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Predicting Smoke Emission From Palm Oil Mill Using Artificial Neural Networks

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dc.contributor.authorAhmad Razlan Bin Yusoff
dc.date.accessioned2024-06-26T00:45:08Z
dc.date.available2024-06-26T00:45:08Z
dc.date.issued2002-11
dc.description.abstractMalaysia 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.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/19513
dc.subjectPredicting Smoke Emission
dc.subjectPalm Oil Mill
dc.subjectArtificial Neural Networks
dc.titlePredicting Smoke Emission From Palm Oil Mill Using Artificial Neural Networks
dc.typeResource Types::text::thesis::master thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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