Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

Application of Artificial Neural Network in Prediction of Methane Gas Hydrate Formation Rate

Mgt Rodzep, Megat Naimputra (2015) Application of Artificial Neural Network in Prediction of Methane Gas Hydrate Formation Rate. IRC, Universiti Teknologi PETRONAS. (Unpublished)

[img] PDF
Download (930kB)


This work aims to use Artificial Neural Network (ANN) in prediction of methane gas hydrate formation. There are a lot of thermodynamic modelling have been developed and applied in prediction of the formation gas hydrate however there is still none yet proven model that can predict the formation rate of methane gas hydrate. This study emerges as to build a kinetic model consume time and are very complex due to stochastic behavior of gas hydrate. Therefore, ANN methods show the best potential technology to be used for development of model to predict the formation rate of gas hydrate. The aims of this study are to develop artificial kinetic models by using ANN that can predict the growth rate of formation of methane gas hydrate. To determine the best configuration to be used in ANN involving the number of layers and number of hidden neurons to be used in ANN models. In this study, pressure and temperature are used as the model’s input with the growth rate of methane gas hydrate as the model’s output. The result shows every ANN model has different best configuration in prediction of methane gas hydrate. From the study also few limitation of ANN also addressed

Item Type: Final Year Project
Academic Subject : Academic Department - Chemical Engineering - Separation Process
Subject: T Technology > TP Chemical technology
Divisions: Engineering > Chemical
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 09 Mar 2016 10:50
Last Modified: 25 Jan 2017 09:35
URI: http://utpedia.utp.edu.my/id/eprint/16295

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...