NDLI: Neural Network-Based Intelligent Integrated Modeling for the CFB-FGD Process

Content Provider	IEEE Xplore Digital Library
Author	Li Hongru Fan Liting Wang Fuli
Copyright Year	2006
Description	Author affiliation: Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang (Li Hongru; Fan Liting; Wang Fuli)
Abstract	The integration modeling method based on input weighted feed-forward neural network was proposed under the intelligent integrated modeling theory. According to the mechanism model of circulated fluidized bed for flue gas desulfurization(CFB-FGD), the influencing factor of sulfur dioxide removal was confirmed. Furthermore, depending on the effect of influencing factor, weighting coefficients of each factor were got and the intelligent integrated model based on neural network was set up. The simulation results indicate that the integrated model can simulate and predict the desulfurization efficiency perfectly, and is better than the mechanism model
Starting Page	4686
Ending Page	4690
File Size	130778
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1713271
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Intelligent networks Fluidization Predictive models Feedforward systems Spraying Feedforward neural networks Flue gases Information science Environmental factors neural network CFB-FGD mechanism model intelligent integrated model
Content Type	Text
Resource Type	Article

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