Combining Genetic Algorithm and Artificial Neural Network to optimize biomass steam power plant emission / Ahmad Razlan Yusoff and Ishak Abdul Aziz (Faculty of Mechanical Engineering and University Publication Centre (UPENA), 2008)
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).