Effects of Graphene Oxide Nanosheets and Al2O3 Nanoparticles on CO2 Uptake in Semi‐clathrate Hydrates

Gas hydrate/clathrate hydrate formation is an innovative method to trap CO₂ into hydrate cages under appropriate thermodynamic and/or kinetic conditions. Due to their excellent surface properties, nanoparticles can be utilized as hydrate kinetic promoters. Here, the kinetics of the CO₂ + tetra‐n‐butyl ammonium bromide (TBAB) semi‐clathrate hydrates system in the presence of two distinct nanofluid suspensions containing graphene oxide (GO) nanosheets and Al₂O₃ nanoparticles is evaluated. The results reveal that the kinetics of hydrate formation is inhibited by increasing the weight fraction of TBAB in aqueous solution. GO and Al₂O₃ are the most effective kinetic promoters for hydrates of (CO₂ + TBAB). Furthermore, the aqueous solutions of TBAB + GO or Al₂O₃ noticeably increase the storage capacity compared to TBAB aqueous solution systems.     

Regional Analysis of Flow Duration Curves through Support Vector Regression

A flow-duration curve (FDC) shows the relationship between magnitude and frequency of daily streamflows over a specific time period. Artificial intelligence methods e.g. Support Vector Machines for Regression (SVR) and Artificial Neural Network (ANN) are useful techniques in the prediction of FDCs in ungagged basins. Regional analysis of FDCs were performed through SVR, ANN and Nonlinear Regression (NLR) using streamflow with durations of 0.02, 0.10, 0.20, 0.50 and 0.90% as dependent variables and six watershed characteristics chosen as effective independent variables on 33 selected watersheds in the Namak-Lake basin located in central zone of Iran. The results shows that the most important watershed characteristics are weighted average height, area, rangeland area, drainage density, permeable formation, and average stream slope. SVR has higher accuracy with relative root mean squared error (RMSE_r) of 9.37 to 1.45 and Nash-Sutcliff criterion (NSE) of 0.54 to 0.91 than ANN with RMSE_r with 9.42 to 3.79 and NSE of 0.39 to 0.86 and NLR with RMSE_r with 18.04 to 3.38 and NSE of 0.53 to 0.79. In general, SVR is proposed to be used to estimate FDCs.

     

Optimal distribution function determination for plus fraction splitting

Reservoir fluid modelling is one of the most important steps in reservoir simulation and modelling of flow lines as well as surface facilities. One of the most uncertain parameters of the reservoir fluids is the plus fraction. An accurate and consistent splitting scheme can reduce this uncertainty and as a result, enhance the modelling of reservoir fluids. The existing schemes for splitting plus fractions are all based on assuming a specific mole fraction‐molecular weight distribution with predefined constant values that may yield inaccurate and inconsistent results. In this study, an optimization‐based algorithm was developed to determine the aforementioned controlling parameters of the plus fraction distribution function, enforcing the relationship between specific gravity and molecular weight of the single carbon numbers (SCNs). The introduced optimization‐based splitting technique was applied to different samples, covering a wide range of reservoir fluids, including gas condensates, volatile oils, black oils, and heavy oils. The results showed that the proposed technique yielded a more consistent molecular weight‐mole fraction distribution concerning the experimental extended analysis of plus fractions, yielding an average relative error of 25.8 % compared to 76, 33.6, and 45.9 % for the Katz, Ahmed, and Whitson methods, respectively. It was also shown that the proposed method results in more accurate and more consistent phase behaviour predictions than the existing methods concerning the experimental data. Furthermore, the results showed that the introduced optimization‐based method yields monotonic split samples regarding specific gravity and molecular weight, while the conventional techniques do not guarantee to preserve the monotonicity.     

Multiview Classification and Dimensionality Reduction of Scalp and Intracranial EEG Data through Tensor Factorisation

Electroencephalography (EEG) signals arise as mixtures of various neural processes which occur in particular spatial, frequency, and temporal brain locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time, and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy for each set. The relative influence on the classification accuracy of the respective spatial, temporal, or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number of components in each mode and also by rejecting components with insignificant contribution to the classification accuracy.     

Correction to: EATMR: an energy-aware trust algorithm based the AODV protocol and multi-path routing approach in wireless sensor networks

Telecommunication Systems -     

A new MEMS based variable capacitor using electrostatic vertical comb drive actuator and auxiliary cantilever beams

We have proposed a new wide tunable MEMS variable capacitor. In the proposed structure, an electrostatic vertical comb drive actuator is used to extend the tuning range. Moreover, the auxiliary cantilever-beams are used in the electrostatic comb drive actuator to delay the front sticking (Pull in) and increase the tunability. The effect of lateral gap distance between the fingers in the capacitance tunability is investigated. Not only a full review of electrostatic actuator portion is done but also the electric fields related to lateral gap changes are simulated by COMSOL software and its results are compared with theoretical results as well. The structure is calculated using MATLAB software. To verify, the calculated results are compared with simulated results using Intellisuite software. According to calculation and simulation results the achieved tuning range is 285%.