SENSITIVITY ANALYSIS VIA ARTIFICIAL NEURAL NETWORK OF BIOMASS BOILER EMISSION

Authors

  • Ahmad Razlan Yusoff Faculty of Mechanic al Engineering University College of Engineering and Technology Malaysia (KUKTEM) Locked Bag 12, 25000 Kuantan, Pahang, Malaysia
  • Ishak Abdul Aziz School of Mechanical Engineering Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia

Keywords:

Correlation coeffficient sensitivity analysis, artificial neural networks, biomass boiler emission

Abstract

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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Published

2018-04-25

How to Cite

Yusoff, A. R., & Abdul Aziz, I. (2018). SENSITIVITY ANALYSIS VIA ARTIFICIAL NEURAL NETWORK OF BIOMASS BOILER EMISSION. Jurnal Mekanikal, 18(2). Retrieved from https://jurnalmekanikal.utm.my/index.php/jurnalmekanikal/article/view/208

Issue

Section

Mechanical

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