Plant state operating analysis and control

Patent metadata

Field	Value
Publication number	US-11720072-B2
Application number	US-201916577656-A
Country	US
Kind code	B2
Filing date	Sep 20, 2019
Priority date	Sep 28, 2018
Publication date	Aug 8, 2023
Grant date	Aug 8, 2023

Abstract

A system for analyzing operational data associated with a plant that has processing equipment configured and controlled to run a process involving at least one tangible material. Actual operational data corresponding to plant operations is received by a computing device that may relate to production, equipment, workforce, automation systems, safety, and/or cybersecurity of the plant. A model of the plant is generated based on the actual operational data, where the model indicates ideal plant operations including model operational data. The actual plant operational data and the model operational data are compared. Based on the operational data and the comparison of the operational data to model operational data, at least one recommendation for an action associated with the plant is determined.

First claim

The invention claimed is:

1. A method of managing the operation of a plant in a plant state operating analysis and control system, comprising:
receiving actual operational data using a first computing device associated with the plant that has processing equipment configured and controlled to run a process involving at least one tangible material or a power application originating from at least one device in the plant;
generating based on the actual operational data at least one model of the plant using categorization, importance value and confidence value relating to reliability associated with the actual operation data that exceeds a threshold, wherein the model provides ideal plant operations including model operational data,
wherein the categorization includes categories corresponding to process, processing equipment, people, control, and safety information, wherein the importance value corresponds to significance of problem indicated by the actual operational data, and the confidence value corresponds confidence level associated with all or portions of the organizational data, such that relatively more reliable organizational data is separated from relatively less reliable organizational data;
comparing the actual operational data and the model operational data,
determining based on the actual operational data and the comparing, at least one recommendation for an action associated with the plant; and
transmitting the at least one recommendation to at least one of the first computing device, a second computing device, and an operator implementing the action.

2. The method of claim 1 , wherein the actual operational data is real-time data, and wherein the method is performed by the first computing device in real-time.

3. The method of claim 1 , wherein the actual operational data includes historical operational data for the plant.

4. The method of claim 1 , wherein the actual operational data comprises at least one of information regarding profit or loss of the plant, the processing equipment, workforce performance, automation system performance, safety system performance, and cybersecurity performance.

5. The method of claim 1 , wherein the recommendation is determined from the actual operational data that collectively includes 2 or more different parameters of the process, 2 or more issues associated with the process, or 2 or more issues associated with the processing equipment.

6. The method of claim 5 , wherein the recommendation comprises a plurality of recommendations, further comprising selecting first equipment from the processing equipment that is underperforming, and selecting for the first equipment first data from the actual operational data, and then linking together the first equipment and the first data to filter the plurality of recommendations to only those related to both the first equipment and the first data.

7. The method of claim 1 , wherein the model is based on a machine-learning algorithm.

8. The method of claim 1 , wherein the plant is involved in oil, gas, chemical, beverage, pharmaceutical, pulp and paper manufacturing, petroleum processes, electrical power, or water.

9. A plant state operating analysis and control system, comprising:
a computing device comprising a processor including an associated memory for realizing an analysis engine that is configured for implementing a method of plant state operating analysis and control, the plant state operating analysis and control system configured to:
receive actual operational data using the first computing device, associated with the plant that has processing equipment configured and controlled to run a process involving at least one tangible material or power application originating from at least one device in the plant;
generate based on the actual operational data, at least one model of the plant using categorization, importance value and confidence value relating to reliability associated with the actual operation data that exceeds a threshold, wherein the model indicates ideal plant operations including model operational data,
wherein the categorization includes categories corresponding to process, processing equipment, people, control, and safety information, wherein the importance value corresponds to significance of problem indicated by the actual operational data, and the confidence value corresponds confidence level associated with all or portions of the organizational data, such that relatively more reliable organizational data is separated from relatively less reliable organizational data;

compare the actual operational data and the model operational data,
determine based on the actual operational data and the comparing at least one recommendation for actions associated with the plant; and
transmit the at least one recommendation to at least one of the first computing device, a second computing device, and an operator implementing the action.

10. The system of claim 9 , wherein the actual operational data is real-time data, and wherein the method is performed by the computing device in real-time.

11. The system of claim 9 , wherein the actual operational data includes historical operational data for the plant.

12. The system of claim 9 , wherein the actual operational data comprises at least one of information regarding profit or loss of the plant, the processing equipment, workforce performance, automation system performance, safety system performance, and cybersecurity performance.

13. The system of claim 9 , wherein the recommendation is determined from the actual operational data that collectively includes 2 or more different parameters of the process, 2 or more issues associated with the process, or 2 or more issues associated with the processing equipment.

14. The system of claim 13 , wherein the recommendation comprises a plurality of recommendations, wherein the method further comprises responsive to a user selecting first equipment from the processing equipment that is underperforming, and selecting for the first equipment first data from the actual operational data, linking together the first equipment and the first data to filter the plurality of recommendations to only those related to both the first equipment and the first data.

15. The system of claim 9 , wherein the model is based on a machine-learning algorithm.

16. The system of claim 9 , wherein the plant is involved in oil, gas, chemical, beverage, pharmaceutical, pulp and paper manufacturing, petroleum processes, electrical power, or water.

Assignees

• Honeywell Int Inc

Inventors

• Bertinetti Mark
• Laycock Graeme

Classifications

• G05B17/02 Primary

  electric · CPC title

• G05B13/048

  using a predictor · CPC title

• G06N20/00

  Machine learning · CPC title

• G06Q10/0639

  Performance analysis of employees; Performance analysis of enterprise or organisation operations · CPC title

• G05B2219/23273

  Select, associate the real hardware to be used in the program · CPC title