Relative hydrogen index as a fast method for the simultaneous determination of physicochemical properties of petroleum fractions

Barbosa, Lúcio L.; Montes, Luciana F.; Kock, Flávio Vinícius Crizóstomo; Morgan, Vinícius G.; Souza, André Lopes de; Song, Yi‐Qiao; Castro, Eustáquio R.V.

doi:10.1016/j.fuel.2017.08.057

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…Although TD-NMR is considered a potential technique for the on-line monitoring of liquid fuels at all stages of processing from feedstock to the final product, ,,,, most of the reviewed applications described in Table were made off-line. TD-NMR technology is not often used as an on-line method in industrial environments because of technical challenges of this method, such as the time needed for the premagnetization of the sample and its low sensitivity. ,, However, several examples of on-line and real-time in situ monitoring by TD-NMR have been presented.…”

Section: Reviewmentioning

confidence: 99%

“…Applied to the T 2 and T 1 decays, the Γ distribution model was named as a better alternative to the ILT and a promising approach for data analytics for quality control by Forster et al 43 On-Line Applications. Although TD-NMR is considered a potential technique for the on-line monitoring of liquid fuels at all stages of processing from feedstock to the final product, 28,29,39,48,50 most of the reviewed applications described in Table 1 were made off-line. TD-NMR technology is not often used as an on-line method in industrial environments because of technical challenges of this method, such as the time needed for the premagnetization of the sample and its low sensitivity.…”

Section: ■ Introductionmentioning

confidence: 99%

“…49 Finally, it should be emphasized that one measurement of NMR magnetization decay provides the possibility to determine a wide set of important physical and chemical properties (Table 1). For instance, Barbosa et al 50 estimated five properties of crude oil distillates by using only one parameter, RHI, calculated from the FID. Moreover, by knowledge of a wide spectrum of parameters, the quality of the liquid fuel can be determined more generally, 26 and in the case of its further processing, the properties of the final product can be predicted.…”

Section: ■ Introductionmentioning

confidence: 99%

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Time-Domain NMR in Characterization of Liquid Fuels: A Mini-Review

Nikolskaya

Hiltunen

2020

Energy Fuels

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Low-field nuclear magnetic resonance (LF-NMR) is a highly promising method for characterizing the different physical parameters and chemical structures of liquid fuels. In particular, there have been a variety of essential applications by means of time-domain NMR (TD-NMR). These applications can be developed for the characterization of petroleum fuels and biofuels, reaction monitoring, or process and quality control of liquid fuels. However, only a few applications have demonstrated the use of TD-NMR via on-line measurements. The principle of the majority of reviewed TD-NMR applications is based on finding the correlation between NMR and physical parameters. Special attention in this mini-review is paid to the description of on-line TD-NMR applications. ■ REVIEWAn overview of the LF-NMR analyses of crude oils, lubricant oils, diesel/biodiesel fuels, and edible oils has recently been published by Rudszuck et al., 21 in which both TD-NMR and NMR spectroscopy were taken into account. In that review, papers were categorized on the basis of their processing stage,

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Section: Reviewmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

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Time-Domain NMR in Characterization of Liquid Fuels: A Mini-Review

Nikolskaya

Hiltunen

2020

Energy Fuels

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“…Although density is key to evaluate resources, viscosity is the property that most affects productivity and heavy oil recovery [2] [3]. Moreover, these properties are directly involved in the optimization of oil refining operations, the determination and quality control of derivatives, the estimation of transport and storage conditions [4], the development of reservoir simulation processes [1], and the selection of a suitable oil recovery. However, measurements with conventional techniques take time, usually requiring tedious and expensive experimental processes, where accuracy decreases as oil viscosity increases, as equipment designs and protocols are focused on light and medium oils [5] [4] .…”

Section: Introductionmentioning

confidence: 99%

Low field NMR as an alternative technique to estimate of density and viscosity in toluene-heavy oil mixtures

et al. 2021

CT&F Cienc. Tecnol. Futuro

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The success of low field Nuclear Magnetic Resonance (LF-NMR) to estimate heavy oil properties depends on a good selection of mathematical models and fitting parameters. Since the correlations proposed are not universally applicable, in this study, a NMR published model was chosen and tuned to determine the density and viscosity of several mixtures of a Colombian heavy oil with toluene. The process began by mixing toluene with heavy oil to obtain several measuring points with properties similar to those of heavy oils. Each mixture was taken to a 7.5 MHz spectrometer at 40°C, where NMR parameters were acquired and used in the five pre-selected mathematical models. The reliability of viscosity measurements was analysed with the root mean square error (RMSE) and maximum absolute error (MAE). After the NLS regression process, the most accurate prediction was reached through the Burcaw model, with RMSE values of 40.55 cP. On the other hand, the density was estimated with the Wen correlation with results showing a relative error percentage of less than 1%. According to such error values, the tuned models are considered a starting point to extend the NRM technique use to other Colombian heavy oils with low uncertainty levels.

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“…One potential application is in fuel processing, where there has been a surge for the last few years in the development of fast methods for the characterization of petroleum fractions by LF-NMR . Among many studied physicochemical properties, the oil viscosity was found to be of the principal importance in determining the rate of interaction with fuel during combustion processes in internal combustion engines. , In these studies, the LF-NMR predictive models were typically derived using multivariate calibration with partial least squares (PLS) regression or artificial neural networks (ANN), which proved to be the right approach in most cases.…”

Section: Introductionmentioning

confidence: 99%

Improved Oil Viscosity Characterization by Low-Field NMR Using Feature Engineering and Supervised Learning Algorithms

et al. 2020

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Conventional methods for determining and monitoring the viscosity of oils are time-consuming, expensive, and in some instances, technically unfeasible. These limitations can be avoided using low-field nuclear magnetic resonance (LF-NMR) relaxometry. However, due to the chemical dissimilarity of oils and various temperatures these oils are exposed to, as well as LF-NMR equipment limitations, the commonly used models fail to perform at a satisfactory level, making them impractical for use in heavy oil and bitumen reservoirs and in environments with large temperature oscillations (e.g., mechanical systems). We present a framework that combines supervised learning algorithms with domain knowledge for synthesizing new features to improve model forecasts using only one NMR parameterT 2 geometric mean. Two principal methods were considered, support vector regression (SVR) and gradient boosted trees (GBRT). Models were trained using the experimental data from our previous studies and literature data combining conventional oils, heavy oils, and bitumens from various reservoirs in Canada and United States. The models’ performance was compared against four other intelligent algorithms and four well-known empirical NMR models against which the SVR- and GBRT-based models achieved the highest statistical scores. These two models can be used for oil viscosity prediction in conventional and heavy oil reservoirs with a wide range of oil viscosities and in situations where high precision is needed, such as in the determination of viscosity of petroleum distillates or for monitoring of oil viscosity in mechanical systems. The proposed framework can also be applied to determine other physicochemical properties of oils by LF-NMR, where the application of supervised learning is usually impractical due to the limited volume of experimental data.

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Relative hydrogen index as a fast method for the simultaneous determination of physicochemical properties of petroleum fractions

Cited by 11 publications

References 12 publications

Time-Domain NMR in Characterization of Liquid Fuels: A Mini-Review

Time-Domain NMR in Characterization of Liquid Fuels: A Mini-Review

Low field NMR as an alternative technique to estimate of density and viscosity in toluene-heavy oil mixtures

Improved Oil Viscosity Characterization by Low-Field NMR Using Feature Engineering and Supervised Learning Algorithms

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