Neural network process measurement and control

• US 5,282,261 A
• Filed: 08/03/1990
• Issued: 01/25/1994
• Est. Priority Date: 08/03/1990
• Status: Expired due to Term

• Current Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Original Assignee: E. I. du Pont de Nemours and Company (Corteva, Inc.)
• Inventors: Skeirik, Richard D.
• Primary Examiner(s): MacDonald, Allen R.
• Application Number:
  US07/563,095
• Time in Patent Office: 1,271 Days
• Field of Search: 395/906, 395/68, 395/22, 395/23, 395/27, 395/26, 395/76, 395/24, 364/162, 364/184, 364/165, 364/164
• US Class Current: 706/23
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: Y10S 706/906 Process plant

Abstract

A computer neural network process measurement and control system and method uses real-time output data from a neural network to replace a sensor or laboratory input to a controller. The neural network can use readily available, inexpensive and reliable measurements from sensors as inputs, and produce predicted values of product properties as output data for input to the controller. The system and method overcome process deadtime, measurement deadtime, infrequent measurements, and measurement variability in laboratory data, thus providing improved control. An historical database can be used to provide a history of sensor and laboratory measurements to the neural network. The neural network can detect the appearance of new laboratory measurements in the history and automatically initiate retraining, on-line and in real-time. The system and method can use either a regulatory controller or a supervisory control architecture. A modular software implementation simplifies the building of multiple neural networks, and also optionally provides other control functions, such as supervisory controllers, expert systems, and statistical data filtering, thus allowing powerful extensions of the system and method. Template specification for the neural network, and data specification using data pointers allow the system and method to be more easily implemented.

Claims

1. A computer neural network process control method for controlling a process for producing a product having at least one product property, comprising the steps of:

operating the process with one or more sensors connected to sense process conditions and product at least one process condition measurement for each sensor;

predicting with a neural network first output data using said at least one process condition measurement as input data by summing at least two weighted inputs to an element of said neural network;

controlling an actuator with a supervisory and/or regulatory process controller by computing controller output data using said first output data as controller input data in place of a sensor input data and/or a product property input data; and

changing a controllable process state, using said actuator, in accordance with said controller output data.

2. The computer neural network process control method of claim 1, further comprising the steps:

detecting the presence of a new product property measurement;

retrieving input data associated in time with said new product property measurement;

predicting, using said neural network, second output data from said input data;

computing error data from said new product measurement and said second output data; and

enabling control of the process using a third output data when said error data is less than a metric.

3. The computer neural network process control method of claim 2, further comprising the step of using said new product property measurement as controller input data in place of said first output data when said error is equal to or greater than said metric.

4. The computer neural network process control method of claim 1, further comprising the steps of:

detecting either the completion of said predicting step by said neural network or the presence of new output data; and

initiating said controlling step upon detection by said detecting step.

5. The computer neural network process control method of claim 1, further comprising the steps of:

storing said at least one process condition measurement in an historical database with an associated timestamp; and

retrieving said at least one process condition measurement from said historical database for use by said predicting step.

6. The computer neural network process control method of claim 5, wherein said retrieving step precedes said predicting step.

7. The computer neural network process control method of claim 7, further comprising the steps of:

sampling the process and generating a product property measurement and a second associated timestamp;

storing said product property measurement in said historical database with said second associated timestamp; and

training the neural network by adjusting weights of said neural network in accordance with said product property measurement and said at least one process condition measurement.

8. The computer neural network process control method of claim 7, wherein said operating step is followed by said training step.

9. The computer neural network process control method of claim 7, further comprising the steps of:

detecting the presence of a second product property measurement having a third associated timestamp; and

retraining said neural network by repeating said training step when said second product property measurement is detected.

10. The computer neural network process control method of claim 9, wherein said detecting step further comprises the step of detecting the presence of said second product property measurement by comparing said second associated timestamp with said third associated timestamp.

11. The computer neural network process control method of claim 9, wherein said retraining step further comprises the step of stopping said controlling step when an error measure exceeds a metric.

12. The computer neural network process control method of claim 7, wherein said training step further comprises the steps of:

retrieving said product property measurement from said historical database with said second associated timestamp, as a first training input data,

selecting an associated timestamp value in accordance with said second associated timestamp and retrieving from said historical database, as a second input data, said at least one process condition measurement having said associated timestamp value; and

adjusting weights of said neural network in accordance with said first training input data and said second input data.

