Abstract: A computer method and apparatus for automatic execution of software applications where the software applications are launched from a business terminology oriented workspace built using a business solutions framework. The framework includes the ability to define a workspace consisting of users, user types, business categories, business activities and business tasks. The workspace is presented by displaying associated business activities and the business tasks using business-oriented language and organization. A hierarchy of business categories, business activities and business tasks is also displayed in the workspace.

Abstract: A non-linear dynamic predictive device (60) is disclosed which operates either in a configuration mode or in one of three runtime modes: prediction mode, horizon mode, or reverse horizon mode. An external device controller (50) sets the mode and determines the data source and the frequency of data. In the forward modes (prediction and horizon), the data are passed to a series of preprocessing units (20) which convert each input variable (18) from engineering units to normalized units. Each preprocessing unit feeds a delay unit (22) that time-aligns the input to take into account dead time effects. The output of each delay unit is passed to a dynamic filter unit (24). Each dynamic filter unit internally utilizes one or more feedback paths that provide representations of the dynamic information in the process. The outputs (28) of the dynamic filter units are passed to a non-linear approximator (26) which outputs a value in normalized units.

Abstract: A non-linear dynamic predictive device (60) is disclosed which operates either in a configuration mode or in one of three runtime modes: prediction mode, horizon mode, or reverse horizon mode. An external device controller (50) sets the mode and determines the data source and the frequency of data. In prediction mode, the input data are such as might be received from a distributed control system (DCS) (10) 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. In prediction mode, the device controller operates the predictive device once per base sample time and receives the output from the predictive device through path (14). In horizon mode and reverse horizon mode, the device controller operates the predictive device additionally many times during base sample time interval. In horizon mode, additional data is provided through path (52).

Abstract: A multivariable process controller controls a chemical, polymer or other physical process. Slow tuning and over-conservative controlled variable values are employed during step testing. While all controlled process variables are within safe limits, only one manipulated variable (MV) at a time is step changed. Several manipulated variables are moved when process variables exceed safe limits to ensure that the controlled process variables return to the safe range, such that suitable MV targets for step testing are able to be automatically discovered within a closed loop control environment. Thus, the step test is able to be conducted mostly unsupervised and/or remotely via a telephone or network connection.

Abstract: A constrained non-linear approximator for empirical process control is disclosed. The approximator constrains the behavior of the derivative of a subject empirical model without adversely affecting the ability of the model to represent generic non-linear relationships. There are three stages to developing the constrained non-linear approximator. The first stage is the specification of the general shape of the gain trajectory or base non-linear function which is specified graphically, algebraically or generically and is used as the basis for transfer functions used in the second stage. The second stage of the invention is the interconnection of the transfer functions to allow non-linear approximation. The final stage of the invention is the constrained optimization of the model coefficients such that the general shape of the input/output mappings (and their corresponding derivatives) are conserved.

Abstract: A method and apparatus for steady-state target calculation that explicitly accounts for model uncertainty is disclosed. In accordance with one aspect of the invention, when model uncertainty is incorporated, the linear program associated with the steady-state target calculation can be recast as a highly structured nonlinear program. In accordance with another aspect of the invention, primal-dual interior point methods can be applied to take advantage of the resulting special structure. For a system having characteristic gain parameters G having a known uncertainty description, the present invention provides a method and apparatus for selecting steady-state targets for said system-manipulated variables such that all system-controlled variables will remain feasible at steady-state for all possible values of the parameters G within the known uncertainty description.

Abstract: The present invention provides a method and system for using standard global computer network communication protocol to implement server driven “push” technology. An initial communication connection is established between a server and a client. The connection is maintained by predefined periodic signals which are non-substantive messages from the server to the client. Upon existence of a substantive message at the server, the server transmits an appropriate predefined signal to the client. The client thereafter receives the substantive message. In one application, the server exchanges substantive messages between two clients and as such provides instant messaging.

Abstract: An apparatus and method is disclosed for detecting, identifying, and classifying faults occurring in sensors measuring a process. If faults are identified in one or more sensors, the apparatus and method provide replacement values for the faulty sensors so that any process controllers and process monitoring systems that use these sensors can remain in operation during the fault period. The identification of faulty sensors is achieved through the use of a set of structured residual transforms that are uniquely designed to be insensitive to specific subsets of sensors, while being maximally sensitive to sensors not in the subset. Identified faults are classified into one of the types Complete Failure, Bias, Drift, Precision Loss, or Unknown.

Abstract: A method and apparatus for steady-state target calculation that explicitly accounts for model uncertainty is disclosed. In accordance with one aspect of the invention, when model uncertainty is incorporated, the linear program associated with the steady-state target calculation can be recast as a highly structured nonlinear program. In accordance with another aspect of the invention, primal-dual interior point methods can be applied to take advantage of the resulting special structure. For a system having characteristic gain parameters G having a known uncertainty description, the present invention provides a method and apparatus for selecting steady-state targets for said system-manipulated variables such that all system-controlled variables will remain feasible at steady-state for all possible values of the parameters G within the known uncertainty description.

