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Virtual Sensors for Biodiesel Production in a Batch Reactor

Author

Listed:
  • Betty Y. López-Zapata

    (Posgrado Electrónica, Tecnológico Nacional de México-Centro Nacional de Investigación y Desarrollo Tecnológico, Interior Internado Palmira S/N, Palmira, Cuernavaca, Morelos 62490, México
    Departamento de Mecatrónica, Universidad Politécnica de Chiapas, Carretera Tuxtla-Villaflores KM. 1+500, Suchiapa, Chiapas 29150, México)

  • Manuel Adam-Medina

    (Departamento de Ingeniería Electrónica, Tecnológico Nacional de México-Centro Nacional de Investigación y Desarrollo Tecnológico, Interior Internado Palmira S/N, Palmira, Cuernavaca, Morelos 62490, México)

  • Peggy E. Álvarez-Gutiérrez

    (Catedra CONACYT-Tecnológico Nacional de México-Instituto Tecnológico de Tuxtla Gutiérrez, Carretera Panamericana Km. 1080, Tuxtla Gutiérrez, Chis 29000, México)

  • Juan P. Castillo-González

    (Posgrado Electrónica, Tecnológico Nacional de México-Centro Nacional de Investigación y Desarrollo Tecnológico, Interior Internado Palmira S/N, Palmira, Cuernavaca, Morelos 62490, México)

  • Héctor R. Hernández-de León

    (Departamento de Maestría en Mecatrónica, Tecnológico Nacional de México-Instituto Tecnológico de Tuxtla Gutiérrez, Carretera Panamericana km 1080, Col. Terán, Tuxtla Gutiérrez, Chis 29050, México)

  • Luis G. Vela-Valdés

    (Departamento de Ingeniería Electrónica, Tecnológico Nacional de México-Centro Nacional de Investigación y Desarrollo Tecnológico, Interior Internado Palmira S/N, Palmira, Cuernavaca, Morelos 62490, México)

Abstract

Fossil fuel combustion produces around 98% of coal emissions. Therefore, liquid and gaseous biofuels have become more attractive due to their environmental benefits. The biodiesel production process requires measurements that help to control and supervise the variables involved in the process. The measurements provide valuable information about the operation conditions and give estimations about the critical variables of the process. The information from measurements is essential for monitoring the state of a process and verifying if it has an optimal performance. The objective of this study was the conception of a virtual sensor based on the Extended Kalman Filter (EKF) and the model of a batch biodiesel reactor for estimating concentrations of triglycerides (TG), diglycerides (DG), monoglycerides (MG), methyl ester (E), alcohol (A), and glycerol (GL) in real-time through measurement of the temperature and pH. Estimation of the TG, DG, MG, E, A, and Gl through this method eliminates the need for additional sensors and allows the use of different types of control. For the performance analysis of the virtual sensor, the data obtained from the EKF are compared with experimental data reported in the literature, with the mean square error of the estimate then being calculated. In addition, the results of this approach can be implemented in a real system, since it only uses measurements available in a reactor such as temperature and pH.

Suggested Citation

  • Betty Y. López-Zapata & Manuel Adam-Medina & Peggy E. Álvarez-Gutiérrez & Juan P. Castillo-González & Héctor R. Hernández-de León & Luis G. Vela-Valdés, 2017. "Virtual Sensors for Biodiesel Production in a Batch Reactor," Sustainability, MDPI, vol. 9(3), pages 1-11, March.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:3:p:455-:d:93495
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    References listed on IDEAS

    as
    1. Docimo, D.J. & Ghanaatpishe, M. & Mamun, A., 2017. "Extended Kalman Filtering to estimate temperature and irradiation for maximum power point tracking of a photovoltaic module," Energy, Elsevier, vol. 120(C), pages 47-57.
    2. Isidro Ovando-Medina & Francisco Espinosa-García & Juan Núñez-Farfán & Miguel Salvador-Figueroa, 2009. "Does Biodiesel from Jatropha Curcas Represent a Sustainable Alternative Energy Source?," Sustainability, MDPI, vol. 1(4), pages 1-7, November.
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    Cited by:

    1. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    2. Shelare, Sagar D. & Belkhode, Pramod N. & Nikam, Keval Chandrakant & Jathar, Laxmikant D. & Shahapurkar, Kiran & Soudagar, Manzoore Elahi M. & Veza, Ibham & Khan, T.M. Yunus & Kalam, M.A. & Nizami, Ab, 2023. "Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production," Energy, Elsevier, vol. 282(C).

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