Data driven models exploring the combination of NIR and 1H NMR spectroscopies in the determination of gasoline properties

Leal, Ana L.; Silva, Artur M. S.; Ribeiro, Jorge C.; Martins, F.G.

doi:10.1016/j.microc.2022.107217

Cited by 7 publications

(4 citation statements)

References 47 publications

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“…Furthermore, data-driven approaches and machine learning (ML) techniques for chemical mechanisms have become powerful tools, , increasingly finding their way also into applications for combustion chemistry problems. − Since solving inverse problems is often computationally prohibitively costly, ML approaches can be a useful alternative . Appropriate data sets can be searched automatically for specific features and correlations; from this information, prediction of certain properties may be possible. , Such predictions can rely on data used for training purposes that are not part of the generated numerical model .…”

Section: Combustion Chemistry Methodologymentioning

confidence: 99%

“…Data-driven approaches are being used in an increasing number of combustion-related contexts, including, for example, the assessment of fuel properties on the basis of infrared (IR) and nuclear magnetic resonance (NMR) spectra, − ignition delay times, laminar burning velocities, ionization cross sections, species profiles, and information for mechanism reduction in turbulent environments. ,, …”

Section: Combustion Chemistry Methodologymentioning

confidence: 99%

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Combustion, Chemistry, and Carbon Neutrality

Kohse‐Höinghaus

2023

Chem. Rev.

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Combustion is a reactive oxidation process that releases energy bound in chemical compounds used as fuels�energy that is needed for power generation, transportation, heating, and industrial purposes. Because of greenhouse gas and local pollutant emissions associated with fossil fuels, combustion science and applications are challenged to abandon conventional pathways and to adapt toward the demand of future carbon neutrality. For the design of efficient, low-emission processes, understanding the details of the relevant chemical transformations is essential. Comprehensive knowledge gained from decades of fossil-fuel combustion research includes general principles for establishing and validating reaction mechanisms and process models, relying on both theory and experiments with a suite of analytic monitoring and sensing techniques. Such knowledge can be advantageously applied and extended to configure, analyze, and control new systems using different, nonfossil, potentially zero-carbon fuels. Understanding the impact of combustion and its links with chemistry needs some background. The introduction therefore combines information on exemplary cultural and technological achievements using combustion and on nature and effects of combustion emissions. Subsequently, the methodology of combustion chemistry research is described. A major part is devoted to fuels, followed by a discussion of selected combustion applications, illustrating the chemical information needed for the future.

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Section: Combustion Chemistry Methodologymentioning

confidence: 99%

Section: Combustion Chemistry Methodologymentioning

confidence: 99%

Combustion, Chemistry, and Carbon Neutrality

Kohse‐Höinghaus

2023

Chem. Rev.

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“…30 It has also been used to study environmental contamination related to nonaqueous phase liquids, 31 perfluorooctanoic acid degradation, 32 and the leaching of plasticizers. 33 Low-field NMR has further been used to study gasoline 34,35 and biofuel production, [36][37][38][39][40] as well as lignin properties. 41,42 However, in general, the development of low-field NMR for complex sample analysis, especially in the environmental sciences, is still in its infancy.…”

Section: Low-field Nmr Spectroscopy Applicationsmentioning

confidence: 99%

Low‐field, not low quality: 1D simplification, selective detection, and heteronuclear 2D experiments for improving low‐field NMR spectroscopy of environmental and biological samples

Downey,

Bermel,

Soong

et al. 2023

Magnetic Reson in Chemistry

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Understanding environmental change is challenging and requires molecular‐level tools to explain the physicochemical phenomena behind complex processes. Nuclear magnetic resonance (NMR) spectroscopy is a key tool that provides information on both molecular structures and interactions but is underutilized in environmental research because standard “high‐field” NMR is financially and physically inaccessible for many and can be overwhelming to those outside of disciplines that routinely use NMR. “Low‐field” NMR is an accessible alternative but has reduced sensitivity and increased spectral overlap, which is especially problematic for natural, heterogeneous samples. Therefore, the goal of this study is to investigate and apply innovative experiments that could minimize these challenges and improve low‐field NMR analysis of environmental and biological samples. Spectral simplification (JRES, PSYCHE, singlet‐only, multiple quantum filters), selective detection (GEMSTONE, DREAMTIME), and heteronuclear (reverse and CH3/CH2/CH‐only HSQCs) NMR experiments are tested on samples of increasing complexity (amino acids, spruce resin, and intact water fleas) at‐high field (500 MHz) and at low‐field (80 MHz). A novel experiment called Doubly Selective HSQC is also introduced, wherein 1H signals are selectively detected based on the 1H and 13C chemical shifts of 1H–13C J‐coupled pairs. The most promising approaches identified are the selective techniques (namely for monitoring), and the reverse and CH3‐only HSQCs. Findings ultimately demonstrate that low‐field NMR holds great potential for biological and environmental research. The multitude of NMR experiments available makes NMR tailorable to nearly any research need, and low‐field NMR is therefore anticipated to become a valuable and widely used analytical tool moving forward.

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“…When coupled with multivariate analysis, it has shown to be a successful tool in a range of applications, 4 such as in pharmaceutic industries for quantitative determination of the active ingredient composition of solid drug formulations 5,6 and petrochemical for determination of research octane number (RON), alcohols, phenols, and density amongst other properties. [7][8][9] Since wood is a complex material, NIR spectra of wood surfaces are impacted, not only by chemistry, but also by its structure. 10 The NIR spectra of wood are composed of multiple and frequently overlapping bands attributed to its chemical constituents, namely: lignin, cellulose, hemicellulose, and extractives.…”

Section: Introductionmentioning

confidence: 99%

Improving feedstock quality control in formaldehyde-based resin and wood-based panel production through near infrared spectroscopy

Magalhães,

Paiva,

Ferra

et al. 2024

Journal of Near Infrared Spectroscopy

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To assure the quality control of industrial processes, it is important to adopt reproducible and efficient methodologies. Spectroscopic methods, such as near infrared (NIR), are a good option as they are fast and may be used to indirectly estimate multiple physicochemical properties. In this study, NIR spectra of key feedstock samples used in the production of formaldehyde-based resin and wood-based panels (WBP), namely urea, ammonium sulfate, ammonium nitrate, sodium hydroxide, and acetic acid, were acquired. Multivariate data analysis was applied to establish the correlation between the spectra and the properties being measured. Quantitative models were then created using partial least squares (PLS) regression to predict the concentrations of feedstock samples. This study presents quantitative models that were created by combining spectra measured on two probes, which achieved similar prediction results as single-probe based models. The performances of the best models were compared with the reference methods for each of the evaluated samples. For the samples under study, the proposed approach is suitable for routine analysis across multiple equipment configurations using the same quantitative model. NIR spectroscopy combined with chemometric models could be a valuable complement to support in-line raw material monitoring and plant digitalization in the wood panels industry.

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Data driven models exploring the combination of NIR and 1H NMR spectroscopies in the determination of gasoline properties

Cited by 7 publications

References 47 publications

Combustion, Chemistry, and Carbon Neutrality

Combustion, Chemistry, and Carbon Neutrality

Low‐field, not low quality: 1D simplification, selective detection, and heteronuclear 2D experiments for improving low‐field NMR spectroscopy of environmental and biological samples

Improving feedstock quality control in formaldehyde-based resin and wood-based panel production through near infrared spectroscopy

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