Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques

This paper investigates the ability of four artificial intelligence techniques, including artificial neural network (ANN), radial basis neural network (RBNN), adaptive neuro-fuzzy inference system (ANFIS) with grid partitioning, and ANFIS with fuzzy c-means clustering, to predict the peak and residual conditions of actively confined concrete. A large experimental test database that consists of 377 axial compression test results of actively confined concrete specimens was assembled from the published literature, and it was used to train, test, and validate the four models proposed in this paper using the mentioned artificial intelligence techniques. The results show that all of the neural network and ANFIS models fit well with the experimental results, and they outperform the conventional models. Among the artificial intelligence models investigated, RBNN model is found to be the most accurate to predict the peak and residual conditions of actively confined concrete. The predictions of each proposed model are subsequently used to study the interdependence of critical parameters and their influence on the behavior of actively confined concrete.

     

Green bio-synthesis of Ni/montmorillonite nanocomposite using extract of Allium jesdianum as the nano-catalyst for electrocatalytic oxidation of methanol

In this work, synthesis of Ni nanoparticles was carried out successfully by water extract of Allium jesdianum as a biochemical reducing agent in the presence of montmorillonite clay (MMT) as a natural solid support for the first time. Then the electrochemical activity of the synthesized nanocomposite was investigated in methanol electrocatalytic oxidation. MMT with high cation exchange capacity and nano layer structure was exposed to ion exchange conditions in nickel solution. Then Ni²⁺ ion exchanged form was used in this process as a source of ions and also capping agent. Water extract of Allium jesdianum used as a reducing agent due to abundant availability of phenolic and flavonoid contents. The synthesized Ni/MMT nanocomposite was characterized using UV-Vis spectroscopy (UV-Vis), Fourier Transform Infrared Spectroscopy (FT-IR), X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission electron microscopy (TEM) and Energy-dispersive X-ray spectroscopy (EDX). The surface of prepared modified electrode has been characterized using SEM to evaluate the morphology, showing uniform dispersion of Ni nanoparticles with mean diameter of 12 to 20 nm. The modified carbon paste electrode was then used in methanol electrocatalytic oxidation reaction. Methanol oxidation on the proposed modified electrode surface occurs at 0.6 V and 0.3 V in alkaline and acidic medium respectively. Also, the results showed the better performance of modified electrode toward methanol electrocatalytic oxidation in comparison with carbon paste electrode that is modified by ion exchanged MMT. Charge transfer coefficients and apparent charge transfer rate constant for the modified electrode in the absence of methanol in alkaline medium were respectively found as: α_a = 0.53, α_c = 0.37 and k_s = 1.6×10^-1 s^-1. Also, the average value of catalytic rate constant for the electrocatalytic oxidation of methanol by the prepared nano-catalyst was estimated to be about 0.9 L·mol^-1·s^-1 by chronoamperometry technique. The prepared electrode was also effective for electrocatalytic oxidation of ethanol and formaldehyde in alkaline medium.     

Prediction of biodiesel properties and its characterization using fatty acid profiles

Biodiesel, the mono-alkyl esters of vegetable oils or animal fats, is an eco-friendly alternative to petrodiesel. The molecular structures of biodiesels, fatty acid methyl esters were applied to predict the characteristics of biodiesel fuels. Based on the structural similarity of biodiesel and petroleum fractions, molecular weight of biodiesel was correlated with other characteristics including boiling point, viscosity and specific-gravity in the form of three equations. For 24 different kinds of biodiesel, the minimum average relative deviation (ARD) of these correlations was calculated to be 0.68%. Moreover, two correlations were developed to predict viscosity and flash point of biodiesel as a function of weighted-average number of carbon atoms (N _C ) and weighted-average number of double bonds (N _DB ) with ARD 3.72% and 4.24% respectively. Also, a high degree of correlation was shown by the logarithmic function with ARD 0.30% between specific gravity and viscosity of biodiesel. Proposed predictive models were verified by experimental data.     

Mathematical modelling of CO2 facilitated transport through liquid membranes containing amines as carrier

An approximate analytical solution for the facilitated transport of carbon dioxide across a liquid membrane containing aqueous primary and secondary amine solutions has been developed in which the reversible reaction A(CO₂) + 2B(amine) AB(carbamate) + BH(protonated amine) occurs inside the liquid membranes. The current approximate analytical solution predicts the facilitated factor of CO₂ through the membrane and is based on the dimensionless, nonlinear diffusion‐reaction transport problem. The approximate analytical solution predicts the facilitation factors for the range from the moderate reaction rate to the equilibrium chemical reaction regime, allowing unequal diffusivities of complexes and carrier and cases of zero and nonzero permeate side solute concentrations. In the present mathematical model, a constant free carrier concentration is assumed throughout the membrane phase. In comparison with the numerically calculated results, the results obtained were found to be in well agreement.     

Sintered porous heat sink for cooling of high-powered microprocessors for server applications

This paper experimentally investigates the sintered porous heat sink for the cooling of the high-powered compact microprocessors for server applications. Heat sink cold plate consisted of rectangular channel with sintered porous copper insert of 40% porosity and 1.44 × 10^?11 m² permeability. Forced convection heat transfer and pressure drop through the porous structure were studied at Re ? 408 with water as the coolant medium. In the study, heat fluxes of up to 2.9 MW/m² were successfully removed at the source with the coolant pressure drop of 34 kPa across the porous sample while maintaining the heater junction temperature below the permissible limit of 100 ± 5 °C for chipsets. The minimum value of 0.48 °C/W for cold plate thermal resistance (R_cp) was achieved at maximum flow rate of 4.2 cm³/s in the experiment. For the designed heat sink, different components of the cold plate thermal resistance (R_cp) from the thermal footprint of source to the coolant were identified and it was found that contact resistance at the interface of source and cold plate makes up 44% of R_cp and proved to be the main component. Convection resistance from heated channel wall with porous insert to coolant accounts for 37% of the R_cp. With forced convection of water at Re = 408 through porous copper media, maximum values of 20 kW/m² K for heat transfer coefficient and 126 for Nusselt number were recorded. The measured effective thermal conductivity of the water saturated porous copper was as high as 32 W/m K that supported the superior heat augmentation characteristics of the copper–water based sintered porous heat sink. The present investigation helps to classify the sintered porous heat sink as a potential thermal management device for high-end microprocessors.     

Examining potential benefits of combining a chimney with a salinity gradient solar pond for production of power in salt affected areas

The concept of combining a salinity gradient solar pond with a chimney to produce power in salt affected areas is examined. Firstly the causes of salinity in salt affected areas of northern Victoria, Australia are discussed. Existing salinity mitigation schemes are introduced and the integration of solar ponds with those schemes is discussed. Later it is shown how a solar pond can be combined with a chimney incorporating an air turbine for the production of power. Following the introduction of this concept the preliminary design is presented for a demonstration power plant incorporating a solar pond of area 6 hectares and depth 3 m with a 200 m tall chimney of 10 m diameter. The performance, including output power and efficiency of the proposed plant operating in northern Victoria is analysed and the results are discussed. The paper also discusses the overall advantages of using a solar pond with a chimney for production of power including the use of the large thermal mass of a solar pond as a practical and efficient method of storing collected solar energy.     

A Predictive Correlation for Dynamic Viscosity of Fatty Acid Methyl Esters and Biodiesel

The viscosity of biodiesel, which is a mixture of fatty acid methyl esters (FAME), is an important physical property in the injection and efficient combustion. In this study, a simple correlation, with only one adjustable parameter, is proposed for predicting the viscosity of FAME and their mixtures (biodiesel) as a function of temperature. First, the adjustable parameter of the correlation is calculated for various FAME. The average absolute relative deviation (AARD) is obtained to be 0.97% for 226 data points. Second, the adjustable parameter of FAME is connected to the number of carbon atoms and the number of double bonds to build a predictive correlation for the calculation of viscosity. The AARD for 226 data points is obtained to be 2.28%. Third, the proposed model is employed to predict the viscosity of biodiesel without introducing any new adjustable parameter. To predict the viscosity of biodiesel, the average of the adjustable parameter is applied to the correlation. The AARD of 2.96% is obtained for 185 data points comprised of 23 different biodiesels. To better understand the ability of the correlation in the estimation of the viscosity of biodiesel and FAME, a comparison is made between the present correlation and a number of correlations available in the literature.     

Heat extraction from salinity-gradient solar ponds using heat pipe heat exchangers

This paper presents the results of experimental and theoretical analysis on the heat extraction process from solar pond by using the heat pipe heat exchanger. In order to conduct research work, a small scale experimental solar pond with an area of 7.0 m² and a depth of 1.5 m was built at Khon Kaen in North-Eastern Thailand (16°27′N102°E). Heat was successfully extracted from the lower convective zone (LCZ) of the solar pond by using a heat pipe heat exchanger made from 60 copper tubes with 21 mm inside diameter and 22 mm outside diameter. The length of the evaporator and condenser section was 800 mm and 200 mm respectively. R134a was used as the heat transfer fluid in the experiment. The theoretical model was formulated for the solar pond heat extraction on the basis of the energy conservation equations and by using the solar radiation data for the above location. Numerical methods were used to solve the modeling equations. In the analysis, the performance of heat exchanger is investigated by varying the velocity of inlet air used to extract heat from the condenser end of the heat pipe heat exchanger (HPHE). Air velocity was found to have a significant influence on the effectiveness of heat pipe heat exchanger. In the present investigation, there was an increase in effectiveness by 43% as the air velocity was decreased from 5 m/s to 1 m/s. The results obtained from the theoretical model showed good agreement with the experimental data.     

Surface Tension Estimation of Binary Mixtures of Organic Compounds Using Artificial Neural Networks

The surface tension of binary mixtures at different temperatures and compositions is required in much scientific and technological research. Therefore, having an exact correlation between surface tension and easily accessible physical properties is essential. In this work, the sensitivity of the surface tension to some physical properties was studied by using artificial neural networks (ANNs) to find the most effective ones. Furthermore, ANNs were used to estimate the surface tension of binary systems as a function of the most effective physical properties including critical pressure, reduced temperature, acentric factor, and molar density. The experimental data, collected for training and verifying the networks, include various materials such as alkanes, alkenes, aromatics, alcohols, organic acids, as well as chlorine, iodine, sulfur, nitrogen, and fluorine-containing compounds in the composition ranges from 0 to 100 mole percent, and temperatures between 116 K and 393 K. The average absolute relative deviation (AARD) of the most accurate network, obtained for all 2038 data points regarding 83 binary mixtures, is 1.75%.