Abstract: A hybrid analyzer having a data derived primary analyzer and an error correction analyzer connected in parallel is disclosed. The primary analyzer, preferably a data derived linear model such as a partial least squares model, is trained using training data to generate major predictions of defined output variables. The error correction analyzer, preferably a neural network model is trained to capture the residuals between the primary analyzer outputs and the target process variables. The residuals generated by the error correction analyzer is summed with the output of the primary analyzer to compensate for the error residuals of the primary analyzer to arrive at a more accurate overall model of the target process. Additionally, an adaptive filter can be applied to the output of the primary analyzer to further capture the process dynamics. The data derived hybrid analyzer provides a readily adaptable framework to build the process model without requiring up-front knowledge.

Abstract: A first model or first analyzer having a series of filters is provided to represent time-varying effects of maintenance events. The first model or analyzer further enhances the selection of derived variables which are used as inputs to the first analyzer. Additionally, a combination of fuzzy logic and statistical regression analyzers are provided to better model the equipment and the maintenance process. An optimizer with a bi-modal optimization process which integrates discrete maintenance events with continuous process variables is also provided. The optimizer determines the time and the type of maintenance activities which are to be executed, as well as the extent to which the maintenance activities can be postponed by changing other process variables. Thus, potential modifications to process variables are determined to improve the current performance of the processing equipment as it drifts out of tolerance.

Abstract: A first model or first analyzer having a series of filters is provided to represent time-varying effects of maintenance events. The first model or analyzer further enhances the selection of derived variables which are used as inputs to the first analyzer. Additionally, a combination of fuzzy logic and statistical regression analyzers are provided to better model the equipment and the maintenance process. An optimizer with a bi-modal optimization process which integrates discrete maintenance events with continuous process variables is also provided. The optimizer determines the time and the type of maintenance activities which are to be executed, as well as the extent to which the maintenance activities can be postponed by changing other process variables. Thus, potential modifications to process variables are determined to improve the current performance of the processing equipment as it drifts out of tolerance.

Abstract: A process simulator and modeling methodology employs instantaneous property measures. The instantaneous measures of various polymer properties are tracked throughout the subject polymer manufacturing system and used to calculate respective property distribution functions. For example, property distributions of composition, molecular weight, stereoregularity and long chain branching are calculated, tracked in time and location throughout the manufacturing system, and used to model the polymer manufacturing system and polymerization process performed by the system. More specifically, the present invention calculates full distribution of polymer properties from the instantaneous property measures and tracked instantaneous property distributions. This enables accurate and computationally efficient modeling of the polymerization process and manufacturing system for carrying out the same.

Abstract: The present invention is directed to a method for simulating and optimizing a plant model. The plant model having a multiplicity of equipment models for a desired processing plant and a multiplicity of local property models for the material components within the plant includes providing a set of initial values for each property model in a data storage area. A first set of coefficients in each property model is determined. A set of equations representing the equipment models and property models, wherein the property models having a complementarity formulation, are executed simultaneously by the digital processor by using the first set of coefficients to determine a second set of coefficients in each property model. The second set of coefficients are stored in the data storage area.

Abstract: A hybrid analyzer having a data derived primary analyzer and an error correction analyzer connected in parallel is disclosed. The primary analyzer, preferably a data derived linear model such as a partial least squares model, is trained using training data to generate major predictions of defined output variables. The error correction analyzer, preferably a neural network model is trained to capture the residuals between the primary analyzer outputs and the target process variables. The residuals generated by the error correction analyzer is summed with the output of the primary analyzer to compensate for the error residuals of the primary analyzer to arrive at a more accurate overall model of the target process. Additionally, an adaptive filter can be applied to the output of the primary analyzer to further capture the process dynamics. The data derived hybrid analyzer provides a readily adaptable framework to build the process model without requiring up-front knowledge.

Abstract: The present invention provides a data selection apparatus which augments a set of training examples with the desired output data. The resulting augmented data set is normalized such that the augmented data values range between -1 and +1 and such that the mean of the augmented data set is zero. The data selection apparatus then groups the augmented and normalized data set into related clusters using a clusterizer. Preferably, the clusterizer is a neural network such as a Kohonen self-organizing map (SOM). The data selection apparatus further applies an extractor to cull a working set of data from the clusterized data set. The present invention thus picks, or filters, a set of data which is more nearly uniformly distributed across the portion of the input space of interest to minimize the maximum absolute error over the entire input space. The output of the data selection apparatus is provided to train the analyzer with important sub-sets of the training data rather than with all available training data.

Abstract: A flexible method for characterizing polymer components and the use of the same in general purpose polymer process simulation software has been developed. In this method and software apparatus, polymer molecules are defined in terms of their segments or structural units. In addition, a set of component attributes is associated with each polymer component. The component attributes are used to track information on polymer molecular structure, chemical composition and product properties, such as molecular weight averages, average copolymer composition, particle size distribution, etc. This methodology is used in a consistent manner by all key elements of simulation software, such as thermo-physical property calculations and polymerization kinetics.

Abstract: A software system simulates and optimizes a processing plant design. The software system includes a plurality of equipment models for simulating each piece of equipment in the processing plant design. A sequential modular simulation routine executes the equipment models in a first mode to define a first set of values of the operating parameters of the processing plant design. An optimization routine executes the equipment models in a second mode. The optimization routine utilizes the first set of values for the operating parameters from the sequential simulation routine and subsequently determines values of the operating parameters at which the processing plant design is optimized. The equipment models after execution by the sequential simulation routine and optimization routine store the first and second sets of values for the operating parameters in a common plant model file.