In the recent decade, injection of nanoparticles (NPs) into underground formation as liquid nanodispersions has been suggested as a smart alternative for conventional methods in tertiary oil recovery projects from mature oil reservoirs. Such reservoirs, however, are strong candidates for carbon geo-sequestration (CGS) projects, and the presence of nanoparticles (NPs) after nanofluid-flooding can add more complexity to carbon geo-storage projects. Despite studies investigating CO2 injection and nanofluid-flooding for EOR projects, no information was reported about the potential synergistic effects of CO2 and NPs on enhanced oil recovery (EOR) and CGS concerning the interfacial tension (γ) of CO2-oil system. This study thus extensively investigates the effect of silica NPs on the γ of CO2/decane system at elevated pressure and temperature to recognise the potential impact of NPs-injection on the future CGS projects. To achieve this, a wide-ranging series of tests have been conducted to reveal the role of hydrophilic and hydrophobic silica NPs on γ of the CO2/oil system. n-decane was utilized as model oil and different amounts of NPs were mixed with the oil phase. Oil-NPs dispersions were formulated using an ultrasonic homogenizer. The γ of the CO2/oil system was measured at different pressures (0.1 to 20 MPa) and temperatures (25 to 70 °C) using a high-pressure temperature optical cell. The γ data were measured using the pendant drop technique via axisymmetric drop shape analysis (ADSA). The results showed that, generally, CO2/oil γ subjected mainly to pressure, temperature, and with less extent to NPs load in the oil phase. γ decreases with increased pressure until reaching a plateau where no more significant decrease in γ was observed. The γ trend with increased temperature, on the other hand, was more completed. No significant impact of temperature on γ was recorded with low pressure (≤ 5 MPa). Similarly, at relatively high pressure (≥ 25 MPa), only a slight variation of IFT with temperature change was recorded. However, for the pressure range from 5 – 25 MPa, IFT was increased remarkably with temperature. Furthermore, NPs in the oil phase exhibit a remarkable influence on IFT. In this context, the presence of hydrophilic silica NPs in the oil phase can significantly reduce the γ of the CO2/decane system. However, hydrophobic silica NPs showed less influence on IFT reduction. The outcomes of this work afford good understandings into applications of NP for EOR and CGS applications and help to de-risk CO2-geological storage projects.
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The nonlinear optical properties response of nematic liquid crystal (6CHBT) and the impact of doping with two kinds of nanoparticles; Fe3O4 magnetic nanoparticles and SbSI ferroelectric nanoparticles have been studied using the non-linear dynamic method through z-scan measurement technique. This was achieved utilizing CW He-Ne laser. The pure LC and magnetic LC nanoparticle composite samples had a maximum absorption while the ferroelectric LC nanoparticle composite had a minimum absorption of the incident light. The nonlinear refractive index was positive for the pure LC and the rod-like ferronematic LC composite samples, while it was negative for the ferroelectric LC composite. The studying of the nonlinear optical
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Reducing a structure’s self-weight is the main goal and a major challenge for most civil constructions, especially in tall buildings and earthquake-affected buildings. One of the most adopted techniques to reduce the self-weight of concrete structures is applying voids in certain positions through the structure, just like a voided slab or BubbleDeck slab. This research aims to study, experimentally and theoretically, the structural behavior of BubbleDeck reinforced concrete slabs under the effect of harmonic load. Tow-way BubbleDeck slab of 2500mm×2500m×200mm dimensions and uniformly distributed bubbles of 120mm diameter and 160mm spacing c/c was tested experimentally under the effect of harmonic load. Numerical analysis was als
... Show MoreThe aim of this study was to propose and evaluate an eco-epidemiological model with Allee effect and nonlinear harvesting in predators. It was assumed that there is an SI-type of disease in prey, and only portion of the prey would be attacked by the predator due to the fleeing of the remainder of the prey to a safe area. It was also assumed that the predator consumed the prey according to modified Holling type-II functional response. All possible equilibrium points were determined, and the local and global stabilities were investigated. The possibility of occurrence of local bifurcation was also studied. Numerical simulation was used to further evaluate the global dynamics and the effects of varying parameters on the asymptotic behavior of
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A resume is the first impression between you and a potential employer. Therefore, the importance of a resume can never be underestimated. Selecting the right candidates for a job within a company can be a daunting task for recruiters when they have to review hundreds of resumes. To reduce time and effort, we can use NLTK and Natural Language Processing (NLP) techniques to extract essential data from a resume. NLTK is a free, open source, community-driven project and the leading platform for building Python programs to work with human language data. To select the best resume according to the company’s requirements, an algorithm such as KNN is used. To be selected from hundreds of resumes, your resume must be one of the best. Theref
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Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct
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