Abstract: A furnace is monitored by monitoring a statistical variable calculated from a draft measurement and monitoring a process variable related to the operation of the furnace. An abnormal operation of the furnace is determined based upon the statistical variable and the process variable.

Abstract: A sensing system includes a filter construction module that constructs a high pass filter for filtering sensor values indicative of a process variable, the filter construction module setting values for parameters of the filter based on a temperature value indicative of a temperature of the sensor that produced the sensor values.

Abstract: A system for detecting when a plunger reaches a bottom of a well includes a pressure sensor configured to measure a pressure of the well and provide a measured pressure output. Derivative calculation circuitry calculates a derivative of the measured pressure output. Detection circuitry detects when the plunger reaches the bottom of the well based upon the calculated derivative.

Abstract: A system for detecting when a plunger reaches a bottom of a well includes a pressure sensor configured to measure a pressure of the well and provide a measured pressure output. Derivative calculation circuitry calculates a derivative of the measured pressure output. Detection circuitry detects when the plunger reaches the bottom of the well based upon the calculated derivative.

Abstract: A sensing system includes a filter construction module that constructs a high pass filter for filtering sensor values indicative of a process variable, the filter construction module setting values for parameters of the filter based on a temperature value indicative of a temperature of the sensor that produced the sensor values.

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 heat exchanger. Monitoring and diagnosis of faults in a heat exchanger includes statistical analysis techniques, such as regression and load following. In particular, on-line process data is collected from an operating heat exchanger. 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 heat exchanger.

Abstract: A furnace is monitored by monitoring a statistical variable calculated from a draft measurement and monitoring a process variable related to the operation of the furnace. An abnormal operation of the furnace is determined based upon the statistical variable and the process variable.

Abstract: A digital filter design algorithm is implemented directly within a process control field device or other process related equipment. Filter design parameters are exposed so that filter design parameter values may be provided to the digital filter design algorithm so that the digital filter design algorithm may calculate digital filter coefficients for a digital filter having desired frequency response characteristics. The digital filter design parameter values may be provided by a user, or may be provided as process variable data output from a process control field device or other process related equipment. Once the coefficients of the digital filter having the desired frequency response characteristics have been calculated, the digital filter may be applied to process variable data received by the process control field device or other process related equipment.

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 method comprises sensing a differential pressure signal, filtering the differential pressure signal, generating a flooding indicator as a function of the filtered differential pressure signal, and indicating the onset of flooding. The differential pressure signal is sensed along a distillation flow path. The filtered differential pressure signal is responsive to a phase inversion along the flow path, and the flooding indicator is responsive to an onset of a flooding condition, based on the phase inversion. The onset of the flooding condition is indicated based on a change in the flooding indicator.

Abstract: Systems and methods are provided for detecting abnormal conditions and preventing abnormal situations from occurring in controlled processes. Statistical signatures of a monitored variable are modeled as a function of the statistical signatures of a load variable. The statistical signatures of the monitored variable may be modeled according to an extensible regression model or a simplified load following algorithm. The systems and methods may be advantageously applied to detect plugged impulse lines in a differential pressure flow measuring device.

Abstract: In a method for generating a model for modeling at least a portion of the process plant, M process variable data sets, where M is an integer, may be used to determine statistical data that may be utilized to scale process variable data sets. The M process variable data sets are scaled and then utilized to calculate intermediate model terms. For each additional process variable data set, it is scaled using the statistical data and then utilized to update the intermediate model terms. When an adequate number of process variable data sets have been processed, the model may be calculated using the intermediate model terms.

Abstract: A method and system for monitoring a process in a process plant includes collecting data from a process control system within the process plant, where the data is representative of a normal operation of the process when the process is on-line and operating normally, performing a multivariate statistical analysis to represent the normal operation of the process based on a set of collected on-line process data comprising a measure of the normal operation of the process when the process is on-line and operating normally, and representing a real-time on-line operation of the process using monitored on-line process data as an input to the representation of the normal operation of the process, where the monitored on-line process data comprises a measure of a real-time operation to the process when the process is on-line.

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 for visually presenting data receives signal processing data generated signal processing data collection blocks corresponding to devices associated with a process plant. The signal processing data collection blocks may generate data such as statistical data, frequency analysis data, auto regression data, wavelets data, etc. The system displays an image representative of the devices and representative of a context of the devices within the process plant. Additionally, data based on signal processing data corresponding to one or more devices is displayed. For example, the signal processing data for the device could be displayed. As another example, data may be generated based on the signal processing data and this generated data may be displayed.

Abstract: Systems and methods are provided for detecting abnormal conditions and preventing abnormal situations from occurring in controlled processes. Statistical signatures of a monitored variable are modeled as a function of the statistical signatures of a load variable. The statistical signatures of the monitored variable may be modeled according to an extensible regression model or a simplified load following algorithm. The systems and methods may be advantageously applied to detect plugged impulse lines in a differential pressure flow measuring device.

Abstract: A system and method of monitoring and diagnosing on-line multivariate process variable data in a process plant, where the multivariate process data comprises a plurality of process variables each having a plurality of observations, includes collecting on-line process data from a process control system within the process plant when the process is on-line, where the collected on-line process data comprises a plurality of observations of a plurality of process variables and where the plurality of observations of the set of collected process data comprises a first data space having a plurality of dimensions, performing a multivariate statistical analysis to represent the operation of the process based on a set of collected on-line process data comprising a measure of the operation of the process when the process is on-line within a second data space having fewer dimensions than the first data space, performing a univariate analysis to represent the operation of the process as a multivariate projection of the on-l

Abstract: Detection of one or more abnormal situations is performed using various statistical measures, such as a mean, a median, a standard deviation, etc. of one or more process parameters or variable measurements made by statistical process monitoring blocks within a plant. This detection may include determination of the health and performance of one or more heat exchangers in the plant, and in particular, detection of a fouling condition of the one or more heat exchangers. Among the statistical measures, the detection may include calculation of an overall thermal resistance of the heat exchanger, which may be indicative under certain circumstances of heat exchanger performance and in particularly degradation of heat exchanger performance as a result of heat exchanger fouling.

Abstract: A method comprises sensing a differential pressure signal, filtering the differential pressure signal, generating a flooding indicator as a function of the filtered differential pressure signal, and indicating the onset of flooding. The differential pressure signal is sensed along a distillation flow path. The filtered differential pressure signal is responsive to a phase inversion along the flow path, and the flooding indicator is responsive to an onset of a flooding condition, based on the phase inversion. The onset of the flooding condition is indicated based on a change in the flooding indicator.

Abstract: A system and method for monitoring a process in a process plant and detecting an abnormal condition includes collecting data representative of the operation of the process, performing a multivariate statistical analysis to represent the operation of the process in a known state based on a set of collected reference data, where the reference data includes a statistical measure of the operation of the process in the known state. The system and method may further include representing the operation of the process in an unknown state based on a set of monitored data, where the monitored data includes a statistical measure of the operation of the process in an unknown state, and using the output of the multivariate statistical analysis as an input, and comparing the process in the unknown state to the multivariate statistical representation of the operation of the process in the known state to determine the operational state of the process.