Rapid Determination of Physical and Chemical Parameters of Reformed Gasoline by Near-Infrared (NIR) Spectroscopy Combined with the Monte Carlo Virtual Spectrum Identification Method

Li, Jingyan; Xiaoli, Chu

doi:10.1021/acs.energyfuels.8b00854

Cited by 22 publications

(2 citation statements)

References 13 publications

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“…The KPLS method also showed good prediction performance in modeling the PIONA content in gasoline. This method was used to model the PIONA content in gasoline, and achieved good prediction results [25].…”

Section: Nir Model Development For Predicting Hbo Propertiesmentioning

confidence: 99%

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

Long

Fan

2023

Processes

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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.

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Section: Nir Model Development For Predicting Hbo Propertiesmentioning

confidence: 99%

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

Long

Fan

2023

Processes

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“…ASTM D2699 (for RON) and ASTM D2700 (for MON) provide the standard methods for determining the ON of fuels, which are rigorous and complicated experiments conducted with ON testers by means of the procedure settings in the two standards. − Besides the standard methods, several electrochemical, chromatographic, and spectroscopic methods have been proposed − for the determination of ON. Although these methods were simpler than ASTM standard methods, experimentally determining the ON of fuels, by and large, is inefficient and sometimes dangerous. ,,− It is still necessary to set up more reliable, easy-to-use, and cost-effective methodologies for determining the ON of fuels.…”

Section: Introductionmentioning

confidence: 99%

QSPR Studies on the Octane Number of Toluene Primary Reference Fuel Based on the Electrotopological State Index

Jiao

Liu

et al. 2020

ACS Omega

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The quantitative structure–property relationship (QSPR) models for predicting the octane number (ON) of toluene primary reference fuel (TPRF; blends of n-heptane, isooctane, and toluene) was investigated. The electrotopological state (E-state) index of TPRF components was computed and weight-summed to generate the quantitative descriptor of TPRF samples. The partial least squares (PLS) technique was used to build up the regression model between the ON and weight-summed E-state index of the investigated samples. The QSPR models for the research octane number (RON) and motor octane number (MON) of TPRF were built. The prediction performance of the obtained PLS models was assessed by the external test set validation and leave-one-out cross-validation. The validation results demonstrate that the proposed PLS models are feasible for predicting the ON, both RON and MON, of TPRF. In addition, several other QSPR models for the ON of TPRF were developed by employing the stepwise regression and Scheffé polynomials methods, and the prediction performance of these models were compared with that of the PLS models. The comparison result shows that the proposed PLS models are slightly better than multiple linear regression models and Scheffé models. It is demonstrated that the combination of the E-state index and PLS is an easy-to-use and promising method for studying and forecasting the ON of TPRF.

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Pattern Recognition Methods

Bian

2022

Chemometric Methods in Analytical Spectroscopy Technology

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Rapid Determination of Physical and Chemical Parameters of Reformed Gasoline by Near-Infrared (NIR) Spectroscopy Combined with the Monte Carlo Virtual Spectrum Identification Method

Cited by 22 publications

References 13 publications

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

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

QSPR Studies on the Octane Number of Toluene Primary Reference Fuel Based on the Electrotopological State Index

Pattern Recognition Methods

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