The increasing use of petrodiesel-biodiesel fuel blends throughout the world requires fast, economic and efficient analytical techniques that can be used for the quality control of these fuels. In this work, we develo...The increasing use of petrodiesel-biodiesel fuel blends throughout the world requires fast, economic and efficient analytical techniques that can be used for the quality control of these fuels. In this work, we developed an analytical method for determining the concentration of African palm biodiesel in blends with petrodiesel;the method is based on infrared spectroscopy (FTIR-ATR). To build a prediction model, nineteen petrodiesel-biodiesel blends were prepared in triplicate with biodiesel concentrations for 0%-100% by weight. The blends were analyzed using Fourier transform infrared spectroscopy, the spectral fingerprint data were used to build a prediction model through PLS regression. The optimal number of principal components (PCs), the standard error of calibration (SEC), the standard validation error (SEV), the correlation coefficient of calibration (r Cal) and the validation correlation coefficient (r Val) were used to validate the predictive ability of the model. The results show that the model obtained in this work has a good ability for determining the concentration of African palm biodiesel in petrodiesel-biodiesel blends.展开更多
In this work, we developed an analytical method based on UV-visible spectroscopy to determine the concentration of biodiesel from African palm in blends of petrodiesel. Seventy-five samples with biodiesel concentratio...In this work, we developed an analytical method based on UV-visible spectroscopy to determine the concentration of biodiesel from African palm in blends of petrodiesel. Seventy-five samples with biodiesel concentrations between 0-100 wt% were prepared. The spectral fingerprints that were obtained from the analysis of the samples by UV-visible spectroscopy were used to build predictive model using PLS regression. The predictive ability of the models was evaluated through statistical parameters: the standard error of calibration (SEC), the standard error of validation (SEV), the correlation coefficient of calibration (r Cal) and validation (r Val), the ratio (SEC/SEV), the coefficient of determination R2, the paired data Student’s t-test, cross-validation and external validation. The results indicate that the PLS model predicts the concentration of biodiesel from African palm with high precision in mixtures with petrodiesel. The method developed in this study can be applied to determine the concentration of biodiesel African palm in mixtures of petrodiesel in a more rapid and economical way. Moreover, this method has less analytical errors and is more environmentally friendly than the conventional methods.展开更多
文摘The increasing use of petrodiesel-biodiesel fuel blends throughout the world requires fast, economic and efficient analytical techniques that can be used for the quality control of these fuels. In this work, we developed an analytical method for determining the concentration of African palm biodiesel in blends with petrodiesel;the method is based on infrared spectroscopy (FTIR-ATR). To build a prediction model, nineteen petrodiesel-biodiesel blends were prepared in triplicate with biodiesel concentrations for 0%-100% by weight. The blends were analyzed using Fourier transform infrared spectroscopy, the spectral fingerprint data were used to build a prediction model through PLS regression. The optimal number of principal components (PCs), the standard error of calibration (SEC), the standard validation error (SEV), the correlation coefficient of calibration (r Cal) and the validation correlation coefficient (r Val) were used to validate the predictive ability of the model. The results show that the model obtained in this work has a good ability for determining the concentration of African palm biodiesel in petrodiesel-biodiesel blends.
文摘In this work, we developed an analytical method based on UV-visible spectroscopy to determine the concentration of biodiesel from African palm in blends of petrodiesel. Seventy-five samples with biodiesel concentrations between 0-100 wt% were prepared. The spectral fingerprints that were obtained from the analysis of the samples by UV-visible spectroscopy were used to build predictive model using PLS regression. The predictive ability of the models was evaluated through statistical parameters: the standard error of calibration (SEC), the standard error of validation (SEV), the correlation coefficient of calibration (r Cal) and validation (r Val), the ratio (SEC/SEV), the coefficient of determination R2, the paired data Student’s t-test, cross-validation and external validation. The results indicate that the PLS model predicts the concentration of biodiesel from African palm with high precision in mixtures with petrodiesel. The method developed in this study can be applied to determine the concentration of biodiesel African palm in mixtures of petrodiesel in a more rapid and economical way. Moreover, this method has less analytical errors and is more environmentally friendly than the conventional methods.
