Biodiesel quality monitoring using vibrational spectroscopy

Abstract

Biodiesel production and utilization has been increasing rapidly worldwide in recent years. A main challenge in the commercialization and public acceptance of biodiesel is its quality control. This work reports the use of infrared spectroscopy to monitor biodiesel quality through the development of models to predict (1) the blending level of biodiesel in biodiesel-diesel mixtures, (2) the fatty acid profile of biodiesel fuels derived from various lipids, and (3) the concentration of most common impurities present in biodiesel including water, glycerol, methanol and triglycerides. Regressions based on near-infrared (NIR) spectroscopy were developed for relatively inexpensive and rapid on-line measurement of the concentration and specific gravity of biodiesel-diesel blends. Methyl esters of five different oils—soybean oil, canola oil, palm oil, waste cooking oil, and coconut oil—and two different brands of commercial-grade No. 2 on-highway diesel and one brand of off-road No. 2 diesel were used in the calibration and validation processes. The predicted concentration and specific gravity of the biodiesel-diesel blends were compared with the actual values. The maximum and average root-mean-square errors of prediction (RMSEP) of biodiesel concentration were 5.2% and 2.9%, respectively, from the biodiesel type-specific regression. For the general regression, the RMSEP were 3.2% and 0.002 for biodiesel concentration and specific gravity predictions, respectively. Five different models were developed to determine the concentration of methyl palmitate (C16:0), methyl stearate (C18:0), methyl oleate (C18:1), methyl linoleate (C18:2), and methyl linolenate (18:3) present in biodiesel. Using the NIR range a set of models based on four different types of biodiesel was developed. The maximum RMSEP was 0.553% when the models were validated with biodiesel samples that were used in the calibration, however, prediction accuracy of the model under external samples was poor, therefore, a new set of models was proposed. For this case, six different types of biodiesel were used. The models developed for C18:1, C18:2 and C18:3 presented good accuracy on prediction. However, for C16:0 and C18:0, additional work was necessary to reach reasonable accuracy in prediction. Three sub models for specific ranges of concentration (low, medium, and high) were developed. The RMSEP was reduced from 2.98% to 1.51% for the C16:0 and from 2.33% to 0.56% for C18:0, when the sub-models were validated under internal and external samples. Similar procedures were followed to develop regression models based on mid infrared (MIR) spectra. The RMSEP for C16:0, C18:0, C18:1, C18:2, and C18:3 were 0.83%, 0.37%, 1.45%, 1.59%, and 0.84%, respectively. Predictions using MIR spectroscopy models were better than those obtained with NIR spectroscopy models for the C16:0 and C18:0 models. The most common impurities present in biodiesel from production processes, including methanol, free glycerol, triglycerides, and water, were determined by infrared methods using NIR and MIR spectra and partial least square regression (PLSR) methods. The models were developed in two different approaches, one was when a single impurity was present and the other was when all impurities were present. In the single impurity models, the maximum RMSEP obtained in the NIR and MIR models were 647 mg kg[superscript]-1 and 206 mg kg[superscript]-1, respectively. The models for methanol, glycerol, and water performed better using the NIR data. For the triglycerides model, MIR worked better. Only NIR data were used to develop the models for samples with all impurities. Data pre-treatment (Savitzky-Golay second derivative) was necessary to achieve reasonable accuracy in the predictions in this type of models. The maximum RMSEP was 932 mg kg[superscript]-1 presented in the model for triglycerides. The best performance was obtained in the model developed to predict methanol concentration in biodiesel with RMSEP of 177 mg kg[superscript]-1 when all listed impurities were presented. The feasibility of using NIR and MIR spectroscopy to monitor biodiesel quality was demonstrated in this work. The developed method was accurate, rapid, convenient, yet inexpensive to determine some important characteristics of biodiesel, such as biodiesel blending level in biodiesel-diesel mixtures, the fatty acid profile of biodiesel, and impurities present in the fuel.