Rapid and accurate determination of diesel multiple properties through NIR data analysis assisted by machine learning

Practical Online Characterization of the Properties of Hydrocracking Bottom Oil via Near-Infrared Spectroscopy

Providing real-time information on the chemical properties of hydrocracking bottom oil (HBO) as the feedstock for ethylene cracker while minimizing processing time, is important to improve the real-time optimization of ethylene production. In this study, a novel approach for estimating the properties of HBO samples was developed on the basis of near-infrared (NIR) spectra. The main noise and extreme samples in the spectral data were removed by combining discrete wavelet transform with principal component analysis and Hotelling’s T2 test. Kernel partial least squares (KPLS) regression was utilized to account for the nonlinearities between NIR data and the chemical properties of HBO. Compared with the principal component regression, partial least squares regression, and artificial neural network, the KPLS model had a better performance of obtaining acceptable values of root mean square error of prediction (RMSEP) and mean absolute relative error (MARE). All RMSEP and MARE values of density, Bureau of Mines correlation index, paraffins, isoparaffins, and naphthenes were less than 1.0 and 3.0, respectively. The accuracy of the industrial NIR online measurement system during consecutive running periods in predicting the chemical properties of HBO was satisfactory. The yield of high value-added products increased by 0.26 percentage points and coil outlet temperature decreased by 0.25 °C, which promoted economic benefits of the ethylene cracking process and boosted industrial reform from automation to digitization and intelligence.

Rapid determination of viscosity and viscosity index of lube base oil based on near-infrared spectroscopy and new transformation formula

Viscosity and viscosity index are the key product properties in lubricating oil production process. Rapid and even online analysis of viscosity and viscosity index through near-infrared (NIR) spectroscopy combined with chemometrics is helpful to optimize the production process. However, due to the nonlinear effect, the commonly used linear multivariate correction method is not effective. In this work, the feasibility of four existing viscosity linear transformation formulas for establishing NIR models was studied, and a new viscosity linear transformation formula was developed based on the viscosity-gravity constant. The experimental results showed that three of the four existing viscosity linear transformation formulas made some improvement on the viscosity prediction of base oil, but not as good as the newly established viscosity linear transformation formula. For viscosity index, the accuracy of modeling with reference viscosity index directly was much better than calculating by prediction viscosity value. Both of the viscosity and viscosity index prediction results of NIR analysis were in good agreement with the results of reference method, indicating that the determination can meet the needs of rapid and on-line analysis in industrial field.

Near-Infrared Spectroscopy as a Tool for Simultaneous Determination of Diesel Fuel Improvers

Diesel and biodiesel blends requires additives to improve fuel quality properties and engine performance. Diesel improvers are added before, during and/or after the fuel is blended. However, no accurate rapid and non-destructive analytical method is used during the fuel production that could determine the exact concentration of various types of improvers in diesel fuel. Thus, the aim of this study was to determine the concentration of several improvers in diesel matrices at the same time. Three types of diesel improvers, i.e., a cold-flow improver (CFI), a conductivity-lubricity improver (CLI), and a cetane number improver (CNI), were simultaneously determined by near-infrared (NIR) spectroscopy combined with multivariate statistical analysis and the partial least squares algorithm. The prediction models yielded high correlation coefficients (R ²) >0.99 and satisfactory values of the root mean square error of calibration as follows: CLI 4.2 (mg·kg^-1), CFI 4.6 (mg·kg^-1), and CNI 5.3 (mg·kg^-1). The residual standard deviation of the repeatability was calculated to be around 8%. These results highlight the potential of NIR spectroscopy for use as a fast, low-cost, and efficient tool to determine the concentrations of diesel improvers. Moreover, this technique is suitable for application during refinery production, especially for the purpose of online monitoring to prevent overdoses of additives and save financial expenses.

Fast detection of volatile fatty acids in biogas slurry using NIR spectroscopy combined with feature wavelength selection

To analyze the state of anaerobic digestion (AD), fast detection models of volatile fatty acids (VFAs) were constructed using near-infrared transmission spectroscopy combined with partial least squares regression to measure concentrations of the acetic acid (AA), propionic acid (PA) and total acid (TA) in biogas slurry. CARS-SA-BPSO algorithm was proposed based on competitive adaptive reweighted sampling (CARS) and simulated annealing binary particle swarm optimization algorithm (SA-BPSO) for selecting feature wavelengths of the AA, PA and TA. Regression models were established with the determination coefficient of prediction (R_p²) of 0.989, root mean squared error of prediction (RMSEP) of 0.111 and residual predictive deviation (RPD) of 9.706 for AA; R_p² of 0.932, RMSEP of 0.116 and RPD of 3.799 for PA; R_p² of 0.895, RMSEP of 0.689 and RPD of 3.676 for TA. It is sufficient to meet the fast detection needs of the AA and PA concentrations in biogas slurry, and basically meet the measuring demand of the TA concentration. CARS-SA-BPSO effectively improves the performance of the calibration model using sensitive wavelength selections, which provides theoretical support for establishing the spectral quantitative regression model to meet the requirements of practical application.

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an R_P² of 0.9304 and an RMSEP of 0.0458 g·g⁻¹. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.