Discrimination of oils and fuels using a portable NIR spectrometer

Santos, Francine D.; Santos, Layla P.; Cunha, Pedro Henrique Pereira da; Borghi, Flávia T.; Romão, Wanderson; Castro, Eustáquio Vinícius Ribeiro de; Oliveira, Elcio Cruz de; Filgueiras, Paulo R.

doi:10.1016/j.fuel.2020.118854

Cited by 32 publications

(13 citation statements)

References 59 publications

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“…The predictive performance of NIR calibration models for T95 are listed in Table 2. Considering the calibration range of 305°C-372°C, all the obtained models exhibit excellent r 2 CV/P , RMSECV/RMSEP, RPD and satisfy the reproducibility requirement (7.5°C) of the reference method. 27 The comparable performance among the linear models (PCR and PLS regression) reflected the fitness of the selected spectral range and partitioned calibration/ validation sets in the T95 modelling in addition to the spectral pre-processing methods.…”

Section: Multivariate Calibrationsmentioning

confidence: 75%

“…In addition to the economic perspectives, the product properties are also vital for diesel engine technical and environmental reasons; in other words, the fuels must comply with both quality specifications and emission standards. 1,2 One of the industry pain points that constrains the optimisation of diesel blend is that conventional process control relies upon time consuming laboratory testing methods.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate calibration strategy in simultaneous determination of temperature properties of petroleum diesel by near infrared spectrometry

Othman

Zain

Low

2022

Journal of Near Infrared Spectroscopy

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It is essential to control the quality of diesel products so that they comply with relevant fuel specifications, however, the quality assessments rely upon conventional wet chemical analyses that are costly and time consuming. Rapid, simultaneous quality measurement enabling immediate online optimisation for process control and blending offers tremendous cost savings by minimising product quality give-away, shipment demurrage, tank inventory, and laboratory analysis. In this study, the use of near infrared spectroscopy and chemometrics demonstrates a straightforward workflow for simultaneous determination of the petroleum diesel’s boiling point at 95% recovery (T95), flash point (FP), cloud point (CP), and cetane index (CI) calibration development. It involved appropriate spectral region selection, calibration/validation set partition, data pre-processing, regression modelling and validation. Based on the calibration and validation results, the supervised learning models that are obtained from a combination region of 4000–4800 cm−1 on a randomly selected calibration set managed to deliver promising predictive performance in terms of coefficient of determination for prediction (r2P/T95 ≥ 0.94, r2P/FP ≥ 0.89, r2P/CP ≥ 0.89, r2P/CI ≥ 0.993), root mean square error of prediction (RMSEP (T95) ≤ 5.2°C, RMSEP (FP) ≤ 2.0°C, RMSEP (CP) ≤ 2.4°C, RMSEP (CI) ≤ 0.3), and ratio of performance deviation (RPD (T95) ≥ 3.7, RPD (FP) ≥ 3.0, RPD (CP) ≥ 2.9, RPD (CI) ≥ 11). Regardless of principal component regression or partial least square regression on either the multiplicative scattering corrected spectra or Savitzky Golay second derivative spectra, the developed models met respective ASTM reproducibility requirements, and were considered adequate for immediate quality assessment of diesel.

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Section: Multivariate Calibrationsmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Multivariate calibration strategy in simultaneous determination of temperature properties of petroleum diesel by near infrared spectrometry

Othman

Zain

Low

2022

Journal of Near Infrared Spectroscopy

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“…This technique is desirable because of its rapid, nondestructive, and low-cost nature, in addition to its potential for combination with multivariate statistical analysis. Furthermore, the NIR technique can be integrated into portable devices that can be used directly for online monitoring or off-site operations. , Indeed, a combination of NIR spectroscopy and multivariate analysis is often used to determine the chemical and physical properties of fuels and their components. − …”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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“…To solve this problem, some rapid analytical methods for measuring ethanol content in gasoline have been developed, for example, Raman spectroscopy 7 , 8 and Near-infrared (NIR) spectroscopy. 9 , 10 NIR is a fast, non-destructive analytical method that consumes small amounts of reagents. 11 − 14 NIR spectroscopy combined with multivariate statistical analytical methods, such as partial least squares or principal component analysis, has been widely used in rapid analysis of ethanol content in gasoline.…”

Section: Introductionmentioning

confidence: 99%

“…However, this method has disadvantages, including long analysis times and the inability to meet the needs of on-site analysis and real-time detection due to bulky instruments that cannot be easily carried and moved around for analysis. To solve this problem, some rapid analytical methods for measuring ethanol content in gasoline have been developed, for example, Raman spectroscopy , and Near-infrared (NIR) spectroscopy. , NIR is a fast, non-destructive analytical method that consumes small amounts of reagents. − NIR spectroscopy combined with multivariate statistical analytical methods, such as partial least squares or principal component analysis, has been widely used in rapid analysis of ethanol content in gasoline. Mabood et al, using partial least squares and principal component analysis, established a rapid analytical method to determine ethanol content in gasoline using NIR spectroscopy, achieving a low root mean square error of prediction (RMSEP).…”

Section: Introductionmentioning

confidence: 99%

Selection of the Effective Characteristic Spectra Based on the Chemical Structure and Its Application in Rapid Analysis of Ethanol Content in Gasoline

Zhang

et al. 2022

ACS Omega

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Near-infrared (NIR) spectroscopy analysis is one of the most rapid detection methods for determining ethanol content in gasoline. Wavelength selection is a key step in the multivariate calibration analysis of NIR spectroscopy. To improve detection accuracy of ethanol content in gasoline and provide a simpler interpretation, we established NIR spectroscopy, a rapid analysis method based on the effective characteristic spectra. Five effective characteristic spectral bands were used according to the molecular structure of ethanol, followed by the development of four modeling schemes. The four modeling schemes spectra, NIR full spectra, and variable importance projection (VIP) spectra were used for modeling and analysis. The model was established based on the effective characteristic spectra without the interference spectra of aromatic hydrocarbons, achieving the best model performance. In addition, the model was further evaluated by internal cross-validation and external validation. The model’s evaluation parameters were as follows: the root mean square error of cross-validation (RMSECV) was 0.6193, the correlation coefficient of internal cross-validation ( R CV 2 ) was 0.9995, the root mean square error of prediction (RMSEP) was 0.5572, and the correlation coefficient of external prediction validation ( R P 2 ) was 0.9995. The effective characteristic spectra model had smaller RMSEP and RMSECV values, and larger R CV 2 and R P 2 values compared to the full spectra and VIP spectra models. In conclusion, the effective characteristic spectra model had the highest accuracy and could provide rapid analysis of the ethanol content in gasoline.

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Discrimination of oils and fuels using a portable NIR spectrometer

Cited by 32 publications

References 59 publications

Multivariate calibration strategy in simultaneous determination of temperature properties of petroleum diesel by near infrared spectrometry

Multivariate calibration strategy in simultaneous determination of temperature properties of petroleum diesel by near infrared spectrometry

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

Selection of the Effective Characteristic Spectra Based on the Chemical Structure and Its Application in Rapid Analysis of Ethanol Content in Gasoline

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