An artificial neural network approach to predicting electrostatic separation performance for food waste recovery

• Autorzy: Lai K.C. , Lim S.K. , Teh P.C. , Yeap K.H.
• Języki publikacji: EN

Abstrakty

EN

This study presents the empirical exploration of food waste recovery throughout the electrostatic separation process. In addition, the paper discusses the potential of artificial neural network (ANN) in predicting the responses. A five-level three-factor Taguchi orthogonal array (OA) design of experiment was employed as an initiative to optimize the prediction process. The electrostatic separation process was modelled using ANN by considering the recovered food waste and misclassified middling product during separation. A multi-layer feed-forward network developed in MATLAB was constructed. It was found that the results from the experiment and predicted model were in very good agreement. To our best knowledge, this is the first report for prediction of food waste separation performance employing ANN and Taguchi design.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2017
• Tom: 26
• Numer: 4
• Opis fizyczny: p.1921-1926,fig.,ref.

Twórcy

autor

Lai K.C.

• Universiti Tunku Abdul Rahman, Perak, Malaysia

autor

Lim S.K.

• Universiti Tunku Abdul Rahman, Perak, Malaysia

autor

Teh P.C.

• Universiti Tunku Abdul Rahman, Perak, Malaysia

autor

Yeap K.H.

• Universiti Tunku Abdul Rahman, Perak, Malaysia

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Identyfikatory

DOI

10.15244/pjoes/68963