Mid- infrared uncooled sensor for the identification of pure fuel, additives and adulterants in gasoline

Maldonado, María Dolores Sánchez; Elorza, Pilar Barreiro; Gutiérrez, Raúl; Vergara, G.

doi:10.1016/j.fuproc.2017.09.018

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…Rapid oil property detection methods − include near-infrared (NIR), mid-infrared, nuclear magnetic resonance, and Raman spectroscopy. As a rapid analysis technique, NIR spectroscopy has the advantage of simple, fast, and non-destructive characteristics that make it an important detection method in complicated real processes .…”

Section: Introductionmentioning

confidence: 99%

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

et al. 2022

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With the increasing challenges in environmental protection, product quality, and market profit, the development of a feasible optimization approach coupling accurate oil property online prediction has become crucial to the intelligent production of refineries. However, traditional property modeling with near-infrared (NIR) spectroscopy cannot dynamically extract the synergistic effect between wavelengths and cause inaccurate property prediction. The success rate of the data-driven robust optimization (DDRO) of the refining process is influenced by process uncertainty. Thus, we proposed a modified hybrid strategy on online NIR prediction modeling with adjustable characteristic wavelength selection and DDRO under uncertainty for the refining process. An adjustable feature space variable extraction (AFSVE) method was first proposed to dynamically select the characteristic wavelengths for constructing accurate property prediction models. A data-driven robust optimization of gasoline blending model was rendered through dual transformation using the uncertainty set of oil property derived from principal component analysis combined with robust kernel density estimation. An industrial application showed the best prediction accuracy of property models with the AFSVE method, and a high blending success rate was obtained by the proposed modified hybrid strategy, which hedges against uncertainties, including measuring error and blending effect, and boosts environmentally friendly and intelligent process operation.

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Section: Introductionmentioning

confidence: 99%

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

et al. 2022

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“…Cost-reduction can be achieved by designing a specialized spectrometer "around the required wavelengths" by omitting all unnecessary components. Such low-cost spectrometers proved already to be potentially suitable for analysis of chemical compositions by means of different measuring techniques [22,23]. However, typically they provide lower signal-to-noise ratio and resolution [23,24].…”

Section: Introductionmentioning

confidence: 99%

“…Such low-cost spectrometers proved already to be potentially suitable for analysis of chemical compositions by means of different measuring techniques [22,23]. However, typically they provide lower signal-to-noise ratio and resolution [23,24]. This downside is frequently overcome by the benefit provided by quick real-time information and can partly be compensated by multivariate data analysis (MVA).…”

Section: Introductionmentioning

confidence: 99%

A Process Analytical Concept for In-Line FTIR Monitoring of Polysiloxane Formation

et al. 2020

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The chemical synthesis of polysiloxanes from monomeric starting materials involves a series of hydrolysis, condensation and modification reactions with complex monomeric and oligomeric reaction mixtures. Real-time monitoring and precise process control of the synthesis process is of great importance to ensure reproducible intermediates and products and can readily be performed by optical spectroscopy. In chemical reactions involving rapid and simultaneous functional group transformations and complex reaction mixtures, however, the spectroscopic signals are often ambiguous due to overlapping bands, shifting peaks and changing baselines. The univariate analysis of individual absorbance signals is hence often only of limited use. In contrast, batch modelling based on the multivariate analysis of the time course of principal components (PCs) derived from the reaction spectra provides a more efficient tool for real-time monitoring. In batch modelling, not only single absorbance bands are used but information over a broad range of wavelengths is extracted from the evolving spectral fingerprints and used for analysis. Thereby, process control can be based on numerous chemical and morphological changes taking place during synthesis. “Bad” (or abnormal) batches can quickly be distinguished from “normal” ones by comparing the respective reaction trajectories in real time. In this work, FTIR spectroscopy was combined with multivariate data analysis for the in-line process characterization and batch modelling of polysiloxane formation. The synthesis was conducted under different starting conditions using various reactant concentrations. The complex spectral information was evaluated using chemometrics (principal component analysis, PCA). Specific spectral features at different stages of the reaction were assigned to the corresponding reaction steps. Reaction trajectories were derived based on batch modelling using a wide range of wavelengths. Subsequently, complexity was reduced again to the most relevant absorbance signals in order to derive a concept for a low-cost process spectroscopic set-up which could be used for real-time process monitoring and reaction control.

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“…In this context, the development of analytical methods to corroborate the quality and authenticity of fuels used in Brazil is quite necessary. Various analytical techniques have been reported for the determination of fuel adulteration, like electrochemical techniques, [16][17][18] infrared spectroscopy, [19][20][21][22][23][24][25][26][27][28][29] Raman spectroscopy, [30][31][32][33][34] proton nuclear magnetic resonance spectroscopy ( 1 H NMR), 35,36 chemometrics analysis, 30,37,38 gas chromatography (GC), [38][39][40][41][42][43] comprehensive two-dimensional gas chromatography, [44][45][46] high-performance liquid chromatography (HPLC), [47][48][49] and distillation curves. 50,51 Avila et al 47 developed an HPLC method for the selective quantification of ethanol in gasoline.…”

Section: Introductionmentioning

confidence: 99%

Determination of Methanol in Gasoline and Ethanol Fuels by High-Performance Liquid Chromatography

Dias¹,

Santos²,

Carvalho³

et al. 2020

J. Braz. Chem. Soc.

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Gasoline and ethanol fuels have been adulterated with methanol in Brazil. Methanol is not permitted in concentrations greater than 0.5 vol% due its toxicity but its lower price stimulates the adulteration. A new approach for quantitative analysis of methanol in gasoline and ethanol fuels is reported employing high-performance liquid chromatography coupled with refractive index (RI) detector on C18 column (250 × 4.6 mm, 5 µm) using deionized water as mobile phase (0.6 mL min −1 ). The method showed good analytical performance in terms of linearity for methanol concentration ranging from 0.5 to 4.5 vol% (coefficient of determination (R 2 ) = 0.999) and from 4.0 to 12.0 vol% (R 2 = 0.998). The recoveries (accuracy) values ranged from 98.6 to 103.2%. The results indicated that the developed method is accurate and suitable for the determination of methanol in gasoline with ethanol and ethanol fuel as an alternative procedure to gas chromatography (GC)-based techniques.

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Mid- infrared uncooled sensor for the identification of pure fuel, additives and adulterants in gasoline

Cited by 11 publications

References 13 publications

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

A Process Analytical Concept for In-Line FTIR Monitoring of Polysiloxane Formation

Determination of Methanol in Gasoline and Ethanol Fuels by High-Performance Liquid Chromatography

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