Abstract: Systems and methods for utilizing compliance drivers to conserve system resources are provided. Data that corresponds to a pre-determined historical period may be used. A method may extract issue information, regulations data, operations loss data, drivers data, pending activities data and/or pending examinations data. The method may perform a plurality of transformations on the issue information, the drivers data, the regulations data, and the operations loss. The transformations may apply enterprise compliance hierarchy information and transform the data associated with the issue information, the drivers data, the regulations data, and the operations loss data into quarterly information. The method may include performing transformations on issue information, regulations data, operations loss data and drivers data. The method may perform final transformations in order to either allocate resources to remediate a compliance trend, remediate a compliance projection, or correct a current compliance issue.

Abstract: Embodiments include a computer-implemented method (and system) for performing automated batch data alignment for modeling, monitoring, and control of an industrial batch process. The method (and system) loads, scales, and screens plant historian batch data for an industrial batch process. The method (and system) selects a reference batch as basis of the batch alignment, defines and adds or modifies one or more batch phases, and selects one or more batch variables based on one or more profiles and corresponding curvatures of the batch data. The method (and system) estimates one or more weightings, adjust one or more tuning parameters and uses a sliding time window combined with DTW, DTI and GSS algorithms, performs the batch alignment in offline mode or online mode.

Abstract: Systems and methods for utilizing compliance drivers to conserve system resources are provided. Data that corresponds to a pre-determined historical period may be used. A method may extract issue information, regulations data, operations loss data, drivers data, pending activities data and/or pending examinations data. The method may perform a plurality of transformations on the issue information, the drivers data, the regulations data, and the operations loss. The transformations may apply enterprise compliance hierarchy information and transform the data associated with the issue information, the drivers data, the regulations data, and the operations loss data into quarterly information. The method may include performing transformations on issue information, regulations data, operations loss data and drivers data. The method may perform final transformations in order to either allocate resources to remediate a compliance trend, remediate a compliance projection, or correct a current compliance issue.

Abstract: Computer-based methods and systems perform root cause analysis with the construction of a probabilistic graph model (PGM) that explains the, e.g., negative, event dynamics of a processing plant, demonstrates precursor profiles for real-time monitoring, and provides probabilistic prediction of plant event occurrence based on real-time data. The methods and systems establish causal relationships between processing events in the upstream and resulting events in the downstream sensor data. The methods and systems provide early warnings for online process monitoring in order to prevent undesired events. The methods and systems successfully combine historical time series data with PGM analysis for operational diagnosis and prevention in order to identify the root cause of one or more events in the midst of multitude of continuously occurring events.

Abstract: Embodiments are directed to computer methods and systems that build and deploy a pattern model to detect an operating event in an online plant process. To build the pattern model, the methods and systems define a signature of the operating event, such that the defined signature contains a time series pattern for a KPI associated with the operating event. The methods and systems deploy the pattern model to automatically monitor, during online execution of the plant process, trends in movement of the KPI as a time series. The methods and systems determine, in real-time, a distance score between a range of the monitored time series and the time series pattern contained in the defined signature. The methods and systems automatically detect the operating event in the online industrial process based on the determined distance score, and alter parameters of the process (e.g., valves, actuators, etc.) to prevent the operating event.

Abstract: A system that provides an improved approach for detecting and predicting failures in a plant or equipment process. The approach may facilitate failure-model building and deployment from historical plant data of a formidable number of measurements. The system implements methods that generate a dataset containing recorded measurements for variables of the process. The methods reduce the dataset by cleansing bad quality data segments and measurements for uninformative process variables from the dataset. The methods then enrich the dataset by applying nonlinear transforms, engineering calculations and statistical measurements. The methods identify highly correlated input by performing a cross-correlation analysis on the cleansed and enriched dataset, and reduce the dataset by removing less-contributing input using a two-step feature selection procedure. The methods use the reduced dataset to build and train a failure model, which is deployed online to detect and predict failures in real-time plant operations.

Abstract: Embodiments are directed to computer methods and systems that construct a calibrated operation-centric first-principles model suitable for online deployment to monitor, predict, and control real-time plant operations. The methods and systems identify a plant-wide first-principles model configured for offline use and select a modeled operating unit contained in the plant-wide model. The methods and systems convert the plant-wide model to an operation-centric first-principles model of the selected modeled operating unit. The methods and systems recalibrate the operation-centric model to function using real-time measurements collected by physical instruments of the operating unit at the plant. The recalibration may include reconciling flow and temperature, estimating feed compositions, and tuning liquid and vapor traffic flow in the model.

Abstract: Methods and system are disclosed that manage behavior of a graphical user interface associated with an application during a runtime of the application. In one aspect, the graphical user interface (GUI) may be configured with attributes associated with the application by a GUI configuration manager. Upon determining application configuration information, a data field metadata manager may determine data fields to be mapped onto the GUI. The data field metadata manager may read the metadata information associated with the data fields that may include data field attributes and domain values. A GUI metadata manager may retrieve metadata information associated with the mapped data fields. A GUI runtime manager may manage the behavior of the GUI and the data received by the data fields may be saved in a data store in a data format associated with the application.

Abstract: Embodiments are directed to systems that build and deploy inferential models for generating predictions of a plant process. The systems select input variables and an output variable for the plant process. The systems load continuous measurements for the selected input variables. For the selected output variable, the systems load measurements of type: continuous from the subject plant process, intermittent from an online analyzer, or intermittent from lab data. If continuous or analyzer measurements are loaded, the systems build a FIR model with a subspace ID technique using continuous output measurements. From intermittent analyzer measurements, the systems generate continuous output measurements using interpolation. If lab data is loaded, the systems build a hybrid FIR model with subspace ID and PLS techniques, using continuous measurements of a reference variable correlated to the selected output variable.