13. A computer neural network process control method for controlling a process for producing having at least one product property, comprising the steps of:

operating the process with one or more sensors connected to sense process conditions and produce at least one process condition measurement for each sensor;

storing said at least one process condition measurement in an historical database with at least one associated timestamp;

sampling the process and generating a product property measurement and a second associated timestamp; and

storing said product property measurement in said historical database with said second associated timestamp;

retrieving said at least one process condition measurement from said historical database for use by said predicting step;

running a modular neural network process control system, comprising the steps of;

running a module timing and sequencing means and independently triggering, in accordance with respective module timing specifications, a predicting submodule and a training submodule of a neural network module;

training a neural network, using said training submodule of said neural network module, when triggering by said module timing and sequencing means, by adjusting weights of said neural network in accordance with said product property measurement and said at least one process condition measurement; and

predicting, using said predicting submodule of said neural network module, first output data using said at least one process condition measurement as input data, when triggered by said module timing and sequencing means;

controlling an actuator with a supervisory and/or regulatory process controller by computer controller output data using said first output data as controller input data in place of a sensor input data and/or a product property input data; and

changing a controllable process state, using said actuator, in accordance with said controller output data.

14. The computer neural network process control method of claim 13, wherein said training step comprising the steps of:

retrieving said product property measurement from said historical database with said second associated timestamp, as a first training input data,

selecting an associated timestamp value in accordance with said second associated timestamp and retrieving from said historical database, as a second input data, said at least one process condition measurement having said associated timestamp value; and

adjusting weights of said neural network in accordance with said first training input data and said second input data.

15. A computer neural network process control method for controlling a process for producing a product having at least one product property, comprising the steps of:

operating the process with one or more sensors connected to sense process conditions and produce at least one process condition measurement for each sensor;

running a modular neural network process control system, comprising the steps of;

running a module timing and sequencing means and triggering, in accordance with module timing specifications, a neural network module; and

predicting, using said neural network module, first output data using said at least one process condition measurement as input data, when triggered by said module timing and sequencing means by summing at least two weighted inputs to an element of said neural network;

controlling an actuator with a supervisory and/or regulatory process controller by computing controller output data using said first output data as controller input data in place of a sensor input data and/or a product property input data; and

changing a controllable process sate, using said actuator, in accordance with said controller output data.

16. The computer neural network process control method of claim 15, further comprising the steps of:

storing said at least one process condition measurement in an historical database with an associated timestamp; and

retrieving said at least one process condition measurement from said historical database for use by said predicting step.

17. The computer neural network process control method of claim 16, wherein said predicting step is preceded by said retrieving step.

18. The computer neural network process control method of claim 15, further comprising the step of triggering, in accordance with respective module timing specifications, said supervisory and/or regulatory controller module;

and wherein said controlling step is triggered by said module timing and sequencing means.

19. The computer neural network process control method of claim 15, further comprising the steps of:

storing said at least one process condition measurement in an historical database with an associated timestamp; and

retrieving said at least one process condition measurement from said historical database for use by said predicting step.

20. The computer neural network process control method of claim 19, wherein said retrieving step precedes said predicting step.

21. A computer neural network process control method for controlling a process for producing a product having at least one product property, comprising the steps of:

operating the process with one or more sensors connected to sense process conditions and product at least one process condition measurement for each sensor;

storing said at least one process condition measurement in an historical database with an associated timestamp;

sampling the process and generating a product property measurement and a second associated timestamp;

storing said product property measurement in said historical database with said second associated timestamp;

retrieving said at least one process condition measurement from said historical database for use by said predicting step.

running a modular neural network process control system, comprising the steps of;

running a module timing and sequencing means and independently triggering, in accordance with respective module timing specifications, a predicting submodule and a training submodule of a neural network module and a supervisory and/or regulatory controller module;

training a neural network, using said training submodule of said neutral network module, when triggered by said module timing and sequencing means, by adjusting weights of said neural network in accordance with said product property measurement and said at least one process condition measurement;

predicting, using said predicting submodule of said neural network module, first output data using said at least one process condition measurement as input data, when triggered by said module timing and sequencing means; and

controlling an actuator by computing with said supervisory and/or regulatory controller module controller output data using said first output data as controller input data in place of a sensor input data and/or a product property input data, when triggered by said module timing and sequencing means; and

changing a controllable process state, using said actuator, in accordance with said controller output data.

22. The computer neural network process control method of claim 21, wherein said training step further comprising the steps of:

retrieving said product property measurement from said historical database with said second associated timestamp, as a first training input data,

selecting an associated timestamp value in accordance with said second associated timestamp and retrieving from said historical database, as a second input data, said at least one process condition measurement having said associated timestamp value; and

adjusting weights of said neural network in accordance with said first training input data and said second input data.

23. A computer neural network process control system for controlling a process for producing a product having at least one product property, comprising:

(a) a sensor, for generating a process condition measurement;

(b) a neural network having predicting means for predicting first output data in accordance with input data;

(c) connection means for providing said process condition measurement to said predicting means for use as said input data;

(d) a supervisory and/or regulatory controller for computing a controller output data in accordance with a controller input data, connected to use said first output data as said controller input data in place of a sensor input data and/or a product property input data; and

(e) an actuator, connected to use said controller output data, for changing a controllable process state in accordance with said controller output data.

24. The computer neural process control system of claim 23, wherein said connection means comprises an historical database for storing and providing said process condition measurement with an associated time stamp.

25. The computer neural process control system of claim 24, further comprising:

laboratory means, for generating a product property measurement;

wherein said historical database is further connected to store and provide said product property measurement with an associated time stamp; and

wherein said neural network further comprises training means, connected to use said product property measurement provided by said historical database as training input data, for adjusting weights of said neural network.

26. The computer neural network process control system of claim 23, wherein said neural network comprises a modular neural network process control system, said modular neural network process control system comprising:

at least one module having at least one neural network module containing said predicting means;

module timing and sequencing means, responsive to module data specifications, having triggering means for initiating predicting by said predicting means of said neural network module.

27. The computer neural network process control system of claim 26, wherein said at least one module further comprises at least one controller module, comprising said supervisory and/or regulatory controller;

and wherein said triggering means further functions for independently initiating controlling by said supervisory and/or regulatory controller module.

28. The computer neural network process control system of claim 27, wherein said controller module comprises a feedback control module supervising a regulatory controller.

29. The computer neural network process control system of claim 27, wherein said controllable process state directly or indirectly affects the product property;

and wherein said at least one module further comprises a feedforward control module connected to directly or indirectly control the process by changing said controllable process state or a second controllable process state affecting the product property.

30. The computer neural network process control system of claim 27, wherein said controllable process state directly or indirectly affects the product property;

and wherein said at least one module further comprises a statistical test module, connected to provide statistical data for directly or indirectly controlling the process by changing said controllable process state or a second controllable process state affecting the product property.

31. The computer neural network process control system of claim 26, wherein said connection means comprises an historical database, for storing and providing said process condition measurement with an associated timestamp.

32. The computer neural network process control system of claim 27, wherein said at least one module further comprises at least one controller module having said supervisory and/or regulatory controller;

and wherein said triggering means further functions for independently initiating controlling by said at least one controller module.

33. The computer neural network process control system of claim 31, further comprising:

laboratory means for generating a product property measurement;

wherein said historical database is further connected to store and provide said product property measurement with an associated timestamp;

wherein said neural network further comprises training means, connected to use said product property measurement provided by said historical database as training input data, for training said at least one neural network module; and

wherein said triggering means further functions for independently initiating training by said training means.

34. The computer neural network process control system of claim 33, wherein said at least one module further comprises at least one controller module having said supervisory and/or regulatory controller;

and wherein said triggering means further functions for independently initiating controlling by said at least one controller module.

35. The computer neural network process control system of claim 26, further comprising a user interface providing a template for entering a size specification of said neural network module and/or a connectivity specification of said neural network module and/or a specification of a source of said input data, wherein said neural network module operates in accordance with said specification(s).

36. The computer neural network process control system of claim 35, wherein said template comprises data pointers for specifying data to be used by said neural network module.

37. The computer neural network process control system of claim 26, further comprising a user interface for configuring said neural network module using a limited set of natural language format specifications.

38. The computer neural network process control system of claim 26, wherein each of said at least one module(s) further comprises:

first storage means for storing module timing and sequencing specifications;

second storage means for storing a pointer to one of a limited set of standard module procedures; and

third storage means for storing parameters for limiting the functions of said standard module procedures.

39. The computer neural network process control system of claim 23, wherein said neural network comprises a software system funning on a digital computer.

40. The computer neural network process control system of claim 23, wherein said neural network comprises a dedicated neural network integrated circuit.

41. The computer neural network process control system of claim 23, wherein said neural network comprises an analog neural network.