Neural network process measurement and control
DCFirst Claim
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.
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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.
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Citations
41 Claims
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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:
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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. - View Dependent Claims (2, 3, 4, 5, 6)
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7. The computer neural network process control method of claim 7, further comprising the steps of:
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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. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A computer neural network process control method for controlling a process for producing having at least one product property, comprising the steps of:
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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. - View Dependent Claims (14)
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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:
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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. - View Dependent Claims (16, 17, 18, 19, 20)
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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:
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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. - View Dependent Claims (22)
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23. A computer neural network process control system for controlling a process for producing a product having at least one product property, comprising:
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(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. - View Dependent Claims (24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41)
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Specification