Fault diagnosis method for catalyzing and cracking main fractionator based on double-layer method

Abstract

The invention discloses a fault diagnosis method for catalyzing and cracking a main fractionator based on a double-layer method. A double-layer fault detection and diagnosis scheme for a rectificationprocess of the fractionator is provided, the rectification process is monitored by using a non-linear model which is developed at the early stage, a corresponding simplified linear model is established, and an abnormal source a measurement value of which has deviation is identified. According to a fault diagnosis algorithm, based on the linear model, a linear least square method is adopted for estimating fault parameters in rectification. A double-layer diagnosis structure can effectively capture the change of the fault parameters, the deviation is sensitively detected, the specific reason offaults can be judged from the change trend of the parameters, and the structure is more effective under comparison with a structure based on a pure non-linear model.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of a catalytic cracking main fractionation tower, in particular to a fault diagnosis method for a catalytic cracking main fractionation tower based on a double-layer method. Background technique [0002] The catalytic cracking unit is an important crude oil secondary processing unit, and the main fractionator is one of the most important equipment in the catalytic cracking process. The main task is to separate the rising oil-gas mixture according to its boiling point, and divide it into refined oil, heavy diesel oil, gasoline fraction, etc. from bottom to top. In the case of low processing capacity and separation efficiency, the failure of the main fractionating column will not only bring huge economic losses to the factory, but also bring some potential safety hazards to the society. [0003] There are mainly three methods in the field of fault diagnosis of chemical process: model-based,...

AI Technical Summary

Problems solved by technology

First, the simplified model derived from the general state-space process input-output data does not contain enough equations for the transport phenomena to express the physically meaningful parameters in the fractionator

Secondly, using a simplified model to directly replace the nonlinear model generation calculation is an effective fault diagnosis scheme, but it reduces the diagnostic performance

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