Title: Experimental study and exergy efficiency prediction of three-leaved yam (Dioscorea dumetorum) starch drying

Authors: Emmanuel Olusola Oke; Kenechi Nwosu-Obieogu; Joan Chiamaka Ude

Addresses: Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria ' Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria ' Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria

Abstract: This study predicted three-leaved yam starch drying using adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and support vector machine (SVM). The input and output variables were drying temperature, time, air velocity and exergy efficiency (EE) as well as sustainability index (SI), respectively. The EE performance indices are: ANN (R² = 0.999, MSE = 7.40038E-13), ANFIS (R² = 0.251, MSE = 0.001) and SVM (R² = 0.999, MSE = 3.25E-06). The SI prediction indices are: ANN (R² = 0.99, MSE = 1.0E-10), ANFIS (R² = 0.41, MSE = 0.010) and SVM (R² = 0.61, MSE = 0.051). The results indicated that ANN and SVM gave the best prediction for exergy efficiency (EE); while only ANN model is capable of predicting TLYS sustainability index (SI).

Keywords: exergy efficiency; artificial neural network; ANN; ANFIS; support vector machine; SVM; sustainability index; mean square error; MSE; three-leaved yam; drying; temperature; time; air velocity.

DOI: 10.1504/IJEX.2020.111690

International Journal of Exergy, 2020 Vol.33 No.4, pp.427 - 443

Received: 13 Jan 2020
Accepted: 09 May 2020
Published online: 09 Dec 2020 *

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