Determination of physicochemical properties of biodiesel and blends using low-field NMR and multivariate calibration

Constantino, André F.; Cubides-Román, Diana C.; Santos, Reginaldo Bezerra dos; Queiroz, Luiz Henrique Keng; Colnago, Luiz Alberto; Neto, Álvaro Cunha; Barbosa, Lúcio L.; Romão, Wanderson; Castro, Eustáquio Vinícius Ribeiro de; Filgueiras, Paulo R.; Lacerda, Valdemar

doi:10.1016/j.fuel.2018.10.045

Cited by 21 publications

(12 citation statements)

References 46 publications

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“…8 The cetane number, viscosity, energy content, and phasechange properties of biodiesel are similar to those of petroleum-based diesel. 9,10 Biodiesel (usually mono alkyl ester of long-chain fatty acid) is commonly produced from triglyceride (T) by transesterification of vegetable oil or animal fats with low-molecular-weight alcohols in the presence of a soluble catalyst 11 and in some cases over solid catalyst. 12,13 In transesterification, an alkaline catalyst is readily used due to mild operating condition requirement and low cost.…”

Section: Introductionmentioning

confidence: 99%

“…Biodiesel is a green fuel and is considered a renewable and environmentally friendly energy resource. − It is biodegradable, sulfur-free, and nontoxic with high oxygen content. − Biodiesel has many environmental benefits, such as reduced greenhouse effect, reduced pollution of air, water, and soil, and reduced health risk when compared to the use of fossil fuels . The cetane number, viscosity, energy content, and phase-change properties of biodiesel are similar to those of petroleum-based diesel. , Biodiesel (usually mono alkyl ester of long-chain fatty acid) is commonly produced from triglyceride (T) by transesterification of vegetable oil or animal fats with low-molecular-weight alcohols in the presence of a soluble catalyst and in some cases over solid catalyst. , In transesterification, an alkaline catalyst is readily used due to mild operating condition requirement and low cost . Methanol (M) is most frequently used as an alcohol reactant of the transesterification reaction because it is reactive and inexpensive.…”

Section: Introductionmentioning

confidence: 99%

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Two-Scale Model for Kinetics, Design, and Scale-Up of Biodiesel Production

Bannatham

Banthaothook

Limtrakul

et al. 2021

Ind. Eng. Chem. Res.

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Optimization of biodiesel production requires fundamentals-based kinetic and reactor models. A liquid–liquid reactor requires reaction rate model including mass-transfer effects. Earlier studies have not analyzed such an effect in detail and thus cannot be used for scale-up and design. This work develops a two-scale model for design and scale-up. The intrinsic rate constant, fit from experimental data, is approximately 300 times larger than the apparent one. The model results for microchannel and stirred-batch reactors agree well with the experimental data and indicate that the former requires approximately 720 times shorter residence time than the latter. Scaling-up of an agitated-batch reactor to industrial scale by keeping tip speed constant provides lower performance due to inadequate power. In contrast, scaling-up with constant power/volume provides higher performance due to oversupply power. The two-scale model is used to adjust agitation speed for achieving performance similarity during scale-up. The corresponding power lies in between the two above-mentioned values.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-Scale Model for Kinetics, Design, and Scale-Up of Biodiesel Production

Bannatham

Banthaothook

Limtrakul

et al. 2021

Ind. Eng. Chem. Res.

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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. However, in nonlinear datasets, the reports in the literature show that PLS did not provide satisfactory accuracy, whereas ANN tended to overfit the data, thus leading to poor model generalization. , In the LF-NMR examination of petroleum fractions, this nonlinearity can occur due to their chemical intricacy, which ultimately leads to the degradation of model forecasting performance…”

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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“…However, the methods commonly used such as mercury intrusion porosimetry (MIP), N 2 adsorption desorption [32], and small angle scattering may cause some damage to the sample matrix. Compared with other methods, nuclear magnetic resonance (NMR) technology has a faster and more accurate representation of the pore distribution in the sample without damaging it, it has been widely used by some researchers to study the size, content, and distribution of pores [33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Sealing Performance of New Solidified Materials: Mechanical Properties and Stress Sensitivity Characterization of Pores

Zhang

Jin

Liu

et al. 2020

Advances in Polymer Technology

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Borehole-sealing solidified material plays a significant role in improving sealing quality and enhancing gas drainage performance. In this study, the MTS815 electro-hydraulic triaxial servo test system and MR-60 NMR test system were adopted to conduct triaxial compression control experiment on the coal sample material, concrete material, and new solidified sealing material, respectively. This paper aims to analyze the difference of support effects, porosity, and stress sensitivity between those materials. Experimental results show that under the same stress condition, the stiffness of traditional concrete solidified material is the largest, while the new solidified material is the second, and the coal sample material is the smallest. Compared with the traditional concrete solidified material, the new solidified sealing material has better strain-bearing capacity and volumetric expansion capacity under each confining pressure in the experiment. The axial strain and volume increment of new solidified material is higher than those of the traditional concrete solidified material at the peak stress. Meanwhile, the confining pressure has a certain hysteresis effect on the postpeak stress attenuation. Fracture has the strongest stress sensitivity in three pore types, and its T2 map relaxation area has a larger compression than adsorption pore and seepage pore under the same pressure. The relative content of seepage pore and fracture in the new solidified material is less than that of coal and concrete samples, and the stress sensitivity of the new solidified materials is weaker than that of coal and concrete materials, thence, new solidified material will have better performance in borehole sealing. Outcomes of this study could provide guidance on the selection of the most effective sealing materials for sealing-quality improvement.

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Determination of physicochemical properties of biodiesel and blends using low-field NMR and multivariate calibration

Cited by 21 publications

References 46 publications

Two-Scale Model for Kinetics, Design, and Scale-Up of Biodiesel Production

Two-Scale Model for Kinetics, Design, and Scale-Up of Biodiesel Production

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

Sealing Performance of New Solidified Materials: Mechanical Properties and Stress Sensitivity Characterization of Pores

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