Paper Info
Title
Implementing a neuro fuzzy expert system for optimising the performance of chemical recovery boiler
Abstract
In chemical recovery boilers of paper mills, main steam outlet temperature control cannot be solved by straight forward automation control. As prior knowledge of the mechanism to maximise steam generation without affecting steam main temperature is unknown, a backpropogation supervisory neural network has been designed which exhibits a good degree of reinforcement learning. Various parameters considered encompassing concentration, composition and firing load of black liquor solids may not have ideal fixed values. Hence, a type 2 fuzzy logic model has been designed which in turn monitors the parameters and predicts the results. Errors are fed back iteratively through the backpropogation network, until the network learns the model. Fuzzy C-means clustering technique has been used to find coherent clusters. Then sensitivity analysis has been done to identify the parameters playing a significant role in obtaining the results. As it can be observed that the behaviour is stochastic, particle swarm optimisation has been implemented to optimise the combined effect of all parameters. Through this tool connecting steam attemperation control and smart soot blowing, clean heating surface is ensured resulting in enhanced green energy output and availability.
Year
DOI
Venue
2014
10.1504/IJAISC.2014.062822
International Journal of Artificial Intelligence and Soft Computing
Keywords
Field
DocType
neural networks,superheater
Particle swarm optimization,Recovery boiler,Neuro-fuzzy,Computer science,Temperature control,Fuzzy logic,Artificial intelligence,Artificial neural network,Superheater,Machine learning,Reinforcement learning
Journal
Volume
Issue
Citations 
4
2/3
1
PageRank 
References 
Authors
0.35
9
3
Name
Order
Citations
PageRank
S. Krishna Anand110.35
T.G. Sundara Raman210.35
S. Subramanian310.35