Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
1998-09-24
2002-09-17
Chaki, Kakali (Department: 2122)
Data processing: artificial intelligence
Neural network
Learning task
C702S024000
Reexamination Certificate
active
06453308
ABSTRACT:
I. FIELD OF THE INVENTION
The present invention pertains to a predictive device that models the dynamic input/output relationships of a physical process, particularly in the process industries such as hydrocarbons, polymers, pulp and paper, and utilities. The predictive device is primarily for multivariable process control, but is also applicable to dynamic process monitoring, or to provide a continuous stream of inferred measurements in place of costly or infrequent laboratory or analyzer measurements.
II. BACKGROUND OF THE INVENTION
Most existing industrial products designed for multivariable model predictive control (MPC) employ linear step-response models or finite impulse response (FIR) models. These approaches result in over-parameterization of the models (Qin and Badgwell, 1996). For example, the dynamics of a first order single input/single output SISO process which can be represented with only three parameters (gain, time constant and dead-time) in a parametric form typically require from 30 to 120 coefficients to describe in a step-response or FIR model. This over-parameterization problem is exacerbated for non-linear models since standard non-parametric approaches, such as Volterra series, lead to an exponential growth in the number of parameters to be identified. An alternative way to overcome these problems for non-linear systems is the use of parametric models such as input-output Nonlinear Auto-Regressive with eXogenous inputs (NARX). Though NARX models are found in many case-studies, a problem with NARX models using feed forward neural networks is that they offer only short-term predictions (Su, et al, 1992). MPC controllers require dynamic models capable of providing long-term predictions. Recurrent neural networks with internal or external feedback connections provide a better solution to the long-term prediction problem, but training such models is very difficult.
The approach described in (Graettinger, et al, 1994) and (Zhao, et al, 1997) provides a partial solution to this dilemma. The process model is identified based on a set of decoupled first order dynamic filters. The use of a group of first order dynamic filters in the input layer of the model enhances noise immunity by eliminating the output interaction found in NARX models. This structure circumvents the difficulty of training a recurrent neural network, while achieving good long-term predictions. However, using this structure to identify process responses that are second order or higher can result in over sensitive coefficients and in undesirable interactions between the first order filters. In addition, this approach usually results in an oversized model structure in order to achieve sufficient accuracy, and the model is not capable of modeling complex dynamics such as oscillatory effects. In the single input variable case, this first order structure is a special case of a more general nonlinear modeling approach described (Sentoni et al., 1996) that is proven to be able to approximate any discrete, causal, time invariant, nonlinear SISO process with fading memory. In this approach a Laguerre expansion creates a cascaded configuration of a low pass and several identical band pass first order filters. One of the problems of this approach is that may it require an excessively large degree of expansion to obtain sufficient accuracy. Also, it has not been known until now how to extend this methodology in a practical way to a multi-input system.
This invention addresses many essential issues for practical non-linear multivariable MPC. It provides the capability to accurately identify non-linear dynamic processes with a structure that
has close to minimum parameterization
can be practically identified with sufficient accuracy
makes good physical sense and allows incorporation of process knowledge
can be proven to identify a large class of practical processes
can provide the necessary information for process control
III SUMMARY OF THE INVENTION
The present invention is a dynamic predictive device that predicts or estimates values of process variables that are dynamically dependent on other measured process variables. This invention is especially suited to application in a model predictive control (MPC) system. The predictive device receives input data under the control of an external device controller. The predictive device operates in either configuration mode or one of three runtime modes—prediction mode, horizon mode, or reverse horizon mode.
The primary runtime mode is the prediction mode. In this mode, the input data are such as might be received from a distributed control system (DCS) as found in a manufacturing process. The device controller ensures that a contiguous stream of data from the DCS is provided to the predictive device at a synchronous discrete base sample time. The device controller operates the predictive device once per base sample time and receives the prediction from the output of the predictive device.
After the prediction mode output is available, the device controller can switch to horizon mode in the interval before the next base sample time. The predictive device can be operated many times during this interval and thus the device controller can conduct a series of experimental scenarios in which a sequence of input data can be specified by the device controller. The sequence of input data can be thought of as a data path the inputs will follow over a forward horizon. The sequence of predictions at the output of the controller is a predicted output path over a prediction horizon and is passed to the device controller for analysis, optimization, or control. The device controller informs the predictive device at the start of an experimental path and synchronizes the presentation of the path with the operation of the device. Internally, horizon mode operates exactly the same way as prediction mode, except that the dynamic states are maintained separately so that the predictive device can resume normal prediction mode operation at the next base sample time. In addition, the outputs of the filter units are buffered over the course of the path and are used during reverse horizon operation of the device.
The purpose of reverse horizon mode is to obtain the sensitivities of the predictive device to changes in an input path. Reverse horizon mode can only be set after horizon mode operation has occurred. The device controller first informs the predictive device the index of the point in the output path for which sensitivities are required. The device controller then synchronizes the reverse operation of the predictive device with the output of sensitivity data at the input paths of the device.
In forward operation, each input is scaled and shaped by a preprocessing unit before being passed to a corresponding delay unit which time-aligns data to resolve dead time effects such as pipeline transport delay. Modeling dead-times is an important issue for an MPC system. In practical MPC, prediction horizons are usually set large enough so that both dynamics and dead-time effects are taken into account; otherwise the optimal control path may be based on short term information, and the control behavior may become oscillatory or unstable. In the preferred embodiment, the predictive device is predicting a single measurement, and the dead-time units align data relative to the time of that measurement. If predictions at several measurement points are required, then several predictive devices are used in parallel. During configuration mode, the dead times are automatically estimated using training data collected from the plant. In the preferred embodiment the training method consists of constructing individual auto-regressive models between each input and the output at a variety of dead-times, and choosing the dead time corresponding to the best such model. As with other components of the invention, manual override of the automatic settings is possible and should be used if there is additional process knowledge that allows a more appropriate setting.
Each dead time unit feeds a dynamic filter unit.
Guiver John P.
Sentoni Guillermo
Zhao Hong
Aspen Technology Inc.
Chaki Kakali
Hamilton Brook Smith & Reynolds P.C.
Starks Wilbert
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