit 51 for processing a material, i.e. for physically and/or chemically transforming the material from an input state M to an output state M’, different to the input state M, and a control unit 52 connected with the processing unit 51
  • the processing device 50 may be a mill or another comminution device for processing an ore.
  • control unit 52 has a prediction unit which is configured to determine predicted quality data ⁇ pQD ⁇ for processing data ⁇ PD ⁇ of the processing unit 51 during processing the material M, M’.
  • control unit 52 may be configured to control processing unit 51 during processing the material M, M’.
  • control unit 52 may be configured to control a parameter of the processing unit 51,
  • the parameter to be controlled may for example be chosen from the (non-limiting) list including: power consumption of the processing unit 51, a charge or filling level of the processing unit 51, a speed of the processing unit 51, a ball charge or pebble charge of the processing unit 51, a feed particle size of the processing unit 51 , and a (produced) product particle size of the material M’ coming out of the of the processing unit 51 ,
  • At least part of these parameters may represent and/or be a part of the processing data ⁇ PD ⁇ used for predicting current quality data ⁇ pQD ⁇ .
  • processing device 50 typically has an internal sensor unit 53 for measuring at least a part of the processing data ⁇ PD ⁇ .
  • control unit 52 may be configured to receive control commands ⁇ C ⁇ from a higher- level (supervising) controller for monitoring and/or operating the continuous industrial process.
  • control unit 52 is configured to process the commands ⁇ C ⁇ and take into account the commands ⁇ C ⁇ for controlling the processing unit 51 .
  • processing device 50 has an interface (not shown) for sending the predicted quality data ⁇ pQD ⁇ and typically also the processing data ( ⁇ PD ⁇ ) to an external device, in particular the higher-level controller and/or for receiving the control commands ⁇ C ⁇ ,
  • a system 100 for monitoring and/or operating a continuous industrial process is shown.
  • the exemplary industrial process includes several processing stations (not shown, see also Fig, 6), each processing station comprising a controller.
  • the controllers comprise a respective software, examples of which will be discussed in more detail with reference to Fig. 6.
  • a mining software system 110 including software 112, software 114, software 116 and software 118 is provided, Mining software system 110 may include several subsystems each including one of the software 112- 118.
  • Each software 112-116 can be specific for the type of processing station, i.e, the software type can emphasize different aspects specific to the type of process performed by the processing station, include different interfaces, file or data formats etc.
  • Software 118 may be a control or scheduling software for controlling software 112-116.
  • Exemplary system 100 includes a digital twin 130, particularly a process digital twin.
  • the digital twin 130 is typically represented in the form of a data construct.
  • digital twin 130 is typically comprised within a computer system or such.
  • the digital twin 130 includes an information metamodel 140 and an adaptive simulation model 150.
  • the software system 110 is connected to the information metamodel 140 by an exporter/importer 120.
  • the exporter/importer 120 exports data provided by the processing stations in a format that is compatible with the information metamodel 140, and imports data into the mining software system 110 in a format that is compatible with the respective software 112, 114, 116, Exporters/importers, such as exporter/importer 120, 122, 124 described herein, will sometimes be referred to only as exporter or importer, depending on their current function.
  • the exemplary information metamodel 140 includes a type library 142.
  • the type library 142 includes data model entity types from all connected systems. In the shown embodiment, the type library 142 is generated by utilizing the exporter 120.
  • the type library includes the data model entity types, particularly in the form of AutomationML System Unit Classes.
  • the data model entity types are be based on the process layout model as derived from the respective processing station layout, and the process interface model derived from the interface models of the respective processing stations.
  • the processing station layouts and the interface models of the processing stations are provided by the software 112, 114, 116 and processed by the exporter 120
  • the exemplary type library 142 can be built in a process referred to as type creation.
  • Type creation can typically be performed as a first operation in building an information metamodel.
  • Type creation typically is semi-automatic.
  • Type creation can utilize the exporter 120.
  • Type creation typically is only required once for a specific industrial process.
