Honeywell International Inc.

Roman Navratil

Karel Marik

Petr Stluka

Wendy Foslien Graber

A system and method include determining a state of a batch process. Historical segments are retrieved from a historical database of trajectories of the batch process as a function of the state of the batch process. A model is created as a function of the retrieved historical segments. The model is used to provide state information about the batch process and may then be discarded.

Batch process monitoring using local multivariate trajectories

In an embodiment, a system and method are described that enable a display of a parallel coordinate plot. Such a parallel coordinate plot includes a plurality of parallel axes, and each of the parallel axes represents a variable. The display further includes one or more polygonal lines connecting values of the variables of the parallel axes. The system and method further enable a display of information about an additional variable on the parallel coordinate plot by varying the property of the one or more polygonal lines.

Process monitoring using multivariate data

Deviations associated with a process system are identified, such as by identifying deviations of process variables or production measurements from target values. A graphical display is generated that identifies at least some of the deviations and one or more characteristics associated with the deviations. For example, the graphical display includes a time axis, and each deviation is associated with a bar positioned in the graphical display to identify a start time and an end time of the deviation along the time axis. Also, each bar may include one or more indicators (such as patterns, colors, or shadings) that identify the one or more characteristics (such as an economic impact and/or a cause) of each deviation identified in the graphical display. In this way, a user can, among other things, identify when deviations from target values occur, durations of the deviations, and economic impacts and/or causes associated with the deviations.

Apparatus and method for visualization of operating status in a process control system

In a process control system, the hidden properties of a catalyst are estimated by including those properties in hidden states within a state space model and solving the state space model based on measurable inputs and outputs of the process. The process may include defining a state space model for a process having a catalyst state comprising a hidden catalyst property; defining a set of empirically measurable input variables for the state space model, defining a set of output variables for the state space model, measuring a set of input values corresponding to the set of input variables; measuring a set of output values corresponding to the set of output variables; and estimating the hidden catalyst property based on the input values, the output values, and the state space model.

Methods and apparatus for process control using catalyst state estimation

Methods, computer program products, and systems adapted to aid in data analysis by graphically displaying current situation on the background of historical data density, Historical data density is represented by one or more contour plots. An indicator of how a current data point compares to the historical data may be placed on one or more of the data density graphs. In some examples, data falling outside a threshold contour of historical data density may represent a new or an abnormal situation, and such data and/or a contour plot having such data may be highlighted in a graphical display.

Diagnostic visual tools and methods for graphical comparison of data point and historical data density

A system and method for monitoring and fault detection in dynamic systems. A 'cross-covariance' matrix is used to construct and implement a principle component analysis (PCA) model and/or partial least squares (PLS) model. This system is further utilized for monitoring and detecting faults in a dynamic system. Time series information is synchronized, with respect to a set of training data. Based on historical data, consistency of correlations between variables can be checked with respect to a given time stamp.

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Prague, Czechia

Roman Navratil