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Volumn 85, Issue 6-7, 2004, Pages 451-462

Artificial neural network-based estimation of mercury speciation in combustion flue gases

Author keywords

Multilayer perceptron; Neural network correlation; Speciated mercury emissions

Indexed keywords

AIR POLLUTION CONTROL; COAL FIRED BOILERS; CORRELATION METHODS; DATA REDUCTION; FLUE GASES; GAS EMISSIONS; MERCURY (METAL); MULTILAYER NEURAL NETWORKS; PARTICULATE EMISSIONS;

EID: 1842831510     PISSN: 03783820     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fuproc.2003.11.020     Document Type: Conference Paper
Times cited : (40)

References (7)
  • 1
    • 1842862743 scopus 로고    scopus 로고
    • Power plants and mercury, environmental issues
    • Palo Alto, CA: Electric Power Research Institute
    • EPRI Power plants and mercury, environmental issues. EPRI Newsletter. 2001;Electric Power Research Institute, Palo Alto, CA.
    • (2001) EPRI Newsletter
  • 2
    • 0003785190 scopus 로고    scopus 로고
    • An Assessment of Mercury Emissions from U.S. Coal-Fired Power Plants
    • Electric Power Research Institute, Palo Alto, CA, 1000608
    • EPRI, An Assessment of Mercury Emissions from U.S. Coal-Fired Power Plants, Technical Report, Electric Power Research Institute, Palo Alto, CA, 2000, 1000608.
    • (2000) Technical Report
  • 5
    • 0036132055 scopus 로고    scopus 로고
    • Neural network prediction of cetane number and density of diesel fuel from its chemical composition determined by LC and GC-MC
    • Yang H., Ring Z., Briker Y., Mclean N., Friesen W., Fairbride C. Neural network prediction of cetane number and density of diesel fuel from its chemical composition determined by LC and GC-MC. Fuel. 81:2002;65-74.
    • (2002) Fuel , vol.81 , pp. 65-74
    • Yang, H.1    Ring, Z.2    Briker, Y.3    Mclean, N.4    Friesen, W.5    Fairbride, C.6
  • 7


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.