Abstract: A computer-implemented method for optimizing electronic content delivery for non-measurable users includes receiving a feature vector for each electronic content impression opportunity, receiving a feature vector for each delivered item of electronic content for measurable users, receiving an in-target indication for each delivered item of electronic content for measurable users, estimating a probability that an electronic content impression opportunity with a specified feature vector will meet targeting requirements based on the received feature vectors and the received in-target indications, receiving an in-target threshold value, generating an in-target rate control signal based on a number of total delivered items of electronic content for measurable users and a number of in-target delivered items of electronic content for measurable users, determining whether the estimated probability is greater than the in-target rate control signal, and generating conditions for delivering a new item of electronic cont

Abstract: The disclosed systems and methods provide a novel framework that provides mechanisms for predicting user actions of provided digital content based on an aggregation of user data. Conventional user tracking, and action prediction and recommendation systems have a lifespan that is ending in the short term due to new privacy laws. The disclosed framework enables personalized recommendations to be formulated for specific users based on an imputation from user data aggregated from a plurality of users. While anonymity is maintained, recommendations for predicted actions can be provided to the users and/or the providers of the content. The disclosed framework can scale the aggregated user data using a Naïve Bayes classifier, from which a logistic regression modeling can be performed to determine the predicted recommendation.

Abstract: One or more computing devices, systems, and/or methods for selecting content items for presentation via client devices are provided. A content event associated with a content item performed by a client device may be detected. The content item may be associated with an entity. A conversion event, associated with the entity, performed by the client device may be detected. A duration of time between the content event and the conversion event may be determined. An attribution score may be determined based upon the duration of time. A plurality of attribution scores, comprising the attribution score, may be stored in an attribution data structure associated with the content item. Responsive to receiving a request for content associated with a second client device, the content item may be selected from a plurality of content items for presentation via the second client device based upon the attribution data structure.

Abstract: One or more computing devices, systems, and/or methods for selecting content items for presentation via client devices are provided. A content event associated with a content item performed by a client device may be detected. The content item may be associated with an entity. A conversion event, associated with the entity, performed by the client device may be detected. A duration of time between the content event and the conversion event may be determined. An attribution score may be determined based upon the duration of time. A plurality of attribution scores, comprising the attribution score, may be stored in an attribution data structure associated with the content item. Responsive to receiving a request for content associated with a second client device, the content item may be selected from a plurality of content items for presentation via the second client device based upon the attribution data structure.

Abstract: In methods and systems that may facilitate detecting abnormal operation in a process plant, values of a process variable are analyzed to determine whether they significantly deviate from expected values. If there is a significant deviation, an indicator may be generated. Analyzing the process variable may include, for example, utilizing a plurality of thresholds determined based on statistics of the process variable. Analyzing the process variable may also include, for example, determining whether a first number of values of the process variable are in a first region, and whether a second number of values are in a second region.

Abstract: A system and method for detecting abnormal operation of a hydrocracker includes a hydrocracker model. The model may be configurable to include one or more regression models corresponding to different operating regions of the portion of the hydrocracker. The system and method may also determine if a monitored temperature difference variable deviates significantly from the temperature difference variable predicted by the model. If there is a significant deviation, this may indicate an abnormal operation.

Abstract: A system and method for detecting abnormal operation of a hydrocracker includes a model to model the hydrocracker. The model may be configurable to include one or more regression models corresponding to different operating regions of the portion of the hydrocracker. The system and method may also determine if a monitored temperature difference variable deviates significantly from the temperature difference variable predicted by the model. If there is a significant deviation, this may indicate an abnormal operation.

Abstract: A system and method for detecting abnormal operation of a hydrocracker includes a hydrocracker model. The model may be configurable to include one or more regression models corresponding to different operating regions of the portion of the hydrocracker. The system and method may also determine if a monitored temperature difference variable deviates significantly from the temperature difference variable predicted by the model. If there is a significant deviation, this may indicate an abnormal operation.

Abstract: In methods and systems that may facilitate detecting abnormal operation in a process plant, values of a process variable are analyzed to determine whether they significantly deviate from expected values. If there is a significant deviation, an indicator may be generated. Analyzing the process variable may include, for example, determining whether a number of values of the process variable are increasingly deviating from expected values, or determining whether the process variable is cycling. Analyzing the process variable may also include, for example, processing and analyzing the process variable to determine whether the process is in a normal state, one or more abnormal states, or an indeterminate state.

Abstract: A system for preventing abnormal situations in process plants is provided. A polynomial regression model is employed to predict values of a monitored variable based on measured samples of a load variable. An abnormal situation is detected when a predicted value of the monitored variable differs from a measured value of the monitored variable by more than a predetermined amount. The system employs one or more algorithms for automatically determining an optimal order or degree of the polynomial regression model.

Abstract: A method and system for detecting and/or predicting abnormal solids buildup in a main fractionator bottom of a fluid catalytic cracking system measures one or more process parameters of the fluid catalytic cracking system (such as a differential pressure across a reactor cyclone, a noise after the main fractionator bottom, a heat transfer at the steam generator, and/or a differential pressure across the main fractionator) and determines abnormal solids buildup when the measured process parameter(s) changes significantly from a baseline value. The method and system implements algorithms using computing devices to detect or predict an abnormal condition based on the change in the process parameter.

Abstract: A method and system for detecting and/or predicting abnormal levels of catalyst loss in a fluid catalytic cracking unit. The method and system measures a differential pressure across portions of a fluid catalytic cracker, such as a reactor cyclone or a regenerator cyclone, and determines abnormal catalyst loss when the differential pressure changes significantly from a baseline differential pressure. The claimed method and system implements algorithms using computing devices to detect or predict an abnormal condition based on the change in a monitored differential pressure in a fluid catalytic cracking unit.

Abstract: A system and method to facilitate the monitoring and diagnosis of a process control system and any elements thereof is disclosed with a specific premise of abnormal situation prevention in a coker heater of a coker unit in a product refining process. Monitoring and diagnosis of faults in a coker heater includes statistical analysis techniques, such as regression. In particular, on-line process data is collected from an operating coker heater in a coker area of a refinery. A statistical analysis is used to develop a regression model of the process. The output may use a variety of parameters from the model and may include normalized process variables based on the training data, and process variable limits or model components. Each of the outputs may be used to generate visualizations for process monitoring and diagnostics and perform alarm diagnostics to detect abnormal situations in the process.

Abstract: A system and method for detecting abnormal operation of a hydrocracker includes a model to model the hydrocracker. The model may be configurable to include one or more regression models corresponding to different operating regions of the portion of the hydrocracker. The system and method may also determine if a monitored temperature difference variable deviates significantly from the temperature difference variable predicted by the model. If there is a significant deviation, this may indicate an abnormal operation.

Abstract: In methods and systems that may facilitate detecting abnormal operation in a process plant, values of a process variable are analyzed to determine whether they significantly deviate from expected values. If there is a significant deviation, an indicator may be generated. Analyzing the process variable may include, for example, utilizing a plurality of thresholds determined based on statistics of the process variable. Analyzing the process variable may also include, for example, determining whether a first number of values of the process variable are in a first region, and whether a second number of values are in a second region.

Abstract: In methods and systems that may facilitate detecting abnormal operation in a process plant, values of a process variable are analyzed to determine whether they significantly deviate from expected values. If there is a significant deviation, an indicator may be generated. Analyzing the process variable may include, for example, determining whether a number of values of the process variable are increasingly deviating from expected values, or determining whether the process variable is cycling. Analyzing the process variable may also include, for example, processing and analyzing the process variable to determine whether the process is in a normal state, one or more abnormal states, or an indeterminate state.