Review on Application of Nanotechnology for Asphaltene Adsorption, Crude Oil Demulsification, and Produced Water Treatment

Farooq, Umer; Patil, Amit; Panjwani, Balram; Simonsen, Galina

doi:10.1021/acs.energyfuels.1c01990

Cited by 28 publications

(18 citation statements)

References 138 publications

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“…These newer generations of heat transfer fluids, known as nanofluids (NFs), were invented based on this concept and breakthroughs in nanotechnology. The NFs are a blend of solid nanoscale materials (metal oxides, metals, nonmetals, and other nanoparticles) and a fluid known as the base fluid (like ethylene glycol, oil, or water). , NFs have emerged as a viable alternative to the currently utilized heat transfer fluids due to their superior heat transfer capabilities. NFs have better thermal conductivities and thermophysical characteristics .…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

222

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Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

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Section: Introductionmentioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

222

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“…Water-in-crude oil emulsions are ubiquitous in the petroleum industry. − Demulsifiers, such as block copolymer surfactants, − aromatic solvents, , ionic liquids, nanoparticles, among other chemicals, have been used in commercial products to break these very stable emulsions. Alcohols are also used as additives to accelerate their process and their performance. − Asphaltenes and demulsifiers have opposite roles in water-in-crude oil emulsions.…”

Section: Introductionmentioning

confidence: 99%

Breaking of Water-in-Crude Oil Emulsions. 10. Experimental Evidence from a Quartz Crystal Resonator Sensor and an Oscillating Spinning Drop Interfacial Rheometer

et al. 2023

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The presence of asphaltenes in crude oil is a key factor in stabilizing water-in-oil emulsions. Changes in asphaltene aggregation have a definite effect on the formation of an elastic and highly resistant film that stabilizes such emulsions. Herein, we report asphaltene aggregation changes using two different methods. First, with a quartz crystal resonator sensor, clear changes in the resonant parameters (Δf n and ΔΓ n ) of the quartz sensor fully immersed in a heavy crude oil + cyclohexane solution were observed when the asphaltene concentration in the solution reached approximately 1000 ppm. Second, using an oscillating spinning drop interfacial rheometer, a distinct discontinuity of the trend of the optimum formulation is found when the asphaltene content C A attains this threshold value. The formulation variation was obtained by two kinds of scans, which were shown to be equivalent: an increase in water salinity for an anionic surfactant-containing system and an ethylene oxide number (EON) decrease of a non-ionic surfactant. In both cases, the hydrophilic surfactant was used as a demulsifier in a fixed concentration C D. It is seen that the variation of the asphaltene concentration C A generates an unexpected sudden change at the interface, according to Winsor’s affinity ratio R and hydrophilic–lipophilic deviation (HLD) premises about the compensation of the effect of asphaltenes by a formulation variable change. These newly reported results happening around C A = 1000 ppm could help to further understand how the asphaltene aggregation models and the HLD equation can be tools for the formulator to find fast and efficient answers to crude oil dewatering.

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“…In addition, corrosion of pipelines, pumps, and tanks, and contamination of refinery catalysts due to the presence of salts are sincere issues. The presence of natural emulsifiers as constituents of crude oil, e.g., asphaltenes, water-soluble naphthenic and carboxylic acids, resins, and solid particles, prompts the formation of stable crude oil emulsions [1][2][3]. Also, the extreme shear stresses in the reservoirs and production lines increase the emulsion's stability and the cost of the separation process [4,5].…”

Section: Introductionmentioning

confidence: 99%

Efficient Demulsification of Heavy Crude Oil Emulsions Using Bio‐Based Amphiphilic Esters

2023

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Abietic acid (AA) as a natural compound is used to synthesize two amphiphilic esters. AA was reacted with tetraethylene glycol (TG) or polyethylene glycol (PEG‐400) to obtain amphiphilic esters, AA‐TG, and AA‐PG, respectively. AA‐TG and AA‐PG were characterized using different techniques. In addition, the efficacy of these esters in demulsifying water‐in‐heavy crude oil emulsions was evaluated by means of several parameters. The demulsification efficacy (DE) of AA‐TG and AA‐PG reached 100 % in different cases. The results indicated that the DE was improved as the demulsifier concentration and temperature increased and the seawater ratio decreased. Furthermore, AA‐TG and AA‐PG achieved a higher DE and shorter demulsification time than the commercial demulsifier.

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Review on Application of Nanotechnology for Asphaltene Adsorption, Crude Oil Demulsification, and Produced Water Treatment

Cited by 28 publications

References 138 publications

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Breaking of Water-in-Crude Oil Emulsions. 10. Experimental Evidence from a Quartz Crystal Resonator Sensor and an Oscillating Spinning Drop Interfacial Rheometer

Efficient Demulsification of Heavy Crude Oil Emulsions Using Bio‐Based Amphiphilic Esters

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