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US 2022/0035346 A1Published application
Predictions For A Process In An Industrial Plant
AssigneeABB Schweiz AG
InventorsMehmet Mercangoez, Andrea Cortinovis
Life of this patent
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Abstract
To generate real-time or at least near real-time predictions for a process in an industrial plant, a set of neural networks are trained to create a set of trained models. The set of trained models is then used to output the predictions, by inputting online measurement results in an original space to two trained models whose outputs are fed, as reduced space inputs and reduced space initial states, to a third trained model. The third trained model processes the reduced space inputs to reduced space predictions. They are fed to a fourth trained model, which outputs the predictions in the original space.
Claims18
A computer implemented method comprising:
- receiving online measurement results comprising, for a plurality of variables, values that are measured from a process in an industrial plant;
- inputting the measurement results in an original space to a first trained model of the process in the industrial plant and to a second trained model of the process in the industrial plant;
- processing, by the first trained model, the measurement results to reduced space inputs;
- processing, by the second trained model, the measurement results to reduced space initial states;
- feeding the reduced space inputs and the reduced space initial states to a third trained model of the process in the industrial plant;
- processing, by the third trained model, the reduced space inputs and the reduced space initial states to reduced space predictions;
- feeding the reduced space predictions to a fourth trained model of the process in the industrial plant;
- processing, by the fourth trained model, the reduced space predictions to predictions in the original space;
- and
- outputting the predictions.
7 more elements
A computer implemented method for creating a set of trained models for a process in an industrial plant, the set of trained models including a first trained model, a second trained model, a third trained model and a fourth trained model, the method comprising:
- using past measurement results from the process in the industrial plant as training data;
- creating the first trained model and the fourth trained model by training simultaneously an encoding artificial neural network for dimensionality reduction to learn the first trained model and a decoding artificial neural network for dimensionality restoring to learn the fourth trained model, wherein the dimensionality reduction is from an original space to a reduced space and the dimensionality restoring is from the reduced space to the original space;
- and
- creating the second trained model and the third trained model by training neural networks either sequentially, first a recurrent neural network between the first and fourth trained models to create the third trained model for reduced space predictions and then a feedforward artificial neural network to create the second trained model for reduced space initial states, or training simultaneously the recurrent neural network between the first and fourth trained models and the feedforward artificial neural network.
1 more element
A non-transitory computer readable medium comprising one of a first set of program instructions, a second set of program instructions, and a third set of program instructions;
- wherein the first set of program instructions causes a computing equipment to perform:
- receiving online measurement results comprising, for a plurality of variables,
- values that are measured from a process in an industrial plant;
- inputting the measurement results in an original space to a first trained model of the process in the industrial plant and to a second trained model of the process in the industrial plant;
- processing, by the first trained model, the measurement results to reduced space inputs;
- processing, by the second trained model, the measurement results to reduced space initial states;
- feeding the reduced space inputs and the reduced space initial states to a third trained model of the process in the industrial plant;
- processing, by the third trained model, the reduced space inputs and the reduced space initial states to reduced space predictions;
- feeding the reduced space predictions to a fourth trained model of the process in the industrial plant;
- processing, by the fourth trained model, the reduced space predictions to predictions in the original space;
- and
- outputting the predictions;
- wherein the second set of program instructions causes a computing equipment to perform:
- using past measurement results from the process in the industrial plant as training data;
- creating the first trained model and the fourth trained model by training simultaneously an encoding artificial neural network for dimensionality reduction to learn the first trained model and a decoding artificial neural network for dimensionality restoring to learn the fourth trained model, wherein the dimensionality reduction is from an original space to a reduced space and the dimensionality restoring is from the reduced space to the original space;
- and
- creating the second trained model and the third trained model by training neural networks either sequentially, first a recurrent neural network between the first and fourth trained models to create the third trained model for reduced space predictions and then a feedforward artificial neural network to create the second trained model for reduced space initial states, or training simultaneously the recurrent neural network between the first and fourth trained models and the feedforward artificial neural network;
- wherein the third set of program instructions causes a computing equipment to perform:
- receiving, in a controller implementing a model predictive control, online measurement results comprising, for a plurality of variables, values that are measured from the process in the industrial plant, and predictions in an original space, which predictions are based on the online measurement results and outputted by a set of trained models, which are created according to the second set of program instructions for the process in the industrial plant;
- using, in the controller implementing the model predictive control, as an internal model in the model predictive control, the set of trained models, to determine, from the received online measurement results and predictions, constraints for an optimization problem of the model predictive control;
- determining, by the optimization problem, based on the constraints determined using the trained models, one or more optimized actions to manipulate the process;
- and causing, by the controller, manipulation of the process according to the one or more optimized actions.
