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Olfactory Profile and Stochastic Analysis: An Innovative Approach for Predicting the Physicochemical Characteristics of Recycled Waste Cooking Oils for Sustainable Biodiesel Production

Author

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  • Suelen Conceição de Carvalho

    (Department of Chemical Engineering, Engineering School of Lorena, University of São Paulo, Lorena 12602-810, SP, Brazil)

  • Maryana Mathias Costa Silva

    (Department of Chemical Engineering, Engineering School of Lorena, University of São Paulo, Lorena 12602-810, SP, Brazil)

  • Adriano Francisco Siqueira

    (Department of Basic and Environmental Sciences, Engineering School of Lorena, University of São Paulo, Lorena 12602-810, SP, Brazil)

  • Mariana Pereira de Melo

    (Department of Basic and Environmental Sciences, Engineering School of Lorena, University of São Paulo, Lorena 12602-810, SP, Brazil)

  • Domingos Sávio Giordani

    (Department of Chemical Engineering, Engineering School of Lorena, University of São Paulo, Lorena 12602-810, SP, Brazil)

  • Tatiane de Oliveira Souza Senra

    (Department of Basic and Environmental Sciences, Engineering School of Lorena, University of São Paulo, Lorena 12602-810, SP, Brazil)

  • Ana Lucia Gabas Ferreira

    (Department of Basic and Environmental Sciences, Engineering School of Lorena, University of São Paulo, Lorena 12602-810, SP, Brazil)

Abstract

The efficient, economical, and sustainable production of biodiesel from waste cooking oils (WCOs) depends on the availability of simple, rapid, and low-cost methods to test the quality of potential feedstocks. The aim of this study was to establish the applicability of stochastic modeling of e-nose profiles in the evaluation of recycled WCO characteristics. Olfactory profiles of 10 WCOs were determined using a Sensigent Cyranose ® 320 chemical vapor-sensing device with a 32 sensor-array, and a stepwise multiple linear regression (MLR) analysis was performed to select stochastic parameters (explanatory variables) for inclusion in the final predictive models of the physicochemical properties of the WCOs. The most important model parameters for the characterization of WCOs were those relating to the time of inception of the e-nose signal “plateau” and to the concentration of volatile organic compounds (VOCs) in the sensor region. A comparison of acid values, peroxide values, water contents, and kinematic viscosities predicted by the MLR models with those determined by conventional laboratory methods revealed that goodness of fit and predictor accuracy varied from good to excellent, with all metric values >90%. Combining e-nose profiling with stochastic modeling was successful in predicting the physicochemical characteristics of WCOs and could be used to select suitable raw materials for efficient and sustainable biodiesel production.

Suggested Citation

  • Suelen Conceição de Carvalho & Maryana Mathias Costa Silva & Adriano Francisco Siqueira & Mariana Pereira de Melo & Domingos Sávio Giordani & Tatiane de Oliveira Souza Senra & Ana Lucia Gabas Ferreira, 2024. "Olfactory Profile and Stochastic Analysis: An Innovative Approach for Predicting the Physicochemical Characteristics of Recycled Waste Cooking Oils for Sustainable Biodiesel Production," Sustainability, MDPI, vol. 16(22), pages 1-14, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9998-:d:1522278
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    References listed on IDEAS

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    1. César, Aldara da Silva & Werderits, Dayana Elizabeth & de Oliveira Saraiva, Gabriela Leal & Guabiroba, Ricardo César da Silva, 2017. "The potential of waste cooking oil as supply for the Brazilian biodiesel chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 246-253.
    2. Talebian-Kiakalaieh, Amin & Amin, Nor Aishah Saidina & Mazaheri, Hossein, 2013. "A review on novel processes of biodiesel production from waste cooking oil," Applied Energy, Elsevier, vol. 104(C), pages 683-710.
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