Pavilion Technologies, Inc.

Other

Devendra B Godbole

James David Keeler

Steven A O'Hara

Eric J Hartman

Jill L Kempf

John P Havener

Joshua Brennan Ellinger

Ralph Bruce Ferguson

Laura Arbila

Audrey Strauss

R Bruce Ferguson, Ii

Jitendra W Telang

John Krauskop

Steve Piche

Mary Anne Harding

Ralph Bruce Ferguson, Ii

A method for determining an output value having a known relationship to an input value with a predicted value includes the step of first training a predictive model with at least one output for a given set of inputs that exist in a finite dataset. Data is then input to the predictive model that is within the set of given inputs. Thereafter, a prediction is made of an output from the predictive model that corresponds to the given input such that a predicted output value will be obtained which will have associated therewith the errors of the predictive model.

Automated method for building a model

A method for building a model of a system includes first extracting data from a historical database (,). Once the data is extracted, a dataset is then created, which dataset involves the steps of preprocessing the data. This dataset is then utilized to build a model. The model is defined as a plurality of transforms which can be utilized to run an on-line model. This on-line model is interfaced with the historical database such that the variable names associated therewith can be downloaded to the historical database. This historical database can then be interfaced with a control system to either directly operate the plant or to provide an operator an interface to various predicted data about the plant. The building operation will create the transform list and then a configuration step is performed in order to configure the model to interface with the historical database. When the dataset was extracted, it is unknown whether the variables names are still valid. It is therefore necessary to read and write the various variables to the database to determine if they are in fact valid. Further, the predicted output values, which may not have been a part of the historical database, need to be verified. Once these are verified, then the on-line model can be created to generate the predicted value for transfer to the control system. During this period of time, the control system must be disabled.

A predictive network is disclosed for operating in a runtime mode and in a training mode. The network includes a preprocessor (34') for preprocessing input data in accordance with parameters stored in a storage device (14') for output as preprocessed data to a delay device (36'). The delay device (36') provides a predetermined amount of delay as defined by predetermined delay settings in a storage device (18). The delayed data is input to a system model (26') which is operable in a training mode or a runtime mode. In the training mode, training data is stored in a data file (10) and retrieved therefrom for preprocessing and delay and then input to the system model (26'). Model parameters are learned and then stored in the storage device (22). During the training mode, the preprocess parameters are defined and stored in a storage device (14) in a particular sequence and delay settings are determined in the storage device (18). During the runtime mode, runtime data is derived from a distributed control system (24) and then preprocessed in accordance with predetermined process parameters and delayed in accordance with the predetermined delay settings. The preprocessed data is then input to the system model (26') to provide a predicted output, which is a control output to the distributed control system (24).

Predictive network with learned preprocessing parameters

Predictive network with graphically determined preprocess transforms

An automatic data flow architecture builder is disclosed that is operable to take raw data which is stored in a raw data buffer (60) and transform it for storage in a transformed data buffer (78). A plurality of transform blocks (68), (70) and (72) are provided for this transform operation, these disposed in a predetermined data flow. When the user inputs a new transform to be disposed within the data flow of the transforms, rules are applied via a rule-base processing system (74) to apply a set of predetermined rules in a rule database (76) to the transform. These rules determine where the transform is to be inserted. This provides an automatic construction operation of a data flow architecture.

Method and apparatus for automatically constructing a data flow

A preprocessing system for preprocessing input data to a neural network includes a training system for training a model (20) on data from a data file (10). The data is first preprocessed in a preprocessor (12) to fill in bad or missing data and merge all the time values on a common time scale. The preprocess operation utilizes preprocessing algorithms and time merging algorithms which are stored in a storage area (14). The output of the preprocessor (12) is then delayed in a delay block (16) in accordance with delay settings in storage area (18). These delayed outputs are then utilized to train the model (20), the model parameter is then stored in a storage area (22) during run time, a distributed control system (24) outputs the data to a preprocess block (34) and then preprocesses data in accordance with the algorithms in storage area (14). These outputs are then delayed in accordance with a delay block (36) with the delay settings (18). The output of the delay block (36) comprises inputs to a run time system model (26) which is built to provide a representation of the system in accordance with the model parameters in the storage area (22). A predicted control output or predicted control inputs are then generated. The control input is input back to the DCS (24).

Method and apparatus for preprocessing input data to a neural network

An internal combustion engine (360) is provided with a plurality of sensors to monitor the operation thereof with respect to various temperature measurements, pressure measurements, etc. A predictive model processor (322) is provided that utilizes model parameters stored in the memory (324) to predict from the sensor inputs a predicted emissions output. The model is trained with inputs provided by the sensor and an actual emissions sensor output. During operation, this predicted output on line (326) can be utilized to provide an alarm or to be stored in a history database in a memory (328). Additionally, the internal combustion engine (260) can have the predicted emissions output thereof periodically checked to determine the accuracy of the model. This is effected by connecting the output of the engine to an external emissions sensor (310) and taking the difference between the actual output and the predicted output to provide an error. This is compared to a threshold and, if the error exceeds the threshold, the predictive model processor (322) can be placed in a training operation wherein new model parameters are generated. Additionally, retraining could take place external to the predictive model processor (322). During runtime, a control network (350) can be utilized to predict control parameters for minimizing the emissions output.

Virtual emissions monitor for automobile and associated control system

A continuous emission monitoring system for a manufacturing plant (10) includes a control system (16) which has associated therewith a virtual sensor network (18). The network (18) is a predictive network that receives as inputs both control values to the plant (10) and also sensor values. The network (18) is then operable to map the inputs through a stored representation of the plant (10) to output a predicted pollutant sensor level. This predicted pollutant sensor level is essentially the prediction of an actual pollutant sensor level that can be measured by a pollutant sensor (14). The network (18) therefore is a substitute for the pollutant sensor (14), thus providing a virtual sensor. The sensor values from the plant (10) are first processed through a sensor validation system (22). The sensor validation system (22) is operable to determine which of the sensors falls outside of acceptable limits and then substitutes therefor a predicted output, such that the inputs to the virtual sensor network (18) are within acceptable limits.

Virtual continuous emission monitoring system

An internal combustion engine [(360)] is provided with a plurality of sensors to monitor the operation thereof with respect to various temperature measurements, pressure measurements, etc. A predictive model processor [(322)] is provided that utilizes model parameters stored in the memory [(324)] to predict from the sensor inputs a predicted emissions output. The model is trained with inputs provided by the sensor and an actual emissions sensor output. During operation, this predicted output on line [(326)] can be utilized to provide an alarm or to be stored in a history database in a memory [(328)]. Additionally, the internal combustion engine [(260)] can have the predicted emissions output thereof periodically checked to determine the accuracy of the model. This is effected by connecting the output of the engine to an external emissions sensor [(310)] and taking the difference between the actual output and the predicted output to provide an error. This is compared to a threshold and, if the error exceeds the threshold, the predictive model processor [(322)] can be placed in a training operation wherein new model parameters are generated. Additionally, retraining could take place external to the predictive model processor [(322)]. During runtime, a control network [(350)] can be utilized to predict control parameters for minimizing the emissions output.

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Austin, TX, United States of America

Devendra B Godbole