Performance prediction of a RPF-fired boiler using artificial neural networks

Author:

Behera Shishir Kumar12,Rene Eldon R.3,Kim Min Choul4,Park Hung-Suck2

Affiliation:

1. Chemical Engineering Department; GMR Institute of Technology; Rajam 532127 Srikakulam Dist. Andhra Pradesh India

2. Center for Clean Technology and Resource Recycling; University of Ulsan; Ulsan 680749 South Korea

3. Pollution Prevention and Resource Recovery Core, Department of Environmental Engineering and Water Technology; UNESCO-IHE; Westvest 7 2611 AX Delft The Netherlands

4. Yoosung Corp. Ltd.; Ulsan South Korea

Publisher

Wiley

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Reference41 articles.

1. MOE (Ministry of Environment), South Korea http://eng.me.go.kr/content.do?method=moveContent&menuCode=pol_rec_pol_rec_energy

2. Industrial symbiosis: literature and taxonomy;Chertow;Annual Review of Energy and the Environment,2000

3. Evolution of ‘designed’ industrial symbiosis networks in the Ulsan Eco-industrial Park: ‘research and development into business’ as the enabling framework;Behera;Journal of Cleaner Production,2012

4. Neural network for evaluating boiler behavior;Romeo;Applied Thermal Engineering,2006

5. Artificial neural network-based prediction of hydrogen content of coal in power station boilers;Yao;Fuel,2005

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