Fault Diagnosis of Chemical Process Based on ACO-BP Neural Network
2012 ◽
Vol 217-219
◽
pp. 2722-2725
Keyword(s):
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.
2013 ◽
Vol 2013
◽
pp. 1-8
◽
2010 ◽
Vol 29-32
◽
pp. 1543-1549
◽
Keyword(s):
2015 ◽
Vol 742
◽
pp. 412-418
2019 ◽
Vol 10
(04)
◽
pp. 1950024
◽
2010 ◽
Vol 439-440
◽
pp. 848-853
Keyword(s):
Control of Continuous Stirred Tank Reactor Using Artificial Neural Networks Based Predictive Control
2012 ◽
Vol 550-553
◽
pp. 2908-2912
◽
2014 ◽
Vol 530-531
◽
pp. 517-521