Ontology and SDG-based framework for fault diagnosis in chemical process

2014 ◽  
Author(s):  
Dong Gao ◽  
Xin Xu ◽  
Xin Ma
Keyword(s):  
Fault Diagnosis ◽  
Chemical Process

Fault Diagnosis of Chemical Process Based on ACO-BP Neural Network

2012 ◽  
Vol 217-219 ◽  
pp. 2722-2725
Author(s):  
Jian Xue Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Hybrid Algorithm ◽  
Chemical Process ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Continuous Stirred Tank Reactor ◽  
The Neural Network ◽  
Continuous Stirred Tank

Fault diagnosis is an important problem in the process of chemical industry and the artificial neural network is widely applied in fault diagnosis of chemical process. A hybrid algorithm combining ant colony optimization (ACO) algorithm with back-propagation (BP) algorithm, also referred to as ACO-BP algorithm, is proposed to train the neural network weights and thresholds. The basic theory and steps of ACO-BP algorithm are given, and applied in fault diagnosis of the continuous stirred-tank reactor (CSTR). Experimental results prove that ACO-BP algorithm has good fault diagnosis precision, and it can detect the fault in CSTR promptly and effectively.

A hybrid fault diagnosis strategy for chemical process startups

2012 ◽  
Vol 22 (7) ◽  
pp. 1287-1297 ◽  
Author(s):  
Zhenheng Wang ◽  
Jinsong Zhao ◽  
Helen Shang
Keyword(s):  
Fault Diagnosis ◽  
Chemical Process
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