Abstract: A method and system for estimating wax or hydrate deposits is desirable for the oil industry and important for assuring flow conditions and production, avoiding downtime, and reducing or preventing costly interventions. The method and system disclosed herein use artificial intelligence and machine learning techniques combined with oil well historical operational sensor data and historical operational event records (such as diesel hot flush, slick line, coil tubing, etc.) to build an oil well model. The method and system enable oil well practitioners to test and validate the built model and deploy the model online to estimate and/or detect wax or hydrate deposition status. By using one or more such models in operating an oil well, users can monitor and/or detect the status of wax of hydrate deposits in an oil well and can optimize production, maintenance, and planning for oil wells.

Abstract: Disclosed herein is an optical node comprising an FRU and a controller. The FRU comprises a module processor and a module memory storing a control plane application (CPA) executable by the module processor. The controller comprises an interface, a controller processor, and a controller memory storing instructions and an application that cause the controller processor to: instantiate a first network having a first client and a first server; register a plugin associated with the CPA with the first network; register an interface having a callback function and operable to communicate with the CPA via a second network; receive, by the interface via the second network, a request having a request property from the CPA; transmit the request, via the plugin, to the first client; receive a response via the first client from a remote node; and transmit, by the interface, the response to the module processor via the second network.

Abstract: Optical networks and nodes are described herein, including an optical network comprising a head-end node and a tail-end node. A line module of the head-end node receives fault information, generates a fault packet, and sends the fault packet to a first node controller identified by first packet forwarding information included in a packet header of the fault packet. The first node controller retrieves second packet forwarding information using the first packet forwarding information, updates the packet header, and sends the fault packet to the tail-end node identified by the second packet forwarding information. A second node controller of the tail-end node retrieves third packet forwarding information using the second packet forwarding information, updates the packet header, and sends the fault packet to an optical protection switching module (OPSM) of the tail-end node identified by the second packet forwarding information. The OPSM switches an optical switch based on the fault information.

Abstract: Embodiments control and optimize batch processes. An embodiment obtains and standardizes historical operating data from a plurality of batch production runs of an industrial process. For each batch production run, the standardized operating data corresponding to the batch is partitioned into one or more stages and one or more signature for each stage is determined using the partitioned standardized data. Each determined signature is associated with a class label based upon whether output of a batch run corresponding to the signature conforms or does not conform with operational standards. A model is trained, with at least a subset of the signatures as inputs and associated class labels as outputs, to predict, based on operating data from a real-world batch process, whether output of the process will conform or not conform with the operational standards. Online predictions can be automatically or manually applied to control and optimize a batch production run.

Abstract: Systems and methods for building predictive and prescriptive analytics of wind turbines generate a historical operational dataset by loading historical operational SCADA data of one or more wind turbines. Each sensor measurement is associated with an engineering tag and at least one component of a wind turbine. The system creates one or more performance indicators corresponding to one or more sensor measurements, and applies at least one data clustering algorithm onto the dataset to identify and label normal operation data clusters. The system builds a normal operation model using normal operational data clusters with Efficiency of Wind-To-Power (EWTP) and defines a statistical confidence range around the normal operation model as criterion for monitoring wind turbine performance. As real-time SCADA data is received by the system, the system can detect an anomalous event, and issue an alert notification and prescriptive early-action recommendations to a user, such as a turbine operator, technician or manager.

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 building predictive and prescriptive analytics of wind turbines generate a historical operational dataset by loading historical operational SCADA data of one or more wind turbines. Each sensor measurement is associated with an engineering tag and at least one component of a wind turbine. The system creates one or more performance indicators corresponding to one or more sensor measurements, and applies at least one data clustering algorithm onto the dataset to identify and label normal operation data clusters. The system builds a normal operation model using normal operational data clusters with Efficiency of Wind-To-Power (EWTP) and defines a statistical confidence range around the normal operation model as criterion for monitoring wind turbine performance. As real-time SCADA data is received by the system, the system can detect an anomalous event, and issue an alert notification and prescriptive early-action recommendations to a user, such as a turbine operator, technician or manager.

Abstract: An action recommendation system uses reinforcement learning that provides a next action recommendation to a traffic controller to give to a vehicle pilot such as an aircraft pilot. The action recommendation system uses data of past human actions to create a reinforcement learning model and then uses the reinforcement learning model with current ABS-B data to provide the next action recommendation to the traffic controller. The action recommendation system may use an anisotropic reward function and may also include an expanding state space module that uses a non-uniform granularity of the state space.

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: 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: An approach for creating dynamic content. The approach receives advertiser data associated with activities of one or more advertisers and receives publisher data associated with activities of one or more publishers. The approach manages the one or more DSPs activities associated with the received advertiser data and publisher data. Furthermore, the approach manages the one or more SSPs activities associated with the received advertiser data, publisher data and the one or more DSPs activities and selects one or more advertisement for one or more website. Finally, the approach manages the one or more consumer behaviors associated with the selected one or more advertisement.

Abstract: A method and system for estimating wax or hydrate deposits is desirable for the oil industry and important for assuring flow conditions and production, avoiding downtime, and reducing or preventing costly interventions. The method and system disclosed herein use artificial intelligence and machine learning techniques combined with oil well historical operational sensor data and historical operational event records (such as diesel hot flush, slick line, coil tubing, etc.) to build an oil well model. The method and system enable oil well practitioners to test and validate the built model and deploy the model online to estimate and/or detect wax or hydrate deposition status. By using one or more such models in operating an oil well, users can monitor and/or detect the status of wax of hydrate deposits in an oil well and can optimize production, maintenance, and planning for oil wells.

Abstract: Computer-implemented methods and systems 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. The methods and systems deploy the operation-centric model to calculate KPIs (Key Performance Indicators) using real-time measurements.

Abstract: An approach for creating dynamic content. The approach receives advertiser data associated with activities of one or more advertisers and receives publisher data associated with activities of one or more publishers. The approach manages the one or more DSPs activities associated with the received advertiser data and publisher data. Furthermore, the approach manages the one or more SSPs activities associated with the received advertiser data, publisher data and the one or more DSPs activities and selects one or more advertisement for one or more website. Finally, the approach manages the one or more consumer behaviors associated with the selected one or more advertisement.

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: System and methods that provide a new paradigm for solving process system engineering (PSE) problems using embedded artificial intelligence (AI) techniques. The approach can facilitate process model building and deployment and benefits from emerging AI and machine learning (ML) technology. The systems and methods can define PSE problems with mathematical equations, first principles and domain knowledges, and physical and economical constraints. The systems and methods generate a dataset of recorded measurements for variables of the process, and reduce the dataset by cleansing bad quality data segments and measurements for uninformative process variables from the dataset. The dataset is then enriched by, for example, applying nonlinear transforms, engineering calculations, and statistical measurements.

Abstract: An action recommendation system uses reinforcement learning that provides a next action recommendation to a traffic controller to give to a vehicle pilot such as an aircraft pilot. The action recommendation system uses data of past human actions to create a reinforcement learning model and then uses the reinforcement learning model with current ABS-B data to provide the next action recommendation to the traffic controller. The action recommendation system may use an anisotropic reward function and may also include an expanding state space module that uses a non-uniform granularity of the state space.

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: 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: 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.