In this study a 2D cubic chamber model filled with paraffin is analyzed with and without the inclusion of magnetic Fe3O4 nanoparticles at concentrations of 0.5, 1, 1.5 and 2 wt%, and an external magnetic field of intensities 0.005, 0.01, 0.015 and 0.02 T. It is ascertained that adding magnetic nanoparticles leads the horizontal temperature gradient to be reduced owing to increments in thermal conductivity. Additionally, this feature is found to be accelerated by applying an external magnetic field, which shapes highly conductive cluster formations of nanoparticles. However, since the increase in nanoparticle concentration and magnetic intensity increases the composite viscosity, there is an optimum configuration while applying both schemes. As such, the addition of 1 wt% nanoparticles provides the best results, as the melting time is reduced up to 25% compared to pure paraffin. Meanwhile, the melting time of a 1 wt% nanoparticle-containing phase change material (PCM) in the presence of an external magnetic field is improved up to 24% compared to the case with no external magnetic field. Also, the heat transfer coefficient of a 1 wt% nanoparticle-containing PCM both with and without an external magnetic field is also staggeringly enhanced compared to pure paraffin. Good correspondence with experimental data was achieved.
Gas injection has emerged over the recent decades as a promising technology to enhance oil recovery in various fields worldwide. The efficiency and success of a gas injection operation can be assessed through a number of vital experimental studies. Interfacial Tension (IFT) between the injected gas and the displacing fluid is a key parameter playing an eminent role in the foregoing studies. The main scope of this work is making a progress in modeling the IFTs between diverse n-alkanes and Methane (CH4), Carbon Dioxide (CO2), and Nitrogen (N2) natural gases. For this purpose, two smart AI-based approaches of Cascaded Feedforward Neural Network (CFNN) and Decision Tree Learning (DT) were used to simultaneously model the IFTs between foregoing immiscible binary systems as a function of pressure, temperature, the gases properties, and the properties of the liquid. Several statistical measures and graphical descriptions were employed to aid the accuracy analysis of the proposed models. Both developed CFNN and DT networks represented desirable close-to-reality predictions in all binary systems. Besides, CFNN established itself as the most robust model for all studies binary systems with RMSE values of 0.5924, 0.5649, and 0.5870 mN/m, and R² values of 0.9902, 0.9910, and 0.9904 for the train, test, and overall data, respectively.
Spontaneous imbibition (SI), which is a process of displacing a nonwetting fluid by a wetting fluid in porous media, is of critical importance to hydrocarbon recovery from fractured reservoirs. In the present study, we utilize deep and ensemble learning techniques to predict SI recovery in porous media under different boundary conditions including All-Faces-Open (AFO), One-End-Open (OEO), Two-Ends-Open (TEO), and Two-Ends-Closed (TEC). An extensive experimental dataset reported in literature representing a multiplicity of non-wetting fluid recovery-time curves was used in our analysis. The prepared dataset was used to learn diverse ensemble and deep learning algorithms of Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Voting Regressor (VR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The training procedure provided us with robust models linking the SI recovery to the absolute permeability (k), porosity (ϕ), characteristic length (Lc), interfacial tension (σ), wetting-phase viscosity (μw), non-wetting-phase viscosity (μnw), and imbibition time (t). To evaluate and validate the models’ prediction, we used two well-established approaches: (i) 10-fold cross-validation and (ii) predicting the SI behavior of a set of unseen data excluded from the model training. Our results illustrate an excellent performance of deep and ensemble learning techniques for prediction of SI with the test RMSE values of 4.642, 4.088, 4.524, 3.933, 3.875, 3.975, 4.513, and 4.807 percent for RF, GBM, XGBoost, LightGBM, VR, CNN, LSTM, and GRU models, respectively. The models have significant benefits in terms of accuracy and generality. Furthermore, they alleviate the sophistications associated with tuning the traditional correlation functions. The findings of this study can pave the road toward a more comprehensive characterization of fluid flow in porous materials which is important to a wide range of environmental and energy-related challenges such as contaminant transport, soil remediation, and enhanced oil recovery.
