A Hybrid Process Monitoring and Fault Diagnosis Approach for Chemical Plants

Lijie Guo,Jianxin Kang

doi:10.1155/2015/864782

Abstract

Given their potentially enormous risk, process monitoring and fault diagnosis for chemical plants have recently been the focus of many studies. Based on hazard and operability (HAZOP) analysis, kernel principal component analysis (KPCA), wavelet neural network (WNN), and fault tree analysis (FTA), a hybrid process monitoring and fault diagnosis approach is proposed in this study. HAZOP analysis helps identify the fault modes and determine process variables monitored. The KPCA model is then constructed to reduce monitoring variable dimensionality. Meanwhile, the fault features of the monitoring variables are extracted, so then process monitoring can be performed with the squared prediction error (SPE) statistics of KPCA. Then, multiple WNN models are designed through the use of low-dimensional sample data preprocessed by KPCA as the training and test samples to detect the fault mode online. Finally, FTA approach is introduced to further locate the fault root causes of the fault mode. The proposed approach is applied to process monitoring and fault diagnosis in a depropanizer unit. Case study results indicate that this approach can be applicable to process monitoring and diagnosis in large-scale chemical plants. Accordingly, the approach can serve as an early and reliable basis for technicians’ and operators’ safety management decision-making.

Highlights

The chemical industry is one of the most important economic forces in world development [1]
While the identification of fault root causes can be performed based on predefined knowledge base and previous experiences, in general, artificial neural network (ANN) or wavelet neural network (WNN) used alone will lead to a large neural network size with long learning time and low diagnosis accuracy
A hybrid approach based on hazard and operability (HAZOP) analysis, kernel principal component analysis (KPCA), WNN, and fault tree analysis (FTA) for process monitoring and fault diagnosis is proposed in this study

Summary

A Hybrid Process Monitoring and Fault Diagnosis Approach for Chemical Plants

Hebei Key Laboratory of Applied Chemistry, College of Environmental and Chemical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China. Given their potentially enormous risk, process monitoring and fault diagnosis for chemical plants have recently been the focus of many studies. Based on hazard and operability (HAZOP) analysis, kernel principal component analysis (KPCA), wavelet neural network (WNN), and fault tree analysis (FTA), a hybrid process monitoring and fault diagnosis approach is proposed in this study. HAZOP analysis helps identify the fault modes and determine process variables monitored. The KPCA model is constructed to reduce monitoring variable dimensionality. Case study results indicate that this approach can be applicable to process monitoring and diagnosis in large-scale chemical plants. The approach can serve as an early and reliable basis for technicians’ and operators’ safety management decision-making

Introduction

Process Monitoring and Fault Diagnosis Approach

Case Study

Conclusions