000			03218ntm a2200349 i 4500
003			MY-KuUP
005			20260715175018.0
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008			260715t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010744 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKKP .D74 2025 r Bc.
100	0		_aNur Danisha Amalia Binti Zainuddin, , , _eauthor.
245	1	0	_aOptimizing risk management strategies of reverse osmosis for pharmaceutical purified water systems through enhanced integration of machine learning and quantitative risk assessment / _cNur Danisha Amalia Binti Zainuddin
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_axi, 56 pages : _billustrations ;
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Chemical and Process Engineering Technology
502			_aFinal Year Report (Bachelor of Manufacturing Engineering Technology (Pharmaceuticals) with Hons ) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025
504			_aInclude bibliographical reference
520	3		_aIn 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	2	0	_aFaculty of Chemical and Process Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
856			_u
942			_2lcc _cPSM
999			_c105230 _d105236