Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol-Gasoline Blends by Raman Spectroscopy.

Methanol-gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol-gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol-gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)-PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol-gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.

Accurate identification of methanol and ethanol gasoline types and rapid detection of the alcohol content using effective chemical information.

Methanol and ethanol gasoline are two emerging clean energy sources with different characteristics. To achieve the qualitative identification and quantitative analysis of the alcohols present in methanol and ethanol gasoline, effective chemical information (ECI) models based on the characteristic spectral bands of the near-infrared (NIR) spectra of the methanol and ethanol molecules were developed using the partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) algorithms. The ECI model was further compared with models built from the full wavenumber (Full) spectra, variable importance in projection (VIP) spectra, and Monte Carlo uninformative variable elimination (MC-UVE) spectra to determine the predictive performance of ECI model. Among the various qualitative identification models, it was found that the ECI-PLS-DA model, which is built using the differences in molecular chemical information between methanol and ethanol, exhibited sensitivity, specificity and accuracy values of 100%. The ECI-PLS-DA model accurately identified methanol gasoline and ethanol gasoline with different contents. In the quantitative analysis model for methanol gasoline, the methanol gasoline and ethanol gasoline ECI-PLS models exhibited the smallest root mean squared error of predictions (RMSEP) of 0.18 and 0.21% (v/v), respectively, compared to the other models. Meanwhile, the F-test and T-test results revealed that the NIR method employing the ECI-PLS model showed no significant difference compared to the standard method. Compared with other spectral models examined herein, the ECI model demonstrated the highest recognition success and determination accuracy. This study therefore established a highly accurate and rapid determination model for the qualitative identification and quantitative analysis based on chemical structures. It is expected that this model could be extended to the NIR analysis of other physicochemical properties of fuel.

Hybrid variable selection strategy coupled with random forest (RF) for quantitative analysis of methanol in methanol-gasoline via Raman spectroscopy.

With the trend of portable and miniaturization, Raman spectrometer requires more advanced analytical methods providing more rapid and accurate analysis performance for in-situ analysis. In this work, a hybrid variable selection method based on V-WSP and variable importance measurement (VIM) coupled with random forest (RF) was used to improve the quantitative analysis performance of portable laser Raman instruments for quantitative analysis of methanol content in methanol gasoline. First, five preprocessing methods were applied to reduce the infection information in the raw spectra, respectively. Based on the spectra data processed by multivariate scattering correction (MSC), V-WSP was employed to filter the infection or redundant information in Raman spectroscopy, and 579 variables were obtained when the correlation threshold is 0.9600. Then, the variables were further eliminated by VIM. Finally, 43 variables were obtained by the V-WSP-VIM method. In data processing, out of bag (OOB) error estimation and 10-flod cross validation (CV) were applied to optimize the parameters of preprocessing methods, V-WSP, VIM and RF model. The results fully demonstrated that compared with the RF model based on raw spectra, the RF model based on V-WSP-VIM method can achieve a better prediction performance for the quantitative analysis of methanol content in methanol-gasoline, with the coefficients of determination of cross-validation (R2CV) improving from 0.9100 to 0.9662, the root mean square error of cross-validation (RMSECV) reducing from 0.0572 to 0.0365%, the coefficients of determination of prediction set (R2P) improving from 0.9214 to 0.9407, the root mean square error of prediction set (RMSEP) reducing from 0.0420 to 0.0382%, the variables reducing from 1044 to 43 and the modeling time reducing from 72.94 to 6.41 s. The results indicates that V-WSP-VIM coupled with RF is an effective method to improve the performance of portable laser Raman spectrometer for quantitative analysis of methanol content in methanol gasoline.

Determination of adulterated gasoline using fluorescence emission-excitation matrices and multivariate calibration.

Methanol gasoline, known as a new energy, has a certain degree of damage to automobile. The aim of this work was to identify and quantify the methanol in methanol gasoline using three-dimensional fluorescence spectroscopy technique combined with second order chemometric methods. Parallel factor analysis (PARAFAC) and self-weighted alternating trilinear decomposition (SWATLD) methods were used to analyse artificial samples. However, the obtained results by PARAFAC were not satisfactory. On the other hand, excellent prediction results were obtained when SWATLD model was applied, with recovery rate between 98.7 and 102.8%, and between 97.4 and 101.9% for two and three factor respectively. In order to verify the accuracy of the method, four real samples were predicted using SWATLD model with RMSEP between 0.1 µg/mL and 0.23 µg/mL.

Assessment of environmentally friendly fuel emissions from in-use vehicle exhaust: low-blend iso-stoichiometric GEM mixture as example.

Gasoline-ethanol-methanol fuel blends were formulated with the same stoichiometric air-to-fuel ratio and volumetric energy concentration as any binary ethanol-gasoline blend. When the stoichiometric blends operated in a vehicle, the time period, injector voltage, and pressure for each fuel injection event in the engine corresponded to a given stoichiometric air-to-fuel ratio, and the load was essentially constant. Three low oxygen content iso-stoichiometric ternary gasoline-ethanol-methanol fuel blends were prepared, and the properties were compared with regular-type fuel without added oxygen. One of the ternary fuels was tested using a fleet of in-use vehicles for15 weeks and compared to neat gasoline without oxygenated compounds as a reference. Only a small number of publications have compared these ternary fuels in the same engine, and little data exist on the performance and emissions of in-use spark-ignition engines. The total hydrocarbon emissions observed was similar in both fuels, in addition to the calculated ozone forming potential of the tailpipe and evaporative emissions. In ozone non-attainment areas, the original purpose for oxygenate gasolines was to decrease carbon monoxide emissions. The results suggest that the strategy is less effective than expected because there still exist a great number of vehicles that have suffered the progressive deterioration of emissions and do not react to oxygenation, while new vehicles are equipped with sophisticated air/fuel control systems, and oxygenation does not improve combustion because the systems adjust the stoichiometric point, making it insensitive to the origin of the added excess oxygen (fuel or excess air). Graphical abstract Low level ternary blend of gasoline-ethanol-methanol were prepared with the same stoichiometric air-fuel ratio and volumetric energy concentration, based on the volumetric energy density of the pre-blended components. Exhaust and evaporative emissions was compared with a blend having no oxygen in a fleet of 12 in-use vehicles. Vehicles that had suffer a normal deterioration of emissions and do not react to oxygenation, and new vehicles with more sophisticated air/fuel control systems do not improve combustion.

Development of tartaric esters as bifunctional additives of methanol-gasoline.

BACKGROUND: Methanol has become an alternative fuel for gasoline, which is facing a rapidly rising world demand with a limited oil supply. Methanol-gasoline has been used in China, but phase stability and vapor lock still need to be resolved in methanol-gasoline applications. In this paper, a series of tartaric esters were synthesized and used as phase stabilizers and saturation vapor pressure depressors for methanol-gasoline. RESULTS: The results showed that the phase stabilities of tartaric esters for methanol-gasoline depend on the length of the alkoxy group. Several tartaric esters were found to be effective in various gasoline-methanol blends, and the tartaric esters display high capacity to depress the saturation vapor pressure of methanol-gasoline. CONCLUSION: According to the results, it can be concluded that the tartaric esters have great potential to be bifunctional gasoline-methanol additives.