Open AccessPrediction of concentration of dispersed phase outlet in rotating disc contactor column
Author(s) -
Ezzatul Farhain Azmi,
Nor Hamizah Miswan,
Khairil Anuar Arshad,
Nurul Amira Zainal
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1349/1/012096
Subject(s) - contactor , support vector machine , artificial neural network , computer science , flexibility (engineering) , column (typography) , mean squared error , artificial intelligence , phase (matter) , machine learning , mathematics , chemistry , statistics , telecommunications , power (physics) , physics , organic chemistry , quantum mechanics , frame (networking)
Rotating Disc Contactor (RDC) columns are one of the extractors that used for liquid-liquid extraction. It has an extensive application in various industries. The performances of these columns indicate that they are more efficient and possess better operational flexibility. However, there is still some improving that researchers can do to enhance the performances. This paper presents Support Vector Machine (SVM) and Neural Network (NN) modeling in prediction of concentration of dispersed phase outlet in RDC column. SVM is an exciting Machine Learning technique that learns by example to sign labels to object and can be used for regression as well as classification purpose, while NN is widely used as an effective approach for handling non-linear data especially in situations where the physical processes are not fully understood. The mean square error (MSE) is calculated to compare the result between the two models. The analysis shows that both SVM and NN modeling can predict the concentration of dispersed phase in RDC column but the SVM approach gives better result than the NN approach. Both modelling systems offer the potential for a more flexible and less error in forecasting. Thus, it can help to save time and reducing cost in conducting experiments.