Abstract: Computer-based methods and system perform root-cause analysis on an industrial process. A processor executes a hybrid first-principles and inferential model to generate KPIs for the industrial process using uploaded process data as variables. The processor selects a subset of the KPIs to represent an event occurring in the industrial process, and divides the selected data into time series. The system and methods select time intervals from the time series based on data variability and perform a cross-correlation between the loaded process variables and the selected time intervals, resulting in a cross-correlation score for each loaded process variable. Precursor candidates from the loaded process variables are selected based on the cross-correlation scores, and a strength of correlation score is obtained for each precursor candidate. The methods and system select root-cause variables from the selected precursor candidates based on the strength of correlation scores, and analyze the root-cause of the event.

Abstract: Computer-based methods and system perform root-cause analysis on an industrial process. A processor executes a hybrid first-principles and inferential model to generate KPIs for the industrial process using uploaded process data as variables. The processor selects a subset of the KPIs to represent an event occurring in the industrial process, and divides the selected data into time series. The system and methods select time intervals from the time series based on data variability and perform a cross-correlation between the loaded process variables and the selected time intervals, resulting in a cross-correlation score for each loaded process variable. Precursor candidates from the loaded process variables are selected based on the cross-correlation scores, and a strength of correlation score is obtained for each precursor candidate. The methods and system select root-cause variables from the selected precursor candidates based on the strength of correlation scores, and analyze the root-cause of the event.

Abstract: Methods and system are disclosed that manage behavior of a graphical user interface associated with an application during a runtime of the application. In one aspect, the graphical user interface (GUI) may be configured with attributes associated with the application by a GUI configuration manager. Upon determining application configuration information, a data field metadata manager may determine data fields to be mapped onto the GUI. The data field metadata manager may read the metadata information associated with the data fields that may include data field attributes and domain values. A GUI metadata manager may retrieve metadata information associated with the mapped data fields. A GUI runtime manager may manage the behavior of the GUI and the data received by the data fields may be saved in a data store in a data format associated with the application.

Abstract: Systems and methods for utilizing compliance drivers to conserve system resources are provided. Data that corresponds to a pre-determined historical period may be used. A method may extract issue information, regulations data, operations loss data, drivers data, pending activities data and/or pending examinations data. The method may perform a plurality of transformations on the issue information, the drivers data, the regulations data, and the operations loss. The transformations may apply enterprise compliance hierarchy information and transform the data associated with the issue information, the drivers data, the regulations data, and the operations loss data into quarterly information. The method may include performing transformations on issue information, regulations data, operations loss data and drivers data. The method may perform final transformations in order to either allocate resources to remediate a compliance trend, remediate a compliance projection, or correct a current compliance issue.

Abstract: The present invention is directed to computer-based methods and system to perform root-cause analysis on an industrial process. The methods and system load process data for an industrial process from a historian database and build a hybrid first-principles and inferential model. The methods and system then executes the hybrid model to generate KPIs for the industrial process using the loaded process variables. The methods and system then selects a subset of the KPIs to represent an event occurring in the industrial process, and divides the data for the subset into multiple subset of time series. The system and methods select time intervals from the time series based on the data variability in the selected time intervals and perform a cross-correlation between the loaded process variables and the selected time interval, resulting in a cross-correlation score for each loaded process variable.

Abstract: A system and method for adding and/or removing a software product to a distribution server is provided. Upon identification that a software product desired is not indexed in the installation server database of the distribution server, then adding the software product to the distribution server. The distribution server maintains multiple products, every product components of the multiple products is indexed by the installation server database of the distribution server. Removal of a software product requires removal of the software product from the index maintained by the installation server database of the distribution server.

Abstract: Carbon dioxide (152) is removed following compressor (130) which compresses a mixture of compressed air (132) and recirculated exhaust gas (118?) from a gas turbine (120). The carbon dioxide depleted gas (136) is humidified (180, 114) and fed to the combustor (110).

Abstract: A system and method for providing a database for distributing installation software. The database contains data separated into a user interface layer and an installation layer. The database has a hierarchical status and is associated with a location. Additional databases having their own respective data may be involved. An additional database will be identifiable by a hierarchical status. The hierarchical status of the database is superior to the hierarchical status of the additional database, so that the database is read before the additional database and the database indexes the additional database.

Abstract: A system and method for creation and installation of a software package containing multiple applications, products and/or parts thereof. The system and method involve identifying applications available on a server for distribution to at least one user workstation, selecting multiple applications, associating the multiple applications in a manner so as to form a software package, and installing the software package containing the multiple applications on the user workstation(s) as a result of a single request.

Abstract: A system and method for adding and/or removing a software product to a distribution server is provided. Upon identification that a software product desired is not indexed in the installation server database of the distribution server, then adding the software product to the distribution server. The distribution server maintains multiple products, every product components of the multiple products is indexed by the installation server database of the distribution server. Removal of a software product requires removal of the software product from the index maintained by the installation server database of the distribution server.

Abstract: A system and method is provided for providing customized input requests for installing products from a distribution server responsive to a request to install the product(s) on a workstation, wherein the customized input requests are determined based on the product(s). The method also includes installing the product(s) on the workstation based on a user input.