Combining Genetic Algorithm and Artificial Neural Network to optimize biomass steam power plant emission / Ahmad Razlan Yusoff and Ishak Abdul Aziz

UNSPECIFIED (2008) Combining Genetic Algorithm and Artificial Neural Network to optimize biomass steam power plant emission / Ahmad Razlan Yusoff and Ishak Abdul Aziz. Journal of Mechanical Engineering, 5 (1). pp. 1-15. ISSN 1823-5514

Abstract

Boiler emission released from the steam power plant of palm oil mill cause severe atmospheric pollutions. Genetic Algorithm and Artificial Neural Network (GAANN) were used to analyze the real data taken from palm oil mill power plant. A parametric study of Genetic Algorithms (GA) parameters such as population size, mutation rates and crossover rates are carried out to get optimal parameters for a GAANN model. GAANN is utilized to search several optimal parameters for the boiler, turbine and furnace which released carbon monoxide (CO), nitrogen oxide (NOJ, sulfur dioxide (S02) and particulate matters (PM). Monitoring and controlling of the emissions are achieved with optimum operating conditions of boiler parameters, i.e. below the level permitted by Department of Environment (DOE).

Metadata

Item Type: Article
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Mechanical Engineering
Journal or Publication Title: Journal of Mechanical Engineering
ISSN: 1823-5514
Volume: 5
Number: 1
Page Range: pp. 1-15
Item ID: 17559
Uncontrolled Keywords: Artificial Neural Network, biomass boiler emission, Genetic Algorithms, optimization and emission
URI: http://ir.uitm.edu.my/id/eprint/17559

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