Abstract: A plant (72) is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) is provided that accurately models the plant (72). The output of the control network (74) provides a predicted output which is combined with a desired output to generate an error. This error is back propagated through an inverse control network (76), which is the inverse of the control network (74) to generate a control error signal that is input to a distributed control system (73) to vary the control inputs to the plant (72) in order to change the output y(t) to meet the desired output. The control network (74) is comprised of a first network NET 1 that is operable to store a representation of the dependency of the control variables on the state variables. The predicted result is subtracted from the actual state variable input and stored as a residual in a residual layer (102).

Abstract: A neural network system is provided that models the system in a system model (12) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (14). Input data is processed in a data preprocess step (10) to reconcile the data for input to the system model (12). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (20) which is utilized to control the output control (14). The output control (14) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (18) exceeds a predetermined decision threshold, input by a decision threshold block (22).

Abstract: A distributed control system (14) receives on the input thereof the control inputs and then outputs control signals to a plant (10) for the operation thereof. The measured variables of the plant and the control inputs are input to a predictive model (34) that operates in conjunction with an inverse model (36) to generate predicted control inputs. The predicted control inputs are processed through a filter (46) to apply hard constraints and sensitivity modifiers, the values of which are received from a control parameter block (22). During operation, the sensitivity of output variables on various input variables is determined. This information can be displayed and then the user allowed to select which of the input variables constitute the most sensitive input variables. These can then be utilized with a control network (470) to modify the predicted values of the input variables. Additionally, a neural network (406) can be trained on only the selected input variables that are determined to be the most sensitive.

Abstract: A neural network system is provided that models the system in a system model (12) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (14). Input data is processed in a data preprocess step (10) to reconcile the data for input to the system model (12). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (20) which is utilized to control the output control (14). The output control (14) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (18) exceeds a predetermined decision threshold, input by a decision threshold block (22).

Abstract: A distributed control system (14) receives on the input thereof the control inputs and then outputs control signals to a plant (10) for the operation thereof. The measured variables of the plant and the control inputs are input to a predictive model (34) that operates in conjunction with an inverse model (36) to generate predicted control inputs. The predicted control inputs are processed through a filter (46) to apply hard constraints, the values of which are received from a control parameter block (22). During operation, predetermined criterion stored in the control parameter block (22) are utilized by a cost minimization block (42) to generate an error control signal which is minimized by the inverse model (36) to generate the control signals. The system works in two modes, an analyze mode and a runtime mode. In the analyze mode, the predictive model (34) and the inverse model (36) are connected to either training data or simulated data from the analyzer (30) and the operation of the plant (10) evaluated.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: A neural network system is provided that models the system in a system model (12) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (14). Input data is processed in a data preprocess step (10) to reconcile the data for input to the system model (12). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (20) which is utilized to control the output control (14). The output control (14) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (18) exceeds a predetermined decision threshold, input by a decision threshold block (22).

Abstract: A plant (72) is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) is provided that accurately models the plant (72). The output of the control network (74) provides a predicted output which is combined with a desired output to generate an error. This error is back propagated through an inverse control network (76), which is the inverse of the control network (74) to generate a control error signal that is input to a distributed control system (73) to vary the control inputs to the plant (72) in order to change the output y(t) to meet the desired output. The control network (74) is comprised of a first network NET 1 that is operable to store a representation of the dependency of the control variables on the state variables. The predicted result is subtracted from the actual state variable input and stored as a residual in a residual layer (102).

Abstract: 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.

Abstract: 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).

Abstract: 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).

Abstract: A plant (72) is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) is provided that accurately models the plant (72). The output of the control network (74) provides a predicted output which is combined with a desired output to generate an error. This error is back propagated through an inverse control network (76), which is the inverse of the control network (74) to generate a control error signal that is input to a distributed control system (73) to vary the control inputs to the plant (72) in order to change the output y(t) to meet the desired output. The control network (74) is comprised of a first network NET 1 that is operable to store a representation of the dependency of the control variables on the state variables. The predicted result is subtracted from the actual state variable input and stored as a residual in a residual layer (102).