19 more elements
Equipment comprising at least one processor and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the equipment to perform:
- receiving online measurement results comprising, for a plurality of variables, values that are measured from a process in an industrial plant;
- inputting the measurement results in an original space to a first trained model of the process in the industrial plant and to a second trained model of the process in the industrial plant;
- processing, by the first trained model, the measurement results to reduced space inputs;
- processing, by the second trained model, the measurement results to reduced space initial states;
- feeding the reduced space inputs and the reduced space initial states to a third trained model of the process in the industrial plant;
- processing, by the third trained model, the reduced space inputs and the reduced space initial states to reduced space predictions;
- feeding the reduced space predictions to a fourth trained model of the process in the industrial plant;
- processing, by the fourth trained model, the reduced space predictions to predictions in the original space;
- and
- outputting the predictions.
7 more elements
14-15. (canceled)
The equipment comprising at least one processor and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the equipment to perform:
- using past measurement results from a process in an industrial plant as training data;
- creating a first trained model and a fourth trained model by training simultaneously an encoding artificial neural network for dimensionality reduction to learn the first trained model and a decoding artificial neural network for dimensionality restoring to learn the fourth trained model, wherein the dimensionality reduction is from an original space to a reduced space and the dimensionality restoring is from the reduced space to the original space;
- and
- creating a second trained model and a third trained model by training neural networks either sequentially, first a recurrent neural network between the first and fourth trained models to create the third trained model for reduced space predictions and then a feedforward artificial neural network to create the second trained model for reduced space initial states, or training simultaneously the recurrent neural network between the first and fourth trained models and the feedforward artificial neural network.
1 more element
The equipment comprising at least one processor and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the equipment to perform:
- receiving, in a controller implementing a model predictive control, online measurement results comprising, for a plurality of variables, values that are measured from a process in an industrial plant, and predictions in an original space, which predictions are based on the online measurement results and outputted by a set of trained models, which are created by the equipment in the industrial plant, the equipment including at least one processor and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the equipment to perform:
- using past measurement results from a process in an industrial plant as training data:
- creating a first trained model and a fourth trained model by training simultaneously an encoding artificial neural network for dimensionality reduction to learn the first trained model and a decoding artificial neural network for dimensionality restoring to learn the fourth trained model, wherein the dimensionality reduction is from an original space to a reduced space and the dimensionality restoring is from the reduced space to the original space;
- and
- creating a second trained model and a third trained model by training neural networks either sequentially, first a recurrent neural network between the first and fourth trained models to create the third trained model for reduced space predictions and then a feedforward artificial neural network to create the second trained model for reduced space initial states, or training simultaneously the recurrent neural network between the first and fourth trained models and the feedforward artificial neural network;
- using, in the controller implementing the model predictive control, as an internal model in the model predictive control, the set of trained models, to determine, from the received online measurement results and predictions, constraints for an optimization problem of the model predictive control;
- determining, by the optimization problem, based on the constraints determined using the trained models, one or more optimized actions to manipulate the process;
- and causing, by the controller, manipulation of the process according to the one or more optimized actions.
5 more elements
A system comprising at least:
- one or more sensors providing measurement results comprising values for a plurality of variables on at least one process in an industrial plant;
- memory for storing the measurement results;
- and
- equipment configured to:
- receive the measurement results;
- input the measurement results in an original space to a first trained model of the at least one process in the industrial plant and to a second trained model of the at least one process in the industrial plant;
- process, by the first trained model, the measurement results to reduced space inputs;
- process, by the second trained model, the measurement results to reduced space initial states;
- feed the reduced space inputs and the reduced space initial states to a third trained model of the at least one process in the industrial plant;
- process, by the third trained model, the reduced space inputs and the reduced space initial states to reduced space predictions;
- feed the reduced space predictions to a fourth trained model of the at least one process in the industrial plant;
- process, by the fourth trained model, the reduced space predictions to predictions in the original space;
- and
- output the predictions.
11 more elements
Dependencies are inferred from the claim text.