Process control of an industrial plant

• US 9,678,502 B2
• Filed: 04/16/2014
• Issued: 06/13/2017
• Est. Priority Date: 08/09/2006
• Status: Active Grant

• Current Assignee: Auckland UniServices Limited (Auckland University)
• Original Assignee: Auckland UniServices Limited (Auckland University)
• Inventors: Chen, John J. J., Taylor, Mark P.
• Primary Examiner(s): Kasenge, Charles
• Application Number:
  US14/254,480
• Publication Number:
  US 20140316579A1
• Time in Patent Office: 1,154 Days
• Field of Search: 700108, 700109, 700110, 700275, 702 81, 702 82, 702 83, 702 84, 702182, 702183, 702184, 702185, 706 12, 707802, 707E17005
• CPC Class Codes: C25C 3/06 of aluminium
• CPC Class Codes: C25C 3/20 Automatic control or regulation of cells controlling or regulating in general G05
• CPC Class Codes: G05B 11/01 electric
• CPC Class Codes: G05B 19/41835 characterised by programme execution
• CPC Class Codes: G05B 2219/31455 Monitor process status
• CPC Class Codes: G05B 2219/32009 Optimal task allocation between operator and machine
• CPC Class Codes: G05B 23/0294 Optimizing process, e.g. process efficiency, product quality

Abstract

A system 10 for controlling an industrial plant 12 comprises automatic control equipment 14 comprising a plurality of measurement sensors 16 for sensing predetermined variables associated with components of the industrial plant 12. The sensors 16 generate measured data relating to operation of the components of the industrial plant 12. A database 20 contains operational data, including observational data, regarding the industrial plant 12. A processor 18 is in communication with the automatic control equipment 14 and the database 20 for receiving the measured data from the sensors 16 of the automatic control equipment 14 and the operational data from the database 20. The processor 18 manipulates the measured and operational data to provide an evolving description of a process condition of each component over time, along with output information relating to operational control of the industrial plant 12 and for updating the database 20.

Claims

1. A system for controlling an industrial plant, the system comprising:

automatic control equipment comprising a plurality of measurement sensors for sensing predetermined variables associated with at least one component of the industrial plant, the sensors generating measured data relating to operation of the at least one component of the industrial plant;

a database containing the measured data and observational data obtained by plant personnel and relating to an operational state of the at least one component of the industrial plant; and

a processor in communication with the automatic control equipment and the database for receiving the measured data from the sensors of the automatic control equipment and the observational data from the database, the processor;

quantifying a level of disorder of the measured data;

computing a raster of the level of disorder of the measured data;

determining a state of the at least one component of the industrial plant based at least in part upon the raster of the level of disorder of the measured data;

controlling the automatic control equipment based on the determined state; and

integrating the measured data and the observational data to build a description of a process condition over time of the at least one component.

2. The system of claim 1 in which the automatic control equipment constitutes a first system level, the processor and database constitute a second system level with the system including a third system level, being a management level.

3. The system of claim 2 in which the management level uses the information output from the processor for effecting control of the industrial plant.

4. The system of claim 2 in which the levels are configured to reduce a range in a number of operating variables of the plant.

5. The system of claim 1 which includes a classifier module in communication with the processor for classifying variations of operating variables of the plant into one of a predetermined number of classes of variations, the classifier module classifying variations in a process variable into one of three classes being:

common cause or natural variation, special cause variation or structural variation.

6. The system of claim 1 in which, when the plant is an aluminium smelting plant, the automatic control equipment includes control inputs, wherein the control inputs comprise bath superheat sensors, bath resistivity sensors, sensors for monitoring and noting electrical current variation and characteristic frequencies, cell off-gas temperature and flow rate sensors, and the processor includes a causal framework for relating identified problems and cell process conditions to specific causes, the causal framework forming part of a learning algorithm of the processor which is improved and periodically updated over time using data from the database.

7. The system of claim 6 in which the observational data relates to an operational state of individual cells, the operational state being formally monitored and integrated into individual cell process conditions.

8. The system of claim 7 in which the processor and the database are operable to check the process condition for each cell individually with the database being updated periodically.

9. The system of claim 6 in which a management level employs causal trees containing the learning algorithm to provide a growing framework of decision support and, in the case of a smelting operation, cell diagnosis over time.

10. The system of claim 9 in which the processor further uses a complexity measure to assess predictability of process outcomes and the overall operation of the plant.

11. A method of controlling an industrial plant, the method comprising:

monitoring operation of at least one component of the industrial plant by a plurality of sensors forming part of automatic control equipment;

transferring measured data from the sensors and observational data relating to operation of the industrial plant to a processor, the observational data being obtained by plant personnel and relating to an operational state of the at least one component;

accessing a database containing the measured data from the sensors and the observational data relating to operation of the industrial plant, as periodically updated by the processor;

quantifying, automatically with the processor, a level of disorder of the measured data;

computing, automatically with the processor, a raster of the level of disorder of the measured data;

determining a state of the at least one component of the industrial plant, automatically with the processor, based at least in part upon the raster of the level of disorder of the measured data;

controlling the automatic control equipment based on the determined state; and

integrating, automatically with the processor, the measured data and the observational data to build a description of a process condition over time of the at least one component of the industrial plant.

12. The method of claim 11 which includes forming three system levels, the automatic control equipment constituting a first system level, the processor and database constituting a second system level and a third system level being a management level.

13. The method of claim 12 which includes using information output from the processor in the management level for effecting control of the industrial plant.

14. The method of claim 12 which includes configuring the levels to reduce a range in a number of operating variables of the plant rather than acting only to maintain the operating variables at arbitrary target levels.

15. The method of claim 14 in which the plant is an aluminium smelting plant and in which the method includes configuring the levels to achieve improvements in a number of operational aspects of the plant and using a causal framework to relate identified problems and process conditions to specific causes, integrating the causal framework into a learning algorithm of the processor which is improved and updated over time using data from the database and employing causal trees containing the learning algorithm to provide a growing framework of decision support and diagnosis of the at least one component over time.

16. The method of claim 15 which includes operating the plant within a range for each variable as determined by variability within a process carried out by the plant and which acts to reduce variation within each variable, key process variables, or both through identifying abnormal or systemic, damaging patterns of variation which can be related to a single dominant cause and correcting or minimising identified causes as appropriate, reducing the range of each process variable and improving process capability over time.

17. The method of claim 15 in which the observational data relates to an operational state of individual cells, the method including formally monitoring and integrating the operational state into individual cell process conditions.

18. The method of claim 11 which includes using a complexity measure to assess predictability of process outcomes and the overall operation of the plant.