Predicting Smoke Emission From Palm Oil Mill Using Artificial Neural Networks

Malaysia produces 50 % of the total quantity of palm oil produced in the world and this makes it the largest palm oil producer in the world. Palm oil is produced in palm oil mills, which have their captive steam power plants and these plants use palm oil waste (shell and fibre) as fuel for the boil...

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Main Author:	Yusoff, Ahmad Razlan
Format:	Thesis
Language:	English
Published:	2002
Subjects:	TJ1-1570 Mechanical engineering and machinery
Online Access:	http://eprints.usm.my/60756/1/Pages%20from%20Ahmad%20Razlan.pdf
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id	my-usm-ep.60756
record_format	uketd_dc
spelling	my-usm-ep.607562024-06-26T00:48:50Z Predicting Smoke Emission From Palm Oil Mill Using Artificial Neural Networks 2002-11 Yusoff, Ahmad Razlan TJ1-1570 Mechanical engineering and machinery Malaysia produces 50 % of the total quantity of palm oil produced in the world and this makes it the largest palm oil producer in the world. Palm oil is produced in palm oil mills, which have their captive steam power plants and these plants use palm oil waste (shell and fibre) as fuel for the boilers. Unfortunately, the combustion products of these materials cause severe atmospheric pollutions. According to a survey in 1999, only 76% of the palm oil mills in Malaysia meet the regulation of Department of Environment (DOE) regarding the emission. The emission released through the chimney can be monitored by modeling its process of input (in fuel, turbine, boiler) and output of the pollutants. Modeling the emission from the palm oil mill boiler based on Artificial Neural Networks (ANN) is used in this research. 2002-11 Thesis http://eprints.usm.my/60756/ http://eprints.usm.my/60756/1/Pages%20from%20Ahmad%20Razlan.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Mekanikal (School of Mechanical Engineering)
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TJ1-1570 Mechanical engineering and machinery
spellingShingle	TJ1-1570 Mechanical engineering and machinery Yusoff, Ahmad Razlan Predicting Smoke Emission From Palm Oil Mill Using Artificial Neural Networks
description	Malaysia produces 50 % of the total quantity of palm oil produced in the world and this makes it the largest palm oil producer in the world. Palm oil is produced in palm oil mills, which have their captive steam power plants and these plants use palm oil waste (shell and fibre) as fuel for the boilers. Unfortunately, the combustion products of these materials cause severe atmospheric pollutions. According to a survey in 1999, only 76% of the palm oil mills in Malaysia meet the regulation of Department of Environment (DOE) regarding the emission. The emission released through the chimney can be monitored by modeling its process of input (in fuel, turbine, boiler) and output of the pollutants. Modeling the emission from the palm oil mill boiler based on Artificial Neural Networks (ANN) is used in this research.
format	Thesis
qualification_level	Master's degree
author	Yusoff, Ahmad Razlan
author_facet	Yusoff, Ahmad Razlan
author_sort	Yusoff, Ahmad Razlan
title	Predicting Smoke Emission From Palm Oil Mill Using Artificial Neural Networks
title_short	Predicting Smoke Emission From Palm Oil Mill Using Artificial Neural Networks
title_full	Predicting Smoke Emission From Palm Oil Mill Using Artificial Neural Networks
title_fullStr	Predicting Smoke Emission From Palm Oil Mill Using Artificial Neural Networks
title_full_unstemmed	Predicting Smoke Emission From Palm Oil Mill Using Artificial Neural Networks
title_sort	predicting smoke emission from palm oil mill using artificial neural networks
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Mekanikal (School of Mechanical Engineering)
publishDate	2002
url	http://eprints.usm.my/60756/1/Pages%20from%20Ahmad%20Razlan.pdf
_version_	1804888994194915328

Predicting Smoke Emission From Palm Oil Mill Using Artificial Neural Networks

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