<?xml version="1.0" encoding="UTF-8"?>
<record
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd"
    xmlns="http://www.loc.gov/MARC21/slim">

  <leader>02916ntm a2200337 i 4500</leader>
  <controlfield tag="003">MY-KuUP</controlfield>
  <controlfield tag="005">20260714172102.0</controlfield>
  <controlfield tag="006">t|||||r|||| 000 0</controlfield>
  <controlfield tag="007">ta</controlfield>
  <controlfield tag="008">260714t20252025my a|||fr|||| 000 0 eng d</controlfield>
  <datafield tag="020" ind1=" " ind2=" ">
    <subfield code="a">THE0010730 (Local)</subfield>
    <subfield code="q">Hardback</subfield>
  </datafield>
  <datafield tag="040" ind1=" " ind2=" ">
    <subfield code="a">UMPSA</subfield>
    <subfield code="b">eng</subfield>
    <subfield code="c">UMPSA</subfield>
    <subfield code="e">rda</subfield>
  </datafield>
  <datafield tag="090" ind1=" " ind2=" ">
    <subfield code="a">FTKKP .K39 2025 r Bc.</subfield>
  </datafield>
  <datafield tag="100" ind1="0" ind2=" ">
    <subfield code="a">Kawsalya Pillay A/P Subramaniam,		 						 	,	,</subfield>
    <subfield code="e">author.</subfield>
  </datafield>
  <datafield tag="245" ind1="1" ind2="0">
    <subfield code="a">Optimizing risk management strategies for pharmaceutical water systems through enhanced intergration of machine learning and quantitative risk assessment /</subfield>
    <subfield code="c">Kawsalya Pillay A/P Subramaniam</subfield>
  </datafield>
  <datafield tag="264" ind1=" " ind2="1">
    <subfield code="a">Kuantan, Pahang :</subfield>
    <subfield code="b">UMPSA,</subfield>
    <subfield code="c">2025</subfield>
  </datafield>
  <datafield tag="264" ind1=" " ind2="4">
    <subfield code="c">&#xA9; 2025</subfield>
  </datafield>
  <datafield tag="300" ind1=" " ind2=" ">
    <subfield code="a">xi, 47 pages :</subfield>
    <subfield code="b">illustrations ;</subfield>
  </datafield>
  <datafield tag="336" ind1=" " ind2=" ">
    <subfield code="2">rdacontent</subfield>
    <subfield code="a">text</subfield>
  </datafield>
  <datafield tag="337" ind1=" " ind2=" ">
    <subfield code="2">rdamedia</subfield>
    <subfield code="a">unmediated</subfield>
  </datafield>
  <datafield tag="338" ind1=" " ind2=" ">
    <subfield code="2">rdacarrier</subfield>
    <subfield code="a">volume</subfield>
  </datafield>
  <datafield tag="347" ind1=" " ind2=" ">
    <subfield code="2">rda</subfield>
    <subfield code="a">text file</subfield>
    <subfield code="b">PDF</subfield>
  </datafield>
  <datafield tag="500" ind1=" " ind2=" ">
    <subfield code="a">Faculty of Chemical and Process Engineering Technology</subfield>
  </datafield>
  <datafield tag="502" ind1=" " ind2=" ">
    <subfield code="a">Final Year Report (Bachelor of Manufacturing Engineering Technology (Pharmaceuticals) with Hons ) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025</subfield>
  </datafield>
  <datafield tag="504" ind1=" " ind2=" ">
    <subfield code="a">Include bibliographical reference</subfield>
  </datafield>
  <datafield tag="520" ind1="3" ind2=" ">
    <subfield code="a">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.</subfield>
  </datafield>
  <datafield tag="610" ind1="2" ind2="0">
    <subfield code="a">Faculty of Chemical and Process Engineering Technology</subfield>
    <subfield code="x">Dissertations</subfield>
  </datafield>
  <datafield tag="650" ind1=" " ind2="0">
    <subfield code="a">Universities and colleges</subfield>
    <subfield code="x">Dissertations</subfield>
  </datafield>
  <datafield tag="650" ind1=" " ind2="0">
    <subfield code="a">Theses</subfield>
    <subfield code="x">Dissertations</subfield>
  </datafield>
  <datafield tag="942" ind1=" " ind2=" ">
    <subfield code="2">lcc</subfield>
    <subfield code="c">PSM</subfield>
  </datafield>
  <datafield tag="952" ind1=" " ind2=" ">
    <subfield code="0">0</subfield>
    <subfield code="1">0</subfield>
    <subfield code="2">lcc</subfield>
    <subfield code="4">0</subfield>
    <subfield code="7">0</subfield>
    <subfield code="a">10000</subfield>
    <subfield code="b">10000</subfield>
    <subfield code="d">2026-07-14</subfield>
    <subfield code="l">0</subfield>
    <subfield code="o">FTKKP .K39 2025 r Bc.</subfield>
    <subfield code="p">T000004323</subfield>
    <subfield code="r">2026-07-14 17:21:16</subfield>
    <subfield code="w">2026-07-14</subfield>
    <subfield code="y">RESTRICT</subfield>
  </datafield>
  <datafield tag="999" ind1=" " ind2=" ">
    <subfield code="c">105216</subfield>
    <subfield code="d">105222</subfield>
  </datafield>
</record>
