Inferential process modelling, quality prediction and fault detection using multi-stage data segregation

Abstract

A process modelling technique uses a single statistical model, such as a PLS, PRC, MLR, etc. model, developed from historical data for a typical process and uses this model to perform quality prediction or fault detection for various different process states of a process. Training data sets of various states of the process are stored and the training data divided into time slices. Mean and/or standard deviation values are determined for both the time slice parameters and variables and the training data. A set of deviations from the mean are determined for the time slice data and the model generated based on the set of deviations. The modeling technique determines means (and possibly standard deviations) of process parameters for each of a set of product grades, throughputs, etc., preferably compares on-line process parameter measurements to these means and use these comparisons in a single process model to perform quality prediction or fault detection across the various states of the process. Because only the means and standard deviations of the process parameters of the process model are updated, a single process model can be used to perform quality prediction or fault detection while the process is operating in any of the defined process stages or states. Moreover, the sensitivity (robustness) of the process model may be manually or automatically adjust each process parameter to tune or adapt the model over time. An alternative aspect is a method of displaying process alert information using a user interface having multiple screens.

Description

technical field [0001] This patent relates generally to process control system modeling, and more specifically to methods for performing process modeling, quality prediction, and fault detection in continuous or batch processing using multi-stage or multi-state data separation. Background technique [0002] Process control systems, such as those used in chemical, petroleum, or other processes, typically include one or more process controllers and communicatively coupled to at least one host computer or operator via an analog, digital, or combined analog / digital bus The workstation is coupled to an input / output (I / O) device of one or more field devices. Field devices, which can be, for example, valves, valve positioners, switches, and transmitters (eg, temperature, pressure, and flow rate sensors) that perform process control functions in a process, such as opening or closing valves and measuring process control parameters. Process controllers receive signals representing pr...

AI Technical Summary

Problems solved by technology

Unfortunately, these techniques are not adequate for continuous or batch processing where multiple grades of product may be produced (at different times) using one or more different plant equipment, Either have variable output, or where operating conditions change regularly

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