Deriving benefits from a variety of flexible sources to lower or even eliminate renewable energy resources challenges is an imperative issue in the management of power systems. Constructing a well compatible flexibility evaluation method that encompasses cost minimization and flexibility improvement is crucial to tackle this. In this paper, a stochastic economical flexibility evaluation method is proposed based upon the envelope of the feasible operation region to the uncertain space. The probability distribution function of the network's flexibility is provided for each hour. Furthermore, the flexibility that storage systems as a flexible source provide for the distribution networks is quantified. The flexibility sensitivity to the storage facility capacity is scrutinized. The simulation results show the impact of PV generation and energy storage systems on the flexibility improvement. Moreover, the sensitivity outcomes reveal that which storage capacity should be adopted for the secure distribution grid operation.
The present review aims to classify the natural essential oil compounds used as corrosion inhibitors for different metal samples into various corrosive media. These inhibitors are classified into several class based on the parts of plant which are used for production of these oil inhibitor compounds. These parts of plants are such as aerial parts, seeds or nuts, leaves, flowers, and fruits. The cases such as the names of the oil inhibitors, the analyzed samples, the tested media, the adsorption nature, and the highest inhibition efficiencies are given in the tables. In addition, at the end of the review, the inhibition mechanism of the oil inhibitor compounds is reported. The results show that the common mechanism of this type of the inhibitors is (chemical and/or physical) adsorption process of the oil molecules over the surface of the tested samples, separating the surface of the samples from the corrosive medium, and blocking the active reactions. Graphical Abstract
Frequency is one of the essential parameters for monitoring, control, and protection in power systems. Some protection devices use frequency for deciding under various conditions of the power system. Therefore, it is important to estimate frequency rigorously and fast. In this paper, a DFT- based algorithm is introduced for frequency estimation in the presence of harmonics and decaying DC. Initially, According to the condition of the power system, the type of input signal (current or voltage) is determined. The current and voltage signals are the input to the proposed algorithm under fault and normal conditions, respectively. Based on the type of the input signal, the frequency estimation method (FEM) is selected and used. 2 FEMs are proposed to estimate the frequency based on solving a cubic equation. For both FEMs, an optimization-based filter is designed and applied to mitigate harmonics. The proposed algorithm is validated under various static and dynamic tests in MATLAB. The results show that the proposed algorithm is accurate and fast with a low computational burden.
The mud weight window (MW) determination is one of the most important parameters in drilling oil and gas wells, where accurate design can secure the drilled well and deliver a stable borehole. In this paper, novel algorithms based on the most influential set of input features are developed to predict pore pressure, including rate of penetration (ROP), deep resistivity (ILD), density (RHOB), photoelectric index (PEF), corrected gamma ray (CGR), compression-wave velocity (Vp), weight on bit (WOB), shear-wave velocity (Vs) and pore compressibility (Cp). The algorithms used in this study are as follows: 1) machine learning algorithms (ML), these are the K-nearest neighbor (KNN) algorithm, weighted K-Nearest Neighbor (WKKNN), and distance weighted KNN (DWKNN); 2) hybrid machine learning algorithms (HML), which include the combination of three ML with particle swarm optimization (PSO) (KNN-PSO, WKNN-PSO and DWKNN-PSO). The 2875-record dataset used in this study was collected from three wells (S1, S2 and S3) in one of the gas reservoirs (Tabnak field) in Iran. After comparing the performance accuracy of all algorithms, DWKNN-PSO has the best performance accuracy compared to other algorithms presented in this paper (for the total dataset of wells S1 and S2: R2=0.9656 and RMSE = 12.6773 psi). Finally, the generalizability of the best predictive algorithm for PP, DWKNN-PSO, is evaluated by testing the proposed algorithm on an unseen dataset from another well (S3) in the field under study, where the DWKNN-PSO algorithm provides PP predictions in well S3 with high accuracy, R2 = 0.9765 and RMSE = 9.7545 psi, confirming its ability to be used in PP prediction in the studied field.
In recent years, there has been a substantial increase in the induced seismicity associated with geothermal systems. However, understanding and modeling of injection-induced seismicity have still remained as a challenge. This paper presents a two-dimensional fully thermo-hydro-mechanical (THM) coupled boundary element approach to characterize the fault response to forced fluid injection and assess the effect of different injection protocols on seismic risk mitigation as well as permeability enhancement. The laboratory-derived rate-and-state friction law was used to capture the frictional paradigm observed in mature faults produced in granite rocks. All phases of stick-slip cycles, including aseismic slip, propagation of dynamic rupture, and interseismic periods, were simulated. The modeling results showed that the residual values of effective normal stress and static shear stress after a particular event completely dominate the constitutive behavior of fault friction during the next seismic event. The seismic energy analyses indicated that there is a negative correlation between the seismic magnitude and the total injected volume, such that a prolonged monotonic injection eventually results in the steady slip, rather than the seismic slip. Several fluid injection protocols were designed based on a volume-controlled (VC) approach and traffic light systems (TLS) to explore their effectiveness on the seismic risk mitigation and permeability enhancement. The results showed that cyclic injection based on TLS is the most effective approach for irreversible permeability enhancement of faults through promoting slow and steady slips. Our numerical simulations also revealed that fluid extraction (backflow-fixing bottom hole pressure at atmospheric pressure), regardless of the injection style, can considerably reduce the seismicity-related risks by preventing the fast-accelerated fracture slip during the post-injection stage. This study presents novel insights into modeling the rate-and-state governed faults exposed to forced fluid injection, and provides useful approaches for shear stimulation of faults with reduced seismic risks.
The present study focused on the exergoenvironmental analysis via Life Cycle Assessment and emergy based evaluation of a multigeneration system of hydrogen, carbon dioxide, and power through biomass gasification (Sawdust). In this regards, energy, exergy, exergoeconomic, exergoenvironmental, emergoeconomic, and emergoenvironmental modeled in MATLAB software for a considered multigeneration system, is to employ hydrogen in a solid oxide fuel cell to increase power yield and to generate hydrogen and CO2 as output products. The Life Cycle Assessment for exergoenvironmental analysis has been performed in SIMA Pro based on Ecoindicator 99. Sensitivity analysis has been performed for the main decision variables of the considered multigeneration system based on energy, exergy, exergoeconomic, exergoenvironmental, emergoeconomic, and emergoenvironmental assessment.
Nature employs a wide library of transient interaction to operate adaptively on various time and length scales. Inspired by such elegant designs, combinations of different supramolecular assemblies have been extensively introduced in synthetic material platforms to obtain biomimetic functions. Among the most widely used transient bond, metal–ligand coordination performs a distinct role in designing robust and stimuli-responsive metallo-supramolecular polymer networks (MSPNs). The functionality of the metal–ligand complex, in addition to the technological benefits of the polymer backbone, provides novel materials with outstanding potentials to be used in a wide range of applications. Although the correlation between bulk macroscopic properties of such systems to the microscopic characteristics of the polymer precursor and transient bonds is frequently studied, the importance of coordination geometry of the metal complex is often overlooked. Despite that, the knowledge of controlling the spatial configuration of supramolecular coordination complexes (SCCs) has been extremely advanced in recent years. Notable outcomes include but are not limited to the development of highly ordered SCCs arcing from 1D nanofibers to 3D metal–organic frameworks or the advent of reversible molecular rearrangement as employed in molecular machines. This wealth of knowledge has been rarely employed in the field of polymer science, while it could provide a new approach to build highly ordered model-type networks or even induce novel structural rearrangements upon the application of external stimuli. Accordingly, we classify existing reports on the coordination geometry of MSPNs and review relevant self-sorting mechanisms of SCCs to lay out general directions for employing coordination geometry as a new design parameter in polymeric systems.
Disposable, flexible and low-cost coated biosensor electrode obtained by printing conductive inks presents a great potential as health monitoring devices. A novel and highly conductive coating of graphene ink was printed on a flexible substrate using the stencil method to prepare a sensitive biosensor for enzymatic electrochemical detection of glucose. A novel water-based graphene ink including an acrylic copolymer was formulated in this work. The Sheet resistance was obtained in the range of 42.14 Ω/sq in new formulation of the newly proposed graphene ink to make it a proper candidate for flexible biosensor manufacturing. The sensitivity and limit of detection of 16.42 μA·mM⁻¹·cm⁻² and 1.34 mM were respectively achieved for the biosensor in the linear range of <2 mM to >28 mM. The results signified high selectivity against interfering species such as ascorbic acid and lactose, and high repeatability and reproducibility with low relative standard deviation values of 1.04 % and 1.5 %, respectively, for the device. No considerable reduction in the printed electrochemical biosensor (PEB) performance was observed after bending cycles of up to 100, indicating the high flexibility of the biosensor. The PEB presented a great potential to be used as a personalized glucose monitoring device in complex biological fluids.
This study investigated the role of adipic acid (AA) in improving the anti-corrosion performance and in-vitro bioactivity of hydroxyapatite coating applied on AZ31 Mg alloy. Firstly, hydroxyapatite (HA) coating was applied on the AZ31 Mg alloy. In the next step, the optimized hydroxyapatite-based coating, modified with adipic acid by direct addition and post-treatment methods. Corrosion behavior of uncoated sample and different coated samples were studied by Electrochemical Impedance Spectroscopy (EIS) and Direct Current (DC) Polarization techniques. Surface characterization was examined by Field Emission Scanning Electron Microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDXS), X-ray diffraction (XRD) and contact angle measurements. According to the contact angle results, all coated samples reveal high hydrophilic behavior. Additionally, the XRD analysis confirms the formation of hydroxyapatite crystals in all coated samples and the presence of the AA in HA + AA and HA − AA coatings does not change the crystal structure of the hydroxyapatite. Electrochemical studies display that the Rp value of HA coating is increased from 3630 Ω.cm² to 10784 Ω.cm² by the direct addition method also by the post-treatment method the polarization resistance reached 10294 Ω.cm². In addition, an in-vitro bioactivity assessment was performed in a simulated body fluid (SBF). Based on FE-SEM studies, a protective layer of AA is formed on HA coating by the post-treatment method which improves the anti-corrosion behavior efficiently. The results showed that the adipic acid-modified hydroxyapatite coatings by direct addition and post-treating methods display better anti-corrosion properties and bioactivity compared to the optimal hydroxyapatite coating.
Active contours model (ACM) has been extensively used in computer vision and image processing. In recent studies, convolutional neural networks (CNNs) have been combined with ACM replacing the user in the process of contour evolution and image segmentation to eliminate limitations associated with ACM dependence on energy functional parameters and initialization. However, prior studies did not aim for automatic initialization, which is addressed in this article. In addition to manual initialization, current methods are highly sensitive to the initial location and fail to delineate borders accurately. We propose a fully automatic image segmentation method to address problems of manual initialization, insufficient capture range, and poor convergence to boundaries, in addition to the problem of assignment of energy functional parameters. We train two CNNs, one of which generating ACM weighting parameters and the other generating a ground truth mask to extract distance transform (DT) and an initialization circle. DT is used to form a vector field pointing from each pixel of the image towards the closest ground truth boundary point. Vector magnitudes are equal to the Euclidean distance between each pixel and the closest ground truth boundary point. We evaluate our method on four publicly available datasets, including two building instance segmentation datasets, i.e., Vaihingen and Bing huts, and two mammography image datasets, INBreast and DDSM-BCRP. Our approach achieves state-of-the-art results in mean Intersection over Union (mIoU), Dice similarity coefficient and Boundary F-score (BoundF) with the values of 92.33%, 92.44%, and 86.57% for Vaihingen dataset, and 87.12%, 86.86%, and 66.91% for Bing huts dataset. We obtained the Dice similarity coefficient values of 94.23% and 90.89% for the INBreast and DDSM-BCRP, respectively.
Parameter estimation of power plants is one of the main challenges of power system studies. Among different components of a power plant, excitation system (EXS) has great importance because of its effect on dynamic stability of power systems. Thus, it is vital to have accurate models of EXSs for power system dynamic studies. Since the field voltage and current are not accessible in brushless EXSs, parameter estimation of them is more difficult and challenging. Therefore, a new method is proposed in this paper to estimate field voltage signal using other measurements of the synchronous generator (SG). The proposed method is carried out through three stages; 1) parameter estimation of the SG using load rejection tests, 2) field voltage estimation, and 3) parameter estimation of EXS. The proposed method is applied to a 147 MVA industrial gas unit. The estimated model outputs are compared to the experimental results to show the accuracy and effectiveness of the proposed method.
This paper proposes a distributed gradient dynamics approach for solving Multi-Objective AC Optimal Power Flow (MO-ACOPF) problem. The fuel cost, environmental pollution, and network loss are considered as objective functions. The non-convex MO-ACOPF problem is reformulated as a gradient dynamical system. We derive condition under which the equilibrium point of the proposed gradient dynamical system is the saddle point of the MO-ACOPF Lagrangian function. We propose a distributed solution algorithm for derived MO-ACOPF gradient dynamical system. In the distributed solution algorithm, each bus in the power network is assumed as a local agent, which only uses information from its neighbor agents to compute optimal operating points. We prove convergence of our distributed algorithm to the equilibrium point of the MO-ACOPF gradient dynamical system (and accordingly to the primal and dual optimum of original MO-ACOPF). Accordingly, our proposed approach has the following main advantages: (a) it is a distributed and decentralized algorithm and accordingly it needs less computational time as compared to the centralized model, (b) it has the proof of convergence to the global optimal solution, and (c) it is suitable for varying loads in power networks. The simulation results based on standard IEEE 14-, 30- ,39-, 57-, 118- and 300-bus test systems in different scenarios are carefully studied. The proposed distributed gradient dynamics provides the best results compared to the relevant literature as illustrated by simulation results.
Nanocomposites containing clay nanoparticles often present favorable properties such as good mechanical and thermal properties. They frequently have been studied for tissue engineering (TE) and regenerative medicine applications. On the other hand, poly(glycerol sebacate) (PGS), a revolutionary bioelastomer, has exhibited substantial potential as a promising candidate for biomedical application. Here, we present a facile approach to synthesizing stiff, elastomeric nanocomposites from sodium‐montmorillonite nano‐clay (MMT) in the commercial name of Cloisite Na+ and poly(glycerol sebacate urethane) (PGSU). The strong physical interaction between the intercalated Cloisite Na+ platelets and PGSU chains resulted in desirable property combinations for TE application to follow. The addition of 5% MMT nano‐clay resulted in an over two‐fold increase in the tensile modulus, increased the onset thermal decomposition temperature of PGSU matrix by 18°C, and noticeably improved storage modulus of the prepared scaffolds, compared with pure PGSU. As well, Cloisite Na+ enhanced the hydrophilicity and water uptake ability of the samples and accelerated the in‐vitro biodegradation rate. Finally, in‐vitro cell viability assay using L929 mouse fibroblast cells indicated that incorporating Cloisite Na+ nanoparticles into the PGSU network could improve the cell attachment and proliferation, rendering the synthesized bioelastomers potentially suitable for TE and regenerative medicine applications. The role of Nanoclay as a natural nanoparticles for increasing mechanical and bio‐properties of PGSU material.
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