SENSITIVITY ANALYSIS VIA ARTIFICIAL NEURAL NETWORK OF BIOMASS BOILER EMISSION
Keywords:
Correlation coeffficient sensitivity analysis, artificial neural networks, biomass boiler emissionAbstract
According to a survey in J999, only 76% of the palm oil mills in Malaysia meet the regulation of
Department of Environment (DOE) regarding emission. The emission is released through the
chimney from the process of fuel combustion and steam generation in order to produce power to the mill. The complex and very highly lion-linear process involves several variables in fuel,
turbine and boiler as factors producing pollutants. Therefore. Sensitivity Analysis via Artificial
Neural Networks (SAANN) and Correlation Coefficient (CC) were used to find the major and
minor input variables to each pollutant from 15 input variables. The result shows the major and
minor input variables for both methods are similar.
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