Preliminary communication

https://doi.org/10.15255/KUI.2020.006

Soft Computing Prediction of Oil Extraction from Huracrepitan Seeds

Kenechi Nwosu-Obieogu orcid.org/0000-0002-4920-8676 ; Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria
Felix Aguele ; Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria
Linus Chiemenem ; Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria

Abstract

This study analyses the extraction process parameters of huracrepitan seed oil using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). The experiments were conducted at temperature (60–80 °C), time (4–6 h), and solute/solvent ratio (0.05–0.10) with output parameter as oil yield. Sensitivity analysis shows that temperature and time had the most significant effect on the oil yield. The oil yield estimation performance indicators are: ANN (R2 = 0.999, MSE = 5.63192E-13), ANFIS (R2 = 0.36927, MSE = 0.42331). The results show that ANN gave a better prediction than ANFIS.




This work is licensed under a Creative Commons Attribution 4.0 International License.

Keywords

Huracrepitan seed, extraction; artificial neural network (ANN); adaptive neuro-fuzzy inference system (ANFIS)

Hrčak ID:

245514

URI

https://hrcak.srce.hr/245514

Publication date:

1.11.2020.

Article data in other languages: croatian

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