Method and system for controlling a fluid transport system

Patent metadata

Field	Value
Publication number	US-12379696-B2
Application number	US-202117927346-A
Country	US
Kind code	B2
Filing date	May 21, 2021
Priority date	May 25, 2020
Publication date	Aug 5, 2025
Grant date	Aug 5, 2025

Abstract

A method for controlling operation of a fluid transport system by applying a self-learning control process. The method includes: receiving obtained values of input signals during operation of the system during a first period of time, which is controlled by a predetermined control process, automatically selecting a subset of the input signals based on the received obtained values of the input signals, receiving obtained values of at least the selected subset of input signals during a second period of time, which is controlled by applying the self-learning control process, which is configured to control operation based only on the selected subset of input signals, and wherein applying the self-learning control process includes updating the self-learning control process based on the received obtained values of the selected subset of the input signals and based on at least an approximation of a performance indicator function.

First claim

The invention claimed is:

1. A computer-implemented method for controlling operation of a fluid transport system, by a applying a self-learning control process, the method comprising steps of:
receiving obtained values of a plurality of input signals during operation of the fluid transport system during a first period of time, wherein operation of the fluid transport system during the first period of time is controlled by a predetermined control process,
automatically selecting a subset of the plurality of input signals based on the received obtained values of the plurality of input signals,
receiving obtained values of at least the selected subset of input signals during operation of the fluid transport system during a second period of time, and
controlling the fluid transport system during the second period of time by applying the self-learning control process, wherein the self-learning control process is configured to control operation of the fluid transport system based only on the selected subset of input signals, and wherein applying the self-learning control process comprises updating the self-learning control process based on the received obtained values of the selected subset of the input signals and based on at least an approximation of a performance indicator function.

2. A computer-implemented method according to claim 1 , wherein the predetermined control process is a non-adaptive control process.

3. A computer-implemented method according to claim 1 , wherein the plurality of input signals defines an input space having a first number of dimensions; wherein the selected subset of input signals defines a reduced input space having a reduced number of dimensions, smaller than the first number of dimensions.

4. A computer-implemented method according to claim 1 , wherein automatically selecting includes applying one or more information-theoretic selection criteria.

5. A computer-implemented method according to claim 4 , wherein the one or more information-theoretic selection criteria include a mutual information criterion based on a determined mutual information measure between respective ones of the plurality of input signals and an observed performance measure.

6. A computer-implemented method according to claim 5 , wherein the observed performance measure includes at least one observed performance indicator evaluated at a plurality of times, optionally implementing a time-dependent weighting of performance indicator values or a time-dependent weighting in dependence of a rate of fluid flow in the fluid transport system.

7. A computer-implemented method according to claim 1 , wherein the automatically selecting includes selecting at least one input signal that is associated with a time shift delay dependent on a flow rate of fluid flow in the fluid transport system.

8. A computer-implemented method according to claim 1 , further comprising configuring an initial version of the self-learning control process based on the selected subset of input signals; wherein configuring the initial version of the self-learning control process comprises pre-training the initial version of the self-learning control process based on the received obtained values of the plurality of input signals during the first period of time and based on performance indicator values recorded during operation of the fluid transport system during the first period of time.

9. A computer-implemented method according to claim 8 ; wherein the automatic selection and the configuration of the initial version of the self-learning control process are performed during a transitional period, subsequent to the first period and prior to the second period.

10. A computer-implemented method according to claim 1 , wherein the self-learning control process implements a reward-based learning agent.

11. A computer-implemented method according to claim 10 , wherein the reward-based learning agent is a reinforcement learning agent.

12. A computer-implemented method according or claim 10 , wherein updating the self-learning control process is based on one or more observed performance indicators, observed during a time horizon or a flow-dependent time horizon.

13. A computer-implemented method according to claim 1 , wherein the self-learning control process includes at least one stochastic component.

14. A computer-implemented method according to claim 1 , wherein updating the self-learning control process is based on an approximation of the performance indicator function, wherein said approximation is a performance approximator function approximating a dependence of the performance indicator function on the selected subset of input signals and/or on one or more control actions taken by the self-learning control process to control the fluid transport system.

15. A computer-implemented method according to claim 14 , wherein the performance approximator function is parametrized by a plurality of weight parameters, and wherein the updating the self-learning control process comprises updating one or more of the plurality of weight parameters.

16. A computer-implemented method according to claim 1 , wherein the performance indicator function includes a comfort indicator and/or a cost indicator.

17. A computer-implemented method according to claim 1 , further comprising:
automatically selecting a new subset of the plurality of input signals based on the received obtained values of the plurality of input signals, received during the second period,
receiving obtained values of at least the selected new subset of input signals during operation of the fluid transport system during a third period of time, wherein operation of the fluid transport system during the third period of time is controlled by applying a new self-learning control process adapted to the selected new subset of input signals, wherein the new self-learning process is configured to control operation of the fluid transport system based only on the selected new subset of input signals, and wherein applying the new self-learning control process comprises updating the new self-learning control process based on the received obtained values of the selected new subset of the input signals and based on at least the approximation of the performance indicator function.

18. A control system for controlling a fluid transport system; wherein the control system is configured to perform the steps of the computer-implemented method according to claim 1 .

19. A control system according to claim 18 , comprising a control unit communicatively coupled to one or more controllable components of the fluid transport system; wherein the control unit is configured to receive obtained values of at least the selected subset of input signals during operation of the fluid transport system and to selectively control operation of the fluid transport system by applying the predetermined control process or by applying the self-learning control process.

20. A control system according to claim 18 , comprising a data processing system configured to receive the obtained values of a plurality of input signals during operation of the fluid transport system during the first period of time, and to automatically select the subset of the plurality of input signals based on the received obtained values of the plurality of input signals.

21. A control system according to claim 20 , wherein the data processing system is a remote data processing system, in particular a cloud service, located remotely from the control unit.

Assignees

• Grundfos Holding As

Inventors

• Overgaard Anders
• Nielsen Brian Kongsgaard
• Kallesøe Carsten Skovmose

Classifications

• G05B13/024

  in which a parameter or coefficient is automatically adjusted to optimise the performance · CPC title

• G05B2219/23288

  Adaptive states; learning transitions · CPC title

• G05B2219/2614

  HVAC, heating, ventillation, climate control · CPC title

• G05B13/0265 Primary

  the criterion being a learning criterion · CPC title

• G05B13/048 Primary

  using a predictor · CPC title