A Hybrid Inverse Problem Approach to Model-Based Fault Diagnosis of a Distillation Column

Abstract

Early-stage fault detection and diagnosis of distillation has been considered an essential technique in the chemical industry. In this paper, fault diagnosis of a distillation column is formulated as an inverse problem. The nonlinear least squares algorithm is used to evaluate fault parameters embedded in a nonlinear dynamic model of distillation once abnormal symptoms are detected. A partial least squares regression model is built based on fault parameter history to explicitly predict the development of fault parameters. With the stripper of Tennessee Eastman process as example, this novel approach is tested for step- and random-type faults and several factors affecting its efficiency are discussed. The application result shows that the hybrid inverse problem approach gives the correct change of fault parameter at a speed far faster than the base approach with only a nonlinear model.

Special Issue on “Chemical Process Design, Simulation and Optimization”

“…Their method, called inside-out, divides the calculation into two loop iterations. Sun et al [5] study the problem of model-based fault diagnosis for a distillation column and, again, this study is strongly related to a numerical and algorithmic point of view. Although it might seem a particular case, their approach is general and can be used for different applications.…”

Actuator and Sensor Fault Detection and Isolation System Applied to a Distillation Column

“…The proposed algorithm was useful for detecting both internal and external faults in the distillation process. The fault diagnosis of a distillation column was formulated as an inverse problem in [41].…”

A Hybrid Intelligent Fault Diagnosis Strategy for Chemical Processes Based on Penalty Iterative Optimization

“…Among these methods, quantitative process history-based methods or data-driven methods possess the greatest potential for application in chemical processes [6]. A branch of data-driven methods relies on statistical measures such as principal component analysis (PCA) [7][8][9], independent component analysis (ICA) [10][11][12] and partial least squares (PLS) [13][14][15] for feature extraction and dimensionality reduction. The advantage of these methods is that they can simplify the analysis of complex dimensional data to improve efficiency.…”