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Modeling the energy ratio and productivity of biodiesel with different reactor dimensions and ultrasonic power using ANFIS

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  • Naderloo, Leila
  • Javadikia, Hossein
  • Mostafaei, Mostafa

Abstract

To analyze issues relating to sustainability, the energy consumed for producing one kilogram of a product was utilized to compare systems. Moreover, the energy indices of biodiesel production using the ultrasound-assisted transesterification reactions might be affected by the reactor dimensions and the ultrasonic power. The present study aimed to model the energy productivity and ratio in producing biodiesel from the waste cooking oil through ANFIS model. The properties of the produced biodiesel demonstrated that it possessed a desirable quality. The total energy inputs and outputs measured 36.652 and 45.007MJL−1, respectively. In addition, the mean scores of the energy ratio and productivity were 1.283 and 0.024 MJ/kg, respectively. The inputs of the ANFIS model comprised the diameter of the reactor and the height and the percentage of the ultrasonic power. The results indicated that there was an initial rise in the ratio of the energy with the increase of the height and the diameter of the reactor as well as the ultrasonic power, followed by a drop. Further, the results of employing the ANFIS model demonstrated that MSE and R2 values measured 5.54e−6 and 0.87 for the energy ratio and 2.94e−7 and 0.80 for the energy productivity, respectively. However, the adjusted R2 of the linear regression model without intercept measured 0.773 and 0.774 for the energy ratio and productivity, respectively. Accordingly, the ANFIS model could predict the energy ratio and productivity more precisely than the linear regression model.

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  • Naderloo, Leila & Javadikia, Hossein & Mostafaei, Mostafa, 2017. "Modeling the energy ratio and productivity of biodiesel with different reactor dimensions and ultrasonic power using ANFIS," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 56-64.
    • Handle: RePEc:eee:rensus:v:70:y:2017:i:c:p:56-64
    DOI: 10.1016/j.rser.2016.11.035
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    7. Omid Ghorbanzadeh & Hashem Rostamzadeh & Thomas Blaschke & Khalil Gholaminia & Jagannath Aryal, 2018. "A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(2), pages 497-517, November.
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