Irradiation grafting of methyl methacrylate monomer onto ultra‐high‐molecular‐weight polyethylene: An experimental design approach for improving adhesion to bone cement

The grafting of methyl methacrylate (MMA) onto ultra‐high‐molecular‐weight polyethylene (UHMWPE) and chromic acid etched UHMWPE was conducted with a preirradiation method in air in the presence of a Mohr salt and sulfuric acid. The grafted samples were characterized by Fourier transform infrared (FTIR) spectroscopy, a gravimetric method, differential scanning calorimetry, scanning electron microscopy (SEM), and interfacial bonding strength measurements. The FTIR results showed the presence of ether and carbonyl groups in the MMA‐grafted UHMWPE (MMA‐g‐UHMWPE) samples. The Taguchi experimental design method was used to find the best degree of grafting (DG) and bonding strength. The efficient levels for different variables were calculated with an analysis of variance of the results. SEM micrographs of MMA‐g‐UHMWPE samples showed that with increasing DG and chromic acid etching, the MMA‐g‐UHMWPE rich phase increased on the surface; this confirmed the high interfacial bonding strength of the grafted samples with bone cement. The grafting of the MMA units onto UHMWPE resulted in a lower crystallinity, and the crystallization process proceeded at a higher rate for the MMA‐g‐UHMWPE samples compared to the initial UHMWPE; this suggested that the MMA grafted units acted as nucleating agents for the crystallization of UHMWPE. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

Synthesis and characterization of (poly (N-vinyl formamide)—pregelled starch—graft copolymer

Bromate / cyclohexanone redox system was investigated as a novel initiator for graft copolymerization of N-vinyl formamide onto pregelled starch. A number of variables in the grafting reaction were investigated including N-vinyl formamide, cyclohexanone, bromate ion, sulphuric acid and pregelled starch concentrations, material to liquor ratio along with polymerization time and temperature. The graft copolymers were evaluated in terms of graft yield, graft reaction efficiency and homopolymer formation (%). The optimum conditions for grafting of N-vinyl formamide onto pregelled starch are: N-vinyl formamide 50% based on weight of substrate, cyclohexanone 15 mmol / l, bromate ion, 30 mmol / l, liquor ratio 10, pH 6, time 120 min., and temperature 40°C. On the other hand, characterizations of the resultant copolymers with respect to swelling capacity, solubility %, metal ion up-take and suitability as a sizing agent for cotton textiles were investigated. The results obtained reflect that, the resultant copolymer shows better results for the aforementioned properties in comparison with that obtained from native pregelled starch as a starting substrate.     

Experimental determination of gas holdup and volumetric mass transfer coefficient in a jet bubbling reactor

The hydrodynamics and mass transfer characteristics of a lab-scale jet bubbling reactor(JBR) including the gas holdup, volumetric mass transfer coefficient and specific interfacial area were assessed experimentally investigating the influence of temperature, p H and superficial gas velocity. The reactor diameter and height were 11 and 30 cm,respectively. It was equipped with a single sparger, operating at atmospheric pressure, 20 and 40℃, and two p H values of 3 and 6. The height of the liquid was 23 cm, while the superficial gas velocity changed within 0.010–0.040 m·s~(-1) range. Experiments were conducted with pure oxygen as the gas phase and saturated lime solution as the liquid phase. The liquid-side volumetric mass transfer coefficient was determined under unsteady-state oxygen absorption in a saturated lime solution. The gas holdup was calculated based on the liquid height change, while the specific interfacial area was obtained by a physical method based on the bubble size distribution(BSD) in different superficial gas velocities. The results indicated that at the same temperature but different p H, the gas holdup variation was negligible, while the liquid-side volumetric mass transfer coefficient at the p H value of 6 was higher than that at the p H = 3. At a constant p H but different temperatures, the gas holdup and the liquid-side volumetric mass transfer coefficients at 40℃ were higher than that of the same at 20℃. A reasonable and appropriate estimation of the liquid-side volumetric mass transfer coefficient(kla) in a pilot-scale JBR was provided which can be applied to the design and scale-up of JBRs.     

Introducing a proper hydrogen liquefaction concept for using wasted heat of thermal power plants-case study: Parand gas power plant

A hydrogen liquefaction concept with an innovative configuration and a capacity of 4 kg·s^-1 (345.6 t·d^-1) is developed. The concept involves an ammonia absorption refrigeration system for the pre-cooling of hydrogen and MR streams from 25 ℃ to -30 ℃. The ammonia absorption refrigeration system is fed by exhaust gases of the Parand gas power plant that are normally dissipated to the environment with a temperature of 546 ℃. The simulation is performed by Aspen HYSYS V9.0, using two separate equations of state for simulating hydrogen and MR streams to gain more accurate results especially for ortho-para conversion. Results show that conversion enthalpy estimated by Aspen HYSYS, fits very well to the experimental data. Determining the important independent variables and composition of MRs are done using trial and error procedure, a functional and straightforward method for complicated systems. The minimum temperature limit in the cooling section is lowered, and an ortho-para converter is implemented in this section. The proposed concept performs well from energy aspects and leads to COP and SEC equal to 0.271 and 4.54 kW·h·kg^-1, respectively. The main advantage of this study is in the low SEC, eliminating the losses of the distribution network, and improving the ability of the hydrogen liquefaction for energy storage in off-peak times.     

Development of multilayer perceptron artificial neural network (MLP-ANN) and least square support vector machine (LSSVM) models to predict Nusselt number and pressure drop of TiO2/water nanofluid flows through non-straight pathways

Abstract

In this study, Multilayer Perceptron Artificial Neural Network (MLP-ANN) model and Least Square Support Vector Machine (LSSVM), were developed to predict the thermal performance and pressure loss of nanofluid flow through coils as non-straight pathways. There different coils with various curvature ratios and coil pitches were constructed and used. Stable TiO₂ (50?nm)/water nanofluid in different concentrations from 0.0 to 2.0% were prepared using appropriate method. As it is expected, considerable enhancement of heat transfer was achieved by application of nanofluids instead of water in system. Volume concentration of nanofluid, Prandtl number (ranging from 4.82 to 9.11) and Helical number (106.80 to 1282.87) were introduced to the developed models to obtain Nusselt number (9.89 to 53.30) and pressure drop (291.35 to 18784?kPa) as the output data of the models. According to the output results of developed models, MLP-ANN model was able to predict both Nusselt number and pressure drop of nanofluid flow more precisely in comparison to LSSVM model. The developed MLP model of this study exceeded LSSVM model to high correlation coefficient value of 0.97.     

Radioactivity calculations for production of <Superscript>131</Superscript>I by neutron irradiation of tellurium targets

The radioactivity of ¹³¹I as well as of the other radioisotopes, obtained from different nuclear reactions occurring during irradiation of natural tellurium including different decay branches, at the end of irradiation and after different cooling times was calculated. The optimum cooling time is 90 h. Published in Russian in Radiokhimiya, 2009, Vol. 51, No. 3, pp. 275–281. The text was submitted by the author in English.     

Modeling the efficiency of top Arab banks: A DEA–neural network approach

This study investigates the efficiency of top Arab banks using two quantitative methodologies: data envelopment analysis and neural networks. The study uses a probabilistic neural network (PNN) and a traditional statistical classification method to model and classify the relative efficiency of top Arab banks. Accuracy indices are used to assess the classification accuracy of the models. Results indicate that the predictive accuracy of NN models is quite similar to that of traditional statistical methods. The study shows that the NN models have a great potential for the classification of banks’ relative efficiency due to their robustness and flexibility of modeling algorithms. From a policy perspective, this study highlights the economic importance of encouraging increased efficiency throughout the banking industry in the Arab world.     

Shades of green: A psychographic segmentation of the green consumer in Kuwait using self-organizing maps

This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on green consumption in Kuwait. SOM is a machine learning method that can be used to explore patterns in large and complex datasets for linear and non-linear patterns. The results show that major variables affecting green consumption are related to altruistic values, environmental concern, environmental knowledge, skepticism towards environmental claims, attitudes toward green consumption, and intention to buy green products. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data.     

Evolving comprehensible neural network trees using genetic algorithms

This paper presents a comprehensible neural network tree (CNNTREE). CNNTREE is a proposed general modular neural network structure, where each node in this tree is a comprehensible expert neural network (CENN). One advantage of using CNNTREE is that it is a “gray box”; because it can be interpreted easily for symbolic systems; where each node in the CNNTREE is equivalent for symbolic operator in the symbolic system. Another advantage of CNNTREE is that it can be trained as any normal multi layer feed forward neural network. An evolutionary algorithm is given for designing the CNNTREE. Back propagation is also checked as local learning algorithm that fits for real time learning constraints. The tree generalization and training performance are examined using experiments with a digit recognition problem. The article is published in the original. Elsayed Mostafa. Received the B.Sc. degree in electrical (Communication) Eng., Cairo University at 1967. Dipl.-Ing, and Dr-Ing. from Stuttgart University at 1977, 1981 respectively. He is a member of ECS and EEES. He is currently a professor of electronic circuits, Faculty of Engineering, University of Helwan. Amr Kamel. Graduated from Computer Department, Faculty of Engineering of Ain Shams University, Egypt in 1999, and studying M.Sc. degree in computer engineering from the Faculty of Engineering of Helwan University. His special fields of interest include neural networks and genetic algorithms. Alaa Hamdy. Was born in Giza in Egypt, on August 17, 1966. He graduated from the Telecommunications and Electronics Department, Faculty of Engineering and Technology of Helwan University, Cairo, Egypt in 1989. He received the M.Sc. degree in computer engineering from the same university in 1996 and the Ph.D. degree from the Faculty of Electrical Engineering, Poznan University of Technology, Poland in 2004. Currently he is working as a lecturer in the Faculty of Engineering of Helwan University. His special fields of interest, include image processing, pattern analysis, and machine vision.     

Long term stability assessment of Siah Bisheh powerhouse cavern based on displacement back analysis method

Siah Bisheh pumped storage powerhouse cavern with complex geometry, changeable geological formations and diverse geotechnical properties of rocks, is under construction on the Chalus River at the north of Iran. Powerhouse cavern is located near the lower dam reservoir and its crown is more than 30 m down the lower dam maximum lake level. After impounding of lower dam, powerhouse region will be located under saturated condition. Therefore long term stability assessment of the powerhouse cavern under saturated condition is unavoidable. In this study, displacement based direct back analysis using univariate optimization algorithm were applied and geomechanical properties of rocks, stress ratio and joints parameters were identified. Numerical modeling results are in good agreement with measured displacements using extensometers which confirm the numerical modeling accuracy and back analysis results. Then ordinary analysis of powerhouse cavern under natural condition using back analysis results were carried out. Results of analysis shows that powerhouse cavern is stable under natural condition and existing support system has suitable efficiency and could effectively control displacements. Finally, powerhouse cavern long term stability under saturated condition was analyzed. Results of analysis shows that after lower dam impounding, pore water pressure and uplift pressure in discontinuities around powerhouse cavern will arose and tend to local failure of powerhouse cavern in region 2nd and 3rd instrumentation arrays. To obtain powerhouse long term stability, it is recommended to construct a cutoff curtain around powerhouse cavern.