Heat Exchanger Modeling by Neural Network Optimization for PETRONAS Penapisan Melaka Sdn. Bhd (PPMSB) Crude Preheat Train

Md. Tahir, Norazliza (2005) Heat Exchanger Modeling by Neural Network Optimization for PETRONAS Penapisan Melaka Sdn. Bhd (PPMSB) Crude Preheat Train. [Final Year Project] (Unpublished)

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Abstract

The title of this Final Year Research Project is 'Heat Exchanger Modeling by Neural
Network Optimization for PETRONAS Penapisan Melaka Sdn. Bhd (PPMSB) Crude
Preheat Train'. This project involves the post modeling of heat exchanger sensitivity
analysis matcovers neural network based model and implication of statistical analysis to
predict the heat exchanger efficiency for maintenance scheduling strategy of Crude
Preheat Train (CPT). Themainobjectives of this study are to minimize the error in the
predicted values andenhance therobustness ofthe previous model to predict in future.
This Final Report consists of five major sections. The first section describes the
introduction to Neural Networkbased PredictiveModel, backgroundof the CPT, fouling
activity and Heat Exchanger Maintenance in PP(M)SB, problem statement that defined
the significant ofthepost modeling heat exchanger sensitivity analysis, project objectives
and scope of works done throughout the study. The next section consists of literature
review and theory extracted from well established journals and web sites to provide
relevant information for the project as references.
The third section entails the project methodology comprising series of stages for the
project to be carried out. It follows by the fourth section that serves as the gist of the
report that presents the findings and includes discussion on the results obtained and
significance behind any failure occurs at each stage of the completed optimization
strategies. The results are discussed in term of statistical analysis, comparison ofresults
between different transfer functions configurations used and graphs of actual denormalized
versus predicted outlet temperature for both tube side and shell side. The final
section of the report consists of the conclusion corresponds to the objectives set earlier
and some recommendations for future improvement of the Neural Network model. The
FinalReport ends with a listof references andappendices.

Item Type: Final Year Project
Subjects: T Technology > TP Chemical technology
Departments / MOR / COE: Engineering > Chemical
Depositing User: Users 2053 not found.
Date Deposited: 30 Sep 2013 16:55
Last Modified: 25 Jan 2017 09:46
URI: http://utpedia.utp.edu.my/id/eprint/7654

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