Evolving an Accurate Decision Tree-Based Model for Predicting Carbon Dioxide Solubility in Polymers

Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time-consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO₂ solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO₂ solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.     

The morphology and gas‐separation performance of membranes comprising multiwalled carbon nanotubes/polysulfone–Kapton

The development of desirable chemical structures and properties in nanocomposite membranes involve steps that need to be carefully designed and controlled. This study investigates the effect of adding multiwalled nanotubes (MWNT) on a Kapton–polysulfone composite membrane on the separation of various gas pairs. Data from Fourier transform infrared spectroscopy and scanning electron microscopy confirm that some studies on the Kapton–polysulfone blends are miscible on the molecular level. In fact, the results indicate that the chemical structure of the blend components, the Kapton–polysulfone blend compositions, and the carbon nanotubes play important roles in the transport properties of the resulting membranes. The results of gas permeability tests for the synthesized membranes specify that using a higher percentage of polysulfone (PSF) in blends resulted in membranes with higher ideal selectivity and permeability. Although the addition of nanotubes can increase the permeability of gases, it decreases gas pair selectivity. Furthermore, these outcomes suggest that Kapton–PSF membranes with higher PSF are special candidates for CO₂/CH₄ separation compared to CO₂/N₂ and O₂/N₂ separation. High CH₄, CO₂, N₂, and O₂ permeabilities of 0.35, 6.2, 0.34, and 1.15 bar, respectively, are obtained for the developed Kapton–PSF membranes (25/75%) with the highest percentage of carbon nanotubes (8%), whose values are the highest among all the resultant membranes. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43839.     

Air-breathing microbial fuel cell with enhanced performance using nanocomposite proton exchange membranes

This work aims to improve the performance of air-breathing microbial fuel cells (MFCs) through using hydrocarbon polymer based nanocomposite proton exchange membranes. Accordingly, nanocomposite membranes based on sulfonated poly(ether ether ketone) (SPEEK) and montmorillonite (MMT) were investigated for such an application. Although the incorporation of MMT into SPEEK membranes resulted in reduced oxygen permeability as well as proton conductivity, but the overall selectivity was found to be improved. MFC tests revealed that using the optimized nanocomposite membrane (SPEEK-70/MMT-3 wt%) results in a considerably higher open circuit voltage (OCV) compared to the corresponding neat membrane. Moreover, it was found that the SPEEK-70/MMT-3 wt% membrane is able to provide about 40% more power output than Nafion^®117. On the account of high proton conductivity, low oxygen permeability, high electrochemical performance, ease of preparation and low cost, hydrocarbon based nanocomposite PEMs could be considered as promising electrolytes to enhance the performance of MFCs.     

Oxidation Behavior of Nanostructured Ni-5Al Coating. A case Study on Monophase Coatings

Protection of Metals and Physical Chemistry of Surfaces - Oxidation behavior of nanostructured Ni-5Al HVOF coatings was studied. For this purpose, isothermal oxidation of the free standing coatings...     

Vision-based 3-D trajectory tracking for unknown environments

This paper describes a vision-based system for 3-D localization of a mobile robot in a natural environment. The system includes a mountable head with three on-board charge-coupled device cameras that can be installed on the robot. The main emphasis of this paper is on the ability to estimate the motion of the robot independently from any prior scene knowledge, landmark, or extra sensory devices. Distinctive scene features are identified using a novel algorithm, and their 3-D locations are estimated with high accuracy by a stereo algorithm. Using new two-stage feature tracking and iterative motion estimation in a symbiotic manner, precise motion vectors are obtained. The 3-D positions of scene features and the robot are refined by a Kalman filtering approach with a complete error-propagation modeling scheme. Experimental results show that good tracking and localization can be achieved using the proposed vision system.     

Performance of Belief Propagation for Decoding LDPC Codes in the Presence of Channel Estimation Error

In this paper, we investigate the performance of the belief propagation (BP) algorithm for decoding low-density parity-check codes over the additive white Gaussian noise channel when there is an incorrect estimate of the channel signal-to-noise ratio (SNR) (referred to as "SNR mismatch") at the decoder. At the extremes for over- and underestimation of SNR, the performance of BP tends to that of min-sum algorithm and the channel bit-error rate, respectively. Our results for regular codes indicate that the sensitivity to mismatch increases by increasing the variable-node degree and by decreasing the check-node degree. The effect of variable-node degree, however, appears to be more profound, such that at a given rate, the codes with the smallest variable and check degrees are more robust against SNR mismatch. For irregular codes, by comparing the thresholds of a few ensembles, we demonstrate that the ensemble which performs better in the absence of mismatch can perform worse in the presence of it. To obtain our asymptotic results, we propose a computationally efficient method based on the Gaussian approximation of density evolution in the presence of SNR mismatch. We also show that the asymptotic results are consistent with simulation results for codes with finite block lengths     

Control and Navigation Framework for Quadrotor Helicopters

This paper presents the development of a nonlinear quadrotor simulation framework together with a nonlinear controller. The quadrotor stabilization and navigation problems are tackled using a nested loops control architecture. A nonlinear Backstepping controller is implemented for the inner stabilization loop. It asymptotically tracks reference attitude, altitude and heading trajectories. The outer loop controller generates the reference trajectories for the inner loop controller to reach the desired waypoint. To ensure boundedness of the reference trajectories, a PD controller with a saturation function is used for the outer loop. Due to the complexity involved in controller development and testing, a simulation framework has been developed. It is based on the Gazebo 3D robotics simulator and the Open Dynamics Engine (ODE) library. The framework can effectively facilitate the development and validation of controllers. It has been released and is available at Gazebo quadrotor simulator (2012).     

New insight into prediction of phase behavior of natural gas hydrate by different cubic equations of state coupled with various mixing rules

Progress in hydrate thermodynamic study necessitates robust and fast models to be incorporated in reservoir simulation softwares. However, numerous models presented in the literature makes selection of the best,proper predictive model a cumbersome task. It is of industrial interest to make use of cubic equations of state(EOS) for modeling hydrate equilibria. In this regard, this study focuses on evaluation of three common EOSs including Peng–Robinson, Soave–Redlich–Kwong and Valderrama–Patel–Teja coupled with van der Waals and Platteeuw theory to predict hydrate P–T equilibrium of a real natural gas sample. Each EOS was accompanied with three mixing rules, including van der Waals(vd W),Avlonitis non-density dependent(ANDD) and general nonquadratic(GNQ). The prediction of cubic EOSs was in sufficient agreement with experimental data and with overall AARD% of less than unity. In addition, PR plus ANDD proved to be the most accurate model in this study for prediction of hydrate equilibria with AARD% of 0.166.It was observed that the accuracy of cubic EOSs studied in this paper depends on mixing rule coupled with them,especially at high-pressure conditions. Lastly, the present study does not include any adjustable parameter to be correlated with hydrate phase equilibrium data.     

Hybrid Fractal-Wavelet Method for Multi-Channel EEG Signal Compression

In this paper, a hybrid method is proposed for multi-channel electroencephalograms (EEG) signal compression. This new method takes advantage of two different compression techniques: fractal and wavelet-based coding. First, an effective decorrelation is performed through the principal component analysis of different channels to efficiently compress the multi-channel EEG data. Then, the decorrelated EEG signal is decomposed using wavelet packet transform (WPT). Finally, fractal encoding is applied to the low frequency coefficients of WPT, and a modified wavelet-based coding is used for coding the remaining high frequency coefficients. This new method provides improved compression results as compared to the wavelet and fractal compression methods.