Biodiesel Synthesis Monitoring using Near Infrared Spectroscopy
Estela Kamile Gelinski, Fabiane Hamerski, Marcos Lúcio Corazza, Alexandre Ferreira Santos*
Identifiers and Pagination:Year: 2018
First Page: 95
Last Page: 110
Publisher Id: TOCENGJ-12-95
Article History:Received Date: 24/7/2018
Revision Received Date: 23/8/2018
Acceptance Date: 31/9/2018
Electronic publication date: 14/11/2018
Collection year: 2018
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Biodiesel is a renewable fuel considered as the main substitute for fossil fuels. Its industrial production is mainly made by the transesterification reaction. In most processes, information on the production of biodiesel is essentially done by off-line measurements.
However, for the purpose of control, where online monitoring of biodiesel conversion is required, this is not a satisfactory approach. An alternative technique to the online quantification of conversion is the near infrared (NIR) spectroscopy, which is fast and accurate. In this work, models for biodiesel reactions monitoring using NIR spectroscopy were developed based on the ester content during alkali-catalyzed transesterification reaction between soybean oil and ethanol. Gas chromatography with flame ionization detection was employed as the reference method for quantification. FT-NIR spectra were acquired with a transflectance probe. The models were developed using Partial Least Squares (PLS) regression with synthetic samples at room temperature simulating reaction composition for different ethanol to oil molar ratios and conversions. Model predictions were then validated online for reactions performed with ethanol to oil molar ratios of 6 and 9 at 55ºC. Standard errors of prediction of external data were equal to 3.12%, hence close to the experimental error of the reference technique (2.78%), showing that even without using data from a monitored reaction to perform calibration, proper on-line predictions were provided during transesterification runs.
Additionally, it is shown that PLS models and NIR spectra of few samples can be combined to accurately predict the glycerol contents of the medium, making the NIR spectroscopy a powerful tool for biodiesel production monitoring.