MARC details
| 000 -LEADER |
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| fixed length control field |
03047ntm a2200349 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20260630111140.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t|||||r|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
260630t20242024my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010645 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FTKKP .Y53 2024 r Bc. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Stephanie Su, Yi Chein, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Machine learning-incorporated membrane performance prediction for membrane processes / |
| Remainder of title |
Stephanie Su Yi Chein |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2024 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2024 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvi, 60 pages : |
| Other physical details |
illustrations ; |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Chemical and Process Engineering Technology |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Final Year Report (Bachelor of Chemical Engineering ) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2024 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Include bibliographical reference |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
This research project aimed to develop a machine learning model for predicting membrane performance in membrane processes. The study analysed low and multi-dimensional data using Jupyter as coding environment with Python language. Inputs such as membrane type, pressure, temperature, pressure, and solute concentration were considered. The multilayer perceptron, a fully connected class of feedforward artificial neural network (ANN), was chosen and implemented in Jupyter Notebook as the machine learning technique. The project aimed to overcome the limitations of traditional methods, offering a more efficient and cost-effective approach to predicting membrane performance. The study included examining prediction of permeance and rejection of the membrane, mostly focused on silica-coated membrane. The dataset comprised 361 samples with essential variables and underwent pre-processing steps. Performance evaluation employed mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and coefficient of determination (R2). In exploring the vital parameters affecting membrane performance, a comprehensive principal component analysis (PCA) revealed surprising similarities in factors influencing both permeance and rejection. Employing AI models resulted in unprecedented accuracy, reaching as high as 89% for permeance and 90% for rejection prediction. Therefore, this research successfully developed a machine learning model for predicting membrane performance, focusing on silica-coated membranes. In summary, through data analysis, model development, and validation, the project sought to provide a more efficient and cost-effective approach to predict membrane performance, contributing to sustainable membrane processes. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Chemical and Process Engineering Technology |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
https://umpir.ump.edu.my/id/eprint/47137 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |