MARC details
| 000 -LEADER |
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| fixed length control field |
02916ntm a2200337 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 |
20260714172102.0 |
| 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 |
260714t20252025my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010730 (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 .K39 2025 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kawsalya Pillay A/P Subramaniam, , , |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Optimizing risk management strategies for pharmaceutical water systems through enhanced intergration of machine learning and quantitative risk assessment / |
| Statement of responsibility, etc. |
Kawsalya Pillay A/P Subramaniam |
| 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, 47 pages : |
| Other physical details |
illustrations ; |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| 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. |
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| Summary, etc. |
Pharmaceutical water systems play a crucial role in ensuring the quality and safety of pharmaceutical products. However, contamination risks and system failures remain significant challenges. This study aims to optimize risk management strategies in pharmaceutical water systems through the enhanced integration of machine learning and quantitative risk assessment (QRA). By adopting this approach, a mathematical model was developed to assess membrane fouling risks in reverse osmosis (RO) systems. The Friday 13th (Fr 13) risk assessment framework was utilized to conduct an in-depth evaluation of potential failures in the RO system. Simulations were performed using @RISK software to identify critical parameters influencing system performance. The obtained data was then validated using Scilab, where machine learning techniques were employed to improve the accuracy of risk predictions. Findings from the study indicate that integrating machine learning with QRA enables more proactive risk monitoring and effective failure management. The developed model allows for more precise assessment of membrane fouling failure probabilities and proposes preventive measures to mitigate risks. This research demonstrates that data-driven analytical approaches enhance the resilience of pharmaceutical water systems against contamination risks and failures. The implications of this study contribute to the improvement of risk management efficiency in the pharmaceutical industry, ensuring compliance with global safety and quality standards. |
| 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 |
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| 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 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |