Buoyancy effects on gaseous slip flow in a vertical rectangular microchannel

Consideration is given to the buoyancy effects on the fully developed gaseous slip flow in a vertical rectangular microduct. Two different cases of the thermal boundary conditions are considered, namely uniform temperature at two facing duct walls with different temperatures and adiabatic other walls (case A) and uniform heat flux at two walls and uniform temperature at other walls (case B). The rarefaction effects are treated using the first-order slip boundary conditions. By means of finite Fourier transform method, analytical solutions are obtained for the velocity and temperature distributions as well as the Poiseuille number. Furthermore, the threshold value of the mixed convection parameter to start the flow reversal is evaluated. The results show that the Poiseuille number of case A is an increasing function of the mixed convection parameter and a decreasing function of the channel aspect ratio, whereas its functionality on the Knudsen number is not monotonic. For case B, the Poiseuille number is decreased by increasing each of the mixed convection parameter, the Knudsen number, and the channel aspect ratio.     

Study of the High‐Pressure Vapour‐Liquid Equilibria for Strongly Non‐ideal Binary and Ternary Mixtures

High‐pressure vapour‐liquid equilibria for binary and ternary high polar and asymmetric systems are calculated using the Peng‐Robinson‐Stryjek‐Vera equation of state coupled with the Twu‐Coon (TWMR), the Orbey‐Sandler (OSMR) and the Wong‐Sandler (WSMR) mixing rules. Modified UNIFAC model is used for determining the activity coefficient and excess Gibbs free energy. The calculated results are compared with the experimental data and good agreement is observed. The average absolute deviation percents (AAD%)s indicated that the WSMR has less AAD% than other mixing rules in most of cases.     

Extraction of 2-hydroxyphenol by surfactant coated nanosized magnetic particles

A colloidal suspension of magnetic particles coated with double layer surfactant molecules was prepared, characterized and used for separation of some chemicals. The aqueous magnetic fluid was prepared by chemical precipitation method and characterized by total organic carbon analyzer (TOC), thermogravimetric analysis (TGA) and transmission electron microscopy (TEM). The TOC and TGA measurements indicate that the prepared magnetic particles are coated with surfactant molecules in double layers. The outer surfactant layer has free polar heads. TEM measurement results show that magnetic particles are nanosized. A mixture of 2-hydroxyphenol (strong polar) and 2-nitrophenol (weak polar) was chosen as the target system. Results show that the magnetic fluid can selectively adsorb 2-hydroxyphenol from the mixture, whereas the adsorption of 2-nitrophenol is insignificant. For 2-hydroxyphenol, the adsorption equilibrium can be described well by Langmuir isotherm, and the adsorption kinetic can be fitted well by a linear driving force mass transfer model.     

Eco-friendly electronics,based on nanocomposites of biopolyester reinforced with carbon nanotubes: a review

ABSTRACT

Fabrication of electronic materials from nanocomposite of biopolyesters reinforced with carbon nanotubes can be regarded as the effective alternative for conventional nanocomposites consisting of non-biodegradable polymers. Commercial availability of biopolyester-based nanocomposites is limited because of their high cost compared to other polymers, but the factor of their compostable nature is worthless for environmental protection. Such nanocomposites have potential applications in biodegradable sensors, EMI materials, etc. In this review, the current progress of biopolyester/CNTs nanocomposites in the field of biodegradable electronics is reviewed and also the impact of CNTs dispersion on electrical, thermal and mechanical properties of eco composites is stipulated.     

Deep Neural Networks Regularization Using a Combination of Sparsity Inducing Feature Selection Methods

Neural Processing Letters - Deep learning is an important subcategory of machine learning approaches in which there is a hope of replacing man-made features with fully automatic extracted features....     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Process Mining Approach of a New Water Quality Index for Long-Term Assessment under Uncertainty Using Consensus-Based Fuzzy Decision Support System

One of the biggest challenges in water quality monitoring is how to optimize big Data gathered from a wide range of resources. This paper presented a new software-based pathway of process mining approach for extending a flexible WQI (Water Quality Index) that would deal with uncertainties derived from missing data occurrence in short- and long-term assessments. The methodology is based on integration of four multi-criteria group decision-making models coupled with fuzzy simulation including AHP (Analytical Hierarchy Process), fuzzy OWA (Ordered Weighting Average), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and fuzzy TOPSIS that were used for data mining and group consensus evaluation.. Examining the methodology on groundwater resources being supplied for drinking in Shiraz, Iran showed high integrity, accuracy, and proximity-to-real interpretation of water quality. This was the first study where decision-making risks such as Decision Makers’ risk-prone or risk-aversion attitudes (optimistic degree), DMs’ power, and consensus degree of each water quality parameter have been considered in WQI research. The proposed index offered a flexible choice in defining the intended project duration, stakeholders’ judgments, types of water use and water resource, standards, as well as type and number of water quality parameters. Thus, beside sustaining the unity in structure, this methodology could be suggested as a potentially WQI for other regions. The presented methodology would help more efficient monitoring of water resources for drinking purpose with respect to water quality.

     

Cracks coalescence mechanism and cracks propagation paths in rock-like specimens containing pre-existing random cracks under compression

The mechanism of cracks propagation and cracks coalescence due to compressive loading of the brittle substances containing pre-existing cracks （flaws） was modeled experimentally using specially made rock-like specimens from Portland Pozzolana Cement （PPC）. The breakage process of the specimens was studied by inserting single and double flaws with different inclination angles at the center and applying uniaxial compressive stress at both ends of the specimen. The first crack was oriented at 50＆#176; from the horizontal direction and kept constant throughout the analysis while the orientation of the second crack was changed. It is experimentally observed that the wing cracks are produced at the first stage of loading and start their propagation toward the direction of uniaxial compressive loading. The secondary cracks may also be produced in form of quasi-coplanar and/or oblique cracks in a stable manner. The secondary cracks may eventually continue their propagation in the direction of maximum principle stress. These experimental works were also simulated numerically by a modified higher order displacement discontinuity method and the cracks propagation and cracks coalescence were studied based on Mode I and Mode II stress intensity factors （SIFs）. It is concluded that the wing cracks initiation stresses for the specimens change from 11.3 to 14.1 MPain the case of numerical simulations and from 7.3 to 13.8 MPa in the case of experimental works. It is observed that cracks coalescence stresses change from 21.8 to 25.3 MPa and from 19.5 to 21.8 MPa in the numerical and experimental analyses, respectively. Comparing some of the numerical and experimental results with those recently cited in the literature validates the results obtained by the proposed study. Finally, a numerical simulation was accomplished to study the effect of confining pressure on the crack propagation process, showing that the SIFs increase and the crack initiation angles change in this case.     

Comparing the Performance of Organic Solvent Nanofiltration Membranes in Non-Polar Solvents

Organic solvent nanofiltration (OSN) is gradually expanding from academic research to industrial implementation. The need for membranes with low and sharp molecular weight cutoffs that are able to operate under aggressive OSN conditions is increasing. However, the lack of comparable and uniform performance data frustrates the screening and membrane selection for processes. Here, a collaboration is presented between several academic and industrial partners analyzing the separation performance of 10 different membranes using three model process mixtures. Membrane materials range from classic polymeric and thin film composites (TFCs) to hybrid ceramic types. The model solutions were chosen to mimic cases relevant to today's industrial use: relatively low molar mass solutes (330–550 Da) in n-heptane, toluene, and anisole.     

UV-shielding by a polyurethane/f-Oil fly ash-CeO2 protective coating

A waste material called oil fly ash (OFA) was acid-functionalized, yielding f-OFA-COOH, which was then reacted with cerium oxide (CeO₂) to make CeO₂-functionalized OFA, or f-OFA-CeO₂. Pristine OFA and f-OFA-CeO₂ were used to make waterborne polyurethane (WBPU) dispersions, referred to as WBPU/OFA and WBPU/f-OFA-CeO₂, respectively, with defined OFA and f-OFA-CeO₂ content. All the dispersions were applied to mild steel as organic coatings to evaluate their protective properties, such as their hydrophobicity, adhesive strength and UV-shielding resistance. These protective properties varied based on the OFA and f-OFA-CeO₂ content. The highest water contact angle, minimum water swelling and maximum adhesive strength were found using WBPU/f-OFA-CeO₂-20 coating (using 2.00 wt% f-OFA-CeO₂), which also showed the maximum ultraviolet (UV) absorption via UV–vis spectroscopy analysis. This UV shielding result also matched field test results, as that coating was found to exhibit the lowest UV degradation near a marine atmosphere, as shown by X-ray photoelectron spectroscopy (XPS) analysis. The least affected hydrophobicity was also recorded for the sample with the WBPU/f-OFA-CeO₂-20 coating.