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Development of GC Analyzer Model Using Neural Network

IDRIS BABIKER, AREEJ BABIKER (2011) Development of GC Analyzer Model Using Neural Network. Universiti Teknologi PETRONAS. (Unpublished)

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Gas chromatography (GC) is the most widely used teclmique in analytical chemistry. It is an analytical scientific teclmique to separate a mixture of vaporizable substances and resolve the mixture into single components. Analyzer as hardware has high initial cost, requires frequent maintenance and sometimes fails to provide the accurate outputs. Moreover, the increasing complexity of industrial processes and the struggle for cost reduction, availability, safety and higher profitability requires efficient and reliable instruments. Thus, this final year project is an attempt to develop prototype software that is capable of predicting efficiently plant output and optimize the performance of the model. MATLAB Neural Network and system identification toolboxes were utilized to recommend the best structure to develop this predictive model. The purpose of this report is to show the success and applicability of using neural network in predicting plant output and obtain an alternative measuring system. It presents the followed methodology in achieving project's objectives by giving an overview on neural network and system identification toolboxes and shows a comparison of the performance of Back Propagation Feed Forward Neural Network (BFN) and other System I identification toolbox models. Results demonstrated that neural network model trained using LM provides an adequate result and is suitable for this purposes.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Test and Reliablity
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 08 Nov 2013 11:42
Last Modified: 25 Jan 2017 09:42
URI: http://utpedia.utp.edu.my/id/eprint/10397

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