MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03218ntm a2200349 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260715175018.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t|||||r|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
260715t20252025my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0010744 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| 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 .D74 2025 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Nur Danisha Amalia Binti Zainuddin, , , |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Optimizing risk management strategies of reverse osmosis for pharmaceutical purified water systems through enhanced integration of machine learning and quantitative risk assessment / |
| Statement of responsibility, etc. |
Nur Danisha Amalia Binti Zainuddin |
| 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 |
2025 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2025 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 56 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 Manufacturing Engineering Technology (Pharmaceuticals) with Hons ) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Include bibliographical reference |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
In the pharmaceutical industry, ensuring consistent product quality requires effective quality risk management (QRM) due to the high risks associated with processes and products. A significant challenge arises in purified water systems, particularly in reverse osmosis (RO) units, where membrane fouling affects production reliability. This study explores how big data analytics and machine learning (ML) can improve risk assessment for RO systems used in water purification for pharmaceuticals. This research utilized the Friday 13th (Fr 13) framework with @RISK software for quantitative risk analysis and WEKA software for machine learning simulations. The study began by identifying a suitable mathematical model for the RO process. Risk assessments were performed using the Fr 13 framework with @RISK software, simulating 10,000 possible risk scenarios. Following this, a systematic methodology was employed to validate the p-values from @RISK in WEKA, training the random forest algorithm by separating 70% of the data for training and 30% for testing. The results revealed a minimal 0.43% difference in risk failure predictions between WEKA and @RISK, highlighting the reliability of WEKA’s machine learning capabilities. The findings emphasize that machine learning can enhance the efficiency and accuracy of risk assessments, offering a more adaptable and resource-efficient approach compared to traditional methods. By integrating these advanced tools, this research contributes to achieving UN Sustainable Development Goal (SDG) 6 (Clean Water and Sanitation), ensuring high-quality water for pharmaceutical manufacturing. To conclude, WEKA's machine learning algorithms offer efficient risk assessment, saving time and resources compared to manual and conventional approaches. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| 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 |
|
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |