Kawsalya Pillay A/P Subramaniam,		 						 	,	, <br/><br/>
Optimizing risk management strategies for pharmaceutical water systems through enhanced intergration of machine learning and quantitative risk assessment / 
Kawsalya Pillay A/P Subramaniam 
 - xi, 47 pages :  illustrations ; 
<br/><br/> Faculty of Chemical and Process Engineering Technology 
<br/><br/>Final Year Report (Bachelor of Manufacturing Engineering Technology (Pharmaceuticals) with Hons ) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025 
<br/><br/>Include bibliographical reference 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>THE0010730 (Local) 
<br/><br/><label>Subjects--Corporate Names: </label><br/>Faculty of Chemical and Process Engineering Technology--Dissertations
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