Computer neural network supervisory process control system and method
DCFirst Claim
1. A computer neural network process control method adapted for predicting output data provided to a controller used to control a process for producing a product having at least one product property, said method allowing a control aim to be set without a human operator, the computer neural network process control method comprising the steps of:
- (1) operating the process with one or more sensors connected to sense process conditions and produce process condition measurements, and changing a controllable process state with an actuator,(2) controlling said actuator with a process controller in accordance with a process condition measurement from one or more of said sensors and in accordance with a setpoint;
(3) configuring the neural network by specifying at least one input, at least one output, at least one training input, and at least one specified interval;
(4) training the neural network to produce a trained neural network comprising the substeps of;
(a) retrieving a first raw training input data;
(b) retrieving a second raw training input data;
(c) computing a corresponding first training input data based on said first raw training input data and said second raw training input data, said first training input data indicative of the action of a human operator of the process;
(d) retrieving a first input data,(e) predicting a first output data using said first input data,(f) computing a first error data in accordance with said first training input data and said first output data, and(g) training the neural network to produce said trained neural network in accordance with said first error data;
(5) at said at least one specified interval, retrieving a second input data and predicting, with said trained neural network, second output data using said second input data; and
(6) retrieving said second output data for changing a setpoint of the controller for controlling the process.
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Abstract
A neural network for adjusting a setpoint in process control replaces a human operator. The neural network operates in three modes: training, operation, and retraining. In operation, the neural network is trained using training input data along with input data. The input data is from the sensor(s) monitoring the process. The input data is used by the neural network to develop output data. The training input data are the setpoint adjustments made by a human operator. The output data is compared with the training input data to produce error data, which is used to adjust the weights of the neural network so as to train it. After training has been completed, the neural network enters the operation mode. In this mode, the present invention uses the input data to predict output data used to adjust the setpoint supplied to the regulatory controller. Thus, the operator is effectively replaced. The present invention in the retraining mode utilizes new training input data to retrain the neural network by adjusting the weight(s).
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Citations
50 Claims
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1. A computer neural network process control method adapted for predicting output data provided to a controller used to control a process for producing a product having at least one product property, said method allowing a control aim to be set without a human operator, the computer neural network process control method comprising the steps of:
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(1) operating the process with one or more sensors connected to sense process conditions and produce process condition measurements, and changing a controllable process state with an actuator, (2) controlling said actuator with a process controller in accordance with a process condition measurement from one or more of said sensors and in accordance with a setpoint; (3) configuring the neural network by specifying at least one input, at least one output, at least one training input, and at least one specified interval; (4) training the neural network to produce a trained neural network comprising the substeps of; (a) retrieving a first raw training input data; (b) retrieving a second raw training input data; (c) computing a corresponding first training input data based on said first raw training input data and said second raw training input data, said first training input data indicative of the action of a human operator of the process; (d) retrieving a first input data, (e) predicting a first output data using said first input data, (f) computing a first error data in accordance with said first training input data and said first output data, and (g) training the neural network to produce said trained neural network in accordance with said first error data; (5) at said at least one specified interval, retrieving a second input data and predicting, with said trained neural network, second output data using said second input data; and (6) retrieving said second output data for changing a setpoint of the controller for controlling the process. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28)
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29. A computer neural network process control system adapted for predicting output data provided to control a setpoint of a controller used to control a process for producing a product having at least one product property, the computer neural network process control system comprising:
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(a) a neural network, responsive to a first input data indicative of sensor or aim or lab data, comprising, (1) training means, responsive to a first training input data indicative of the action of a human operator of the process, for training said neural network in accordance with a training set having said first input data and said first training input data to produce trained neural network weights, and (2) predicting means using said trained neural network weights for predicting the output data in accordance with a second input data indicative of sensor or aim or lab data; and (b) said controller, responsive to said predicting means, comprising, (1) sending means for adjusting said setpoint in accordance with the output data, and (2) setpoint adjustment means for accepting a change to said setpoint made by said human operator. - View Dependent Claims (30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42)
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43. A computer neural network process control system adapted for predicting output data provided to control a setpoint of a controller used to control a process for producing a product having at least one product property, the computer neural network process control system comprising:
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(a) a neural network, responsive to a first input data indicative of sensor or aim or lab data, comprising, (1) training means, responsive to a first training input data indicative of the action of a human operator of the process, for training said neural network in accordance with a first training set having said first input data and said first training input data to produce trained neural network weights, and (2) predicting means using said trained neural network weights for predicting the output data in accordance with a second input data indicative of sensor or aim or lab data; (b) said controller, responsive to said predicting means, comprising, (1) sending means for adjusting said setpoint in accordance with the output data; and (2) setpoint adjustment means for accepting a change to said setpoint made by said human operator; and (c) an historical database comprising, (1) storing means for storing said first training input data with an associated first timestamp, and for storing said first input data indicated by said associated first timestamp, and (2) retrieving means, responsive to said storing means, for retrieving said first training set comprising said first training input data and said first input data indicated by said associated first timestamp, and connected to provide said first training set to said training means. - View Dependent Claims (44, 45, 46, 47, 48)
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49. A computer neural network process control system adapted for predicting output data provided to control a setpoint of a controller used to control a process for producing a product having at least one product property, the computer neural network process control system comprising:
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(a) a neural network, responsive to a first input data indicative of sensor or aim or lab data, comprising, (1) predicting means using said trained neural network weights for predicting the output data in accordance with a second input data indicative of sensor or aim or lab data; and (b) said controller, responsive to said predicting means, comprising, (1) sending means for adjusting said setpoint in accordance with the output data, and (2) setpoint adjustment means for accepting a change to said setpoint made by aid human operator.
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50. A computer neural network process control system adapted for predicting output data provided to control a setpoint of a controller used to control a process for producing a product having at least one product property, the computer neural network process control system comprising:
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(a) a neural network, responsive to a first input data indicative of sensor or aim or lab data, comprising, (1) training means, responsive to a first training input data indicative of the action of a human operator of the process, for training said neural network in accordance with a training set having said first input data and said first training input data to produce trained neural network weights, and (2) predicting means using said trained neural network weights for predicting the output data in accordance with a second input data indicative of sensor or aim or lab data; and (b) a controller, responsive to said predicting means, comprising, (1) setpoint adjustment means for accepting a change to said setpoint made by said human operator.
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Specification