000			03047ntm a2200349 i 4500
003			MY-KuUP
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007			ta
008			260630t20242024my a|||fr|||| 000 0 eng d
020			_aTHE0010645 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKKP .Y53 2024 r Bc.
100	1		_aStephanie Su, Yi Chein, _eauthor.
245	1	0	_aMachine learning-incorporated membrane performance prediction for membrane processes / _bStephanie Su Yi Chein
264		1	_aKuantan, Pahang : _bUMPSA, _c2024
264		4	_c© 2024
300			_axvi, 60 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 Chemical Engineering ) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2024
504			_aInclude bibliographical reference
520	3		_aThis research project aimed to develop a machine learning model for predicting membrane performance in membrane processes. The study analysed low and multi-dimensional data using Jupyter as coding environment with Python language. Inputs such as membrane type, pressure, temperature, pressure, and solute concentration were considered. The multilayer perceptron, a fully connected class of feedforward artificial neural network (ANN), was chosen and implemented in Jupyter Notebook as the machine learning technique. The project aimed to overcome the limitations of traditional methods, offering a more efficient and cost-effective approach to predicting membrane performance. The study included examining prediction of permeance and rejection of the membrane, mostly focused on silica-coated membrane. The dataset comprised 361 samples with essential variables and underwent pre-processing steps. Performance evaluation employed mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and coefficient of determination (R2). In exploring the vital parameters affecting membrane performance, a comprehensive principal component analysis (PCA) revealed surprising similarities in factors influencing both permeance and rejection. Employing AI models resulted in unprecedented accuracy, reaching as high as 89% for permeance and 90% for rejection prediction. Therefore, this research successfully developed a machine learning model for predicting membrane performance, focusing on silica-coated membranes. In summary, through data analysis, model development, and validation, the project sought to provide a more efficient and cost-effective approach to predict membrane performance, contributing to sustainable membrane processes.
610	2	0	_aFaculty of Chemical and Process Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
856			_uhttps://umpir.ump.edu.my/id/eprint/47137
942			_2lcc _cPSM
999			_c105060 _d105066