  • the information metamodel 140 typically includes an instance model 144.
  • the instance model 144 may include an instance hierarchy, particularly in the form of an AutomationML Instance Hierarchy.
  • the instance hierarchy may include instances for all connected systems based on the types included in the type library 142.
  • the instance model 144 may further include the process layout model and the interface model.
  • the instance model 144 may include a representation of the industrial process, particularly representing the processing stations as instances of a type according to types within the type library 142.
  • the instances may be modelled to be physically interconnected according to the process layout model, and/or connectively interconnected according to the process interface model.
  • instance model 144 may be built in a process referred to as instance creation.
  • Instance creation can utilize the exporter 120.
  • Instance creation can typically be performed automatically and thus quickly respond to changes in the industrial process, such as the layout of the industrial process, such as the removing or adding of processing stations.
  • the information metamodel 140 in combination with exporter/importer 120 can provide connectivity between software 112, 114, 116 even though the software using different data formats. For this, data from one software, e.g. software 112, is exported by exporter 120. The information metamodel 140 then provides connectivity between connected system 110, since the systems of the industrial process are included in the information metamodel, thus, data can be routed according to the information metamodel. The data is then provided by the importer 120 to, e.g. software 114, in a data format compatible with software 114.
  • the digital twin 130 typically includes an adaptive simulation model 150.
  • the adaptive simulation model 150 may be generated using the information metamodel 140 provided by exporter 122.
  • the adaptive simulation model 150 may be selected from a simulation model library 160. Adaptions to the adaptive simulation model 150 and/or the simulation model library 160 may be performed, based on accuracy threshold requirements. The adaption can be performed with importer/exporter 124.
  • the generation of the adaptive simulation 150 model can be semi-automatic.
  • the adaptive simulation model 150 includes live data connections to the processing stations, i.e. the mining software system 110, for receiving live data from the processing stations.
  • the live data connection is provided by exporting the raw data of software system 110 and the software 112, 114, 116, respectively, with exporter 120 via the information metamodel 140 and exporter 122.
  • the exported live data typically represents current states of the processing stations, which, due to contextualizing the data in the information metamodel, is provided in a homogeneous format and in the holistic context of the industrial process.
  • the adaptive simulation model 150 can perform simulations of the industrial process according to the live data. Further, the adaptive simulation model 150 typically provides the results of the simulation performed by the adaptive simulation model 150 to analytics applications 170 via exporter 128.
  • the analytics application 170 includes a current operations monitor 172.
  • the current operations monitor 172 utilizes live data to visualize current results.
  • the current operations monitor 172 may benefit from an accurate simulation, thus, the adaptive simulation model 150 can be adapted to closely represent the current state of the industrial process.
  • Analytics application 170 may include a what-if analysis tool 176.
  • the what-if analysis tool 176 can provide an analysis based on user-defined simulation scenarios and can offer the possibility of initial value adjustment.
  • the what-if analysis tool 176 can utilize the results of the simulation of a current state by changing one or more parameters within the current state and simulating the industrial process according to the adaptive simulation model according to the current model with the changed parameters.
  • Analytics application 170 may further include a future operations predictor (not shown).
  • the future operations predictor can provide predictive analyses for future timeframes.
  • the future operations predictor can utilize the results of the simulation of future timeframes based on an extrapolation of a current state.
  • one or more of the analytics applications 170 can provide feedback to the digital twin 140 via a feedback line (importer/exporter) 171, particularly to the information metamodel 140.
  • the feedback can include information relating to differences between toe observed live data and the simulation based on the live data, and be utilized for adapting, particularly improving or tuning the information metamodel.
  • system 100 farther includes a typically remote computational component 75 such a cloud service which is configured to receive corresponding quality data ⁇ QD ⁇ and processing data ⁇ PD ⁇ via an exporter 171b and to (offline) train a quality prediction model using the corresponding quality data ⁇ QD ⁇ and processing data ⁇ PD ⁇ as trainings datasets. Training may be performed as e.g. explained above with regard to Figs. 1 A, 1B.
  • the resulting parameter of the quali ty prediction model ⁇ PQPM ⁇ or even the complete characterizing parameter set of the quality prediction model may be transferred to a processing device 50’ of the continuous industrial process.
  • Processing device 50’ is typically similar or can even correspond to processing device 50 explained above with respect to Fig, 4, and/or is configured to predict current quality data ⁇ pQD 5 ⁇ during processing the material by feeding current processing data ⁇ PD’ ⁇ to an instance of the quality prediction model.
  • the predicted current quality data ⁇ pQD’ ⁇ and typically also the corresponding current processing data ⁇ PD’ ⁇ may be transferred via an exporter 181 to mining software system 110, in particular running controlling software 118,
  • the corresponding current processing data ⁇ PD’ ⁇ and predicted quality data ⁇ pQD 5 ⁇ may be transferred via an exporter 181 a as feedback to process digital twin 130, in particular information metamodel 130,
  • the mine process can be an industrial process as described with regard to Fig. 5.
  • the layer model 200 has three layers, with the first layer 202 corresponding to the physical aspects of the mine process.
  • the mine process includes different stations, such as processing stations.
  • the mine process includes a block model 220,
  • the block model can be a block model of a mine and be utilized in a planning step.
  • the mine process includes a planning station 221.
  • the planning station 221 can be utilized for the planning of operations within the mine, such as blasting.
  • the mine process includes blasting 222.
  • Blasting can produce material, such as raw material, such as ore.
  • the material is hauled in operation 223, and stored in a stockyard 224.
  • the material is then processed in station 225, e.g. a processing station, shipped to a port 226 and sold on a market 227.
  • the layer model 200 includes a second layer 204.
  • the second layer 204 includes mine software, control systems, and edge systems.
  • the second layer 204 systems are connected to the first layer systems for receiving data from the first layer 202 systems.
  • the data can be provided from the first layer 202 systems to the second layer 204 in various file formats 230.
  • the file formats may be specific to the first layer 202 system or the second layer 204 system, e.g. utilizing a common file format or an industry standard file format.
  • the block model 220 and the planning station 221 can be connected to a geological planning software 240 for providing data from the block model 220 and the planning station 221 to the geological planning software 240.
  • the data can be provided to the geological planning software 240 as an AML file.
  • the blasting process 222 and the hauling process 223 can be connected to a knowledge manager 242, e.g. ABB AbilityTM Knowledge Manager for mining and mineral processing, for providing data from the blasting process 222 and the hauling process 223 to the knowledge manager 242,
  • the data can be provided to the knowledge manager 242 as an B2MML file, and/or in an ABB AbilityTM file format.
  • the stockyard 224 can be connected to a stockyard management system 244, e.g. ABB AbilityTM Stockyard Management System, for providing data from the stockyard 224 to the stockyard management systems 244,
  • the data can be provided to the stockyard management system 244 as an AML file, and/or in an ABB AbilityTM file format.
  • the processing station 225 can be connected to a control system 246, e.g. ABB AbilityTM System 800xA, for providing data from the processing station 225 to the control system 246.
  • the data can be provided to the control system 246 as an AML file, and/or an MTP file.
  • the port 226 can be connected to other systems 248, e.g. third party systems, such as logistics systems, systems which have an economic focus or such, for providing data from the port 226 to the system 248.
  • the data can be provided to the system 248 in a file format compatible with the system 248, such as an OPC UA file.
  • the layer model 200 includes a third layer 206.
  • the third layer 206 includes mine layouts and interface models.
  • the mine layouts can be processing station layouts according to embodiments described herein.
  • the interface models can be interface models of a processing station, according to embodiments described herein.
  • the mine layouts and interface models are represented as AML files 260, wherein each file 260 includes the respective data of the underlying processing station.
  • the files 260 can be configured for enabling the creation of an information metamodel, as described herein. Creation of the files 260 based on the data provided by the software of the second layer 204 can be performed by utilizing an exporter/importer, such as the exporter/importer 120 described in relation to the embodiment shown in Fig, 5,
  • the event log can be an event log file provided by the digital twin, particularly adaptive simulation model according to an embodiment described herein.
  • the process mining can be performed by an analysis application 170, such as a current operations monitor 172 described herein in relation to embodiment shown in Fig. 1.
  • a processing station with a material flow is shown.
  • the processing station is a crusher comprising a crusher input buffer 310, a crusher 320, a conveyor 330 and a stockpile 340.
  • Two states of the processing station are shown, with state 302 being a first state and state 304 being a second state.
  • Each state 302, 304 is represented as a number of material blobs, such as material blobs 350, 352 and 354. In the given example, the material blobs have already been identified and have attributes correlated thereto.
  • three material blobs are present in the input buffer 310, two material blobs 350, 352 are present in the crusher 320, one material blob 354 is present in the conveyor 330 and three material Mobs are present in the stockpile 340.
  • material blob 352 has, in the first state 302, the attributes 303.
  • the attributes 303 include an identifier of the material blob (“352”), and the attributes “LOCATION: CRUSHER” and “WEIGHT: 50G.
  • the processing station performs a crushing operation and, e.g. after 50 t material have been processed, provides an event which can be, via the digital twin, included in an event log file.
  • the event log file can be mined for events relating to one of the material blobs of the processing station, such as event 305.
  • Event 305 includes information “EVENT: CRUSHED 50t”.
  • Event 305 includes information “EVENT: CRUSHED 50t”.
  • the process mining operation of the example in Fig. 7 can be employed at all stages of an industrial process represented by a digital twin, and for various types of events relating to the material flow within the industrial process.
  • a process map based on a material flow within the industrial process, can be created.
  • the process map can be utilized for visualizing the material flow within the process, and for obtaining statistical data of the process.
  • deviations to standard operations can be more easily identified even for complex industrial processes, and the process can be optimized and better scheduled.

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Abstract

L'invention concerne un procédé (1000, 1001) pour l'entraînement d'un modèle de prédiction de qualité pour un dispositif de traitement (50, 50´) d'un procédé industriel continu. Le procédé comprend la réception (1100, 1101) de données de traitement ({PD}) générées et/ou utilisées par le dispositif de traitement (50, 50´) pendant le traitement d'un matériau (M, M´), la détermination (1200, 1201), pour les données de traitement ({PD}), de données de qualité ({QD}) correspondantes comprenant la modélisation du traitement du matériau (M, M´), et l'utilisation (1300, 1301) des données de traitement ( ({PD}) et les données de qualité ({QD}) pour entraîner ou entrainer à nouveau le modèle de prédiction de qualité pour déterminer des données de qualité prédites ({pQD}) pour les données de traitement ({PD}).
PCT/EP2021/056706 2021-03-16 2021-03-16 Procédé d'entraînement d'un modèle de prédiction de qualité pour un dispositif de traitement d'un processus industriel continu, procédé de commande d'un processus industriel continu comprenant un dispositif de traitement, et dispositif de traitement WO2022194358A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170032281A1 (en) * 2015-07-29 2017-02-02 Illinois Tool Works Inc. System and Method to Facilitate Welding Software as a Service
CN107321646A (zh) 2017-07-26 2017-11-07 成都理工大学 放射性矿石分选系统
WO2019053261A1 (fr) 2017-09-18 2019-03-21 Abb Schweiz Ag Procédé de fonctionnement d'un circuit de comminution et circuit de comminution correspondant

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170032281A1 (en) * 2015-07-29 2017-02-02 Illinois Tool Works Inc. System and Method to Facilitate Welding Software as a Service
CN107321646A (zh) 2017-07-26 2017-11-07 成都理工大学 放射性矿石分选系统
WO2019053261A1 (fr) 2017-09-18 2019-03-21 Abb Schweiz Ag Procédé de fonctionnement d'un circuit de comminution et circuit de comminution correspondant

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