Influences of rennet and container types on proteolysis of traditional Kurdish cheese during the ripening

The effect of rennet and container types was evaluated on proteolysis of traditional Kurdish cheese during 60 days ripening. The enzymes involved were commercial chymosin and traditional rennet from lamb abomasum. Goat skin (traditional container) and plastic containers were used as storage containers. The trend of proteolysis was determined by measuring the content of nitrogen (N) in compounds soluble in water, 12% trichloroacetic acid and 5% phosphotungstic acid along with the urea–polyacrylamide gel electrophoresis method. The results showed that the nitrogen in compounds soluble in water, 12% trichloroacetic acid and 5% phosphotungstic acid was higher in ripened cheeses into plastic containers; however, the containers had no significant effect on the breakdown of α‐ and β‐caseins (P < 0.05). Using commercial rennet caused the breakdown of α‐ and β‐caseins and the level of nitrogen in compounds soluble in water to increase. Finally, however, the amount of α‐ and β‐caseins breakdown was trivial, and α‐casein was decreased more than β‐casein in all samples.     

Minimization of residual monomer content of superabsorbent hydrogels via alteration of initiating system

Production of superabsorbent polymer hydrogels with minimized residual monomer content is an essential prerequisite particularly in their related hygienic, pharmaceutical, and food packaging products. Effect of two thermodissociating initiators and an innovative two‐step initiation approach on the residual monomer content of acrylic acid‐based SAP was preliminarily investigated. Ammonium persulfate (APS) and 4,4′‐azobis(4‐cyanopentanoic acid) (ACPA) were used as water‐soluble initiators for the aqueous solution polymerization. FTIR spectroscopy was used for the structural characterization. It was found that APS was more effective than ACPA, and residual monomer was determined in the range of 2200–3000 and 8900–16,600 ppm for APS‐ and ACPA‐initiated polymerization products, respectively. Residual monomer resulted from APS/tetramethyl ethylenediamine (TMEDA) initiation system was measured to be about 4500 ± 117 ppm for one step initiation. However, a two‐step initiation strategy using the APS‐TMEDA was exposed to be a very effective method to decrease residual monomer to 212 ± 6 ppm. The variation of the hydrogel properties (i.e., gel content, swelling capacity, and the residual monomer content) versus the initiator system and concentration was discussed based on the basic literature and supported by some rheological evidences obtained from rheoanalysis of water‐swollen samples. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Comparative investigation of the formation of polytetrafluoroethylene nanoparticles on different solid substrates through the adsorption of tetrafluoroethylene

The effects of different solid substrates, including carbon nanofibers (CNFs), activated carbon, alumina, silica, molecular sieves, and poly(N‐vinylpyrrolidione) (PVP), were compared for the high‐pressure synthesis of polytetrafluoroethylene [PTFE or (CF₂)_n] nanoparticles via the adsorption of thermally synthesized tetrafluoroethylene (C₂F₄) as the monomer. Scanning electron microscopy, Fourier transform infrared spectroscopy, and thermogravimetric analysis (TGA) were used for the characterization of the PTFE nanoparticles on different solid substrates. The results demonstrate that the average diameters of the PTFE nanoparticles were about 90 nm for the CNFs, 130 nm for PVP, 150 nm for alumina, and about 200 nm for silica. Also, TGA showed that the amounts of PTFE nanoparticles synthesized on each solid substrate were 3.53 ± 0.09% for CNFs, 2.31 ± 0.10% for PVP, 2.11 ± 0.12% for silica, and 0.97 ± 0.16% for alumina. Depending on the active surface area and the morphology of nanomaterials, such as CNFs, different capacities were evaluated for each solid support in the formation of the PTFE nanoparticles. The quantities and the size of the synthesized PTFE nanoparticles relied on the characteristics of the solid substrate. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

A new compromise solution method for fuzzy group decision-making problems with an application to the contractor selection

This paper presents a novel compromise solution method for solving fuzzy group decision-making problems by a group of experts, which can determine the best alternative by considering both conflicting quantitative and qualitative evaluation criteria in real-life applications. The compromise solution method is developed based on the concept that the chosen alternative should be as close as possible to the positive ideal solution and as far away from the negative ideal solution as possible concurrently. The performance rating values of alternatives versus conflicting criteria as well as the weights of criteria are described by linguistic variables with multi-judges and are converted to triangular fuzzy numbers. Then, a new collective index is introduced to distinguish among potential alternatives in the assessment process with respect to subjective judgment and objective information. Finally, a real case study and an application example for a contractor selection problem are provided in construction industry to demonstrate the implementation process of the proposed method.     

Superposition properties of the phase noise based on the LTV model

Using the Linear Time Variant model, for electrical oscillators, it is shown that the superposition of phase noise spectra generated by individual noise sources leads to incorrect result at low offset frequencies while summation of their phase deviations and calculating the final phase noise will give more accurate result.     

Effect of thermal stresses on stability and frequency response of a capacitive microphone

This article deals with effects of thermal stresses on stability and frequency response of a fully clamped circular microplate, which acts as the diaphragm of a capacitive MEMS microphone. Static and dynamic pull-in phenomena limit the stable regions of a capacitive MEMS microphone. The results show that the non-dimensional static pull-in voltage of the studied case is about 5.23 (38.6 V). On the other hand, according to the results, the non-dimensional dynamic pull-in of the diaphragm is about 4.74 (34.98 V), which is as low as 90.63% of the static pull-in threshold. Because of the thermal expansion coefficient, diaphragm temperature increment leads to compressive thermal stresses and conversely, decrement of the diaphragm temperature creates tensile thermal stresses. The effect of temperature on the pull-in parameters is given by a design-correcting factor.As results demonstrate, the deflection of the diaphragm subjected to a given electrostatic force can be controlled by means of the temperature changes. In the absence of electrostatic force, as the results show, although temperature changes do not create any deflection, but for a critical temperature increment the diaphragm stiffness vanishes and the buckling phenomenon takes place. Effects of the electrostatic force and the temperature variation on the frequency response of the microphone subjected to a sound pressure wave are investigated. As the results illustrate, increment of the electrostatic force or increment of the diaphragm temperature increases the output level and sensitivity of the microphone and decreases the fundamental frequency of the microphone, limiting the upper band of its bandwidth. It is obvious that decrement of the diaphragm temperature acts conversely. In addition, the results show that in the presence of the electrostatic force sensitivity of the output level of the diaphragm to the temperature change increases.     

STUDY ON THE ACETYLENE HYDROGENATION PROCESS FOR ETHYLENE PRODUCTION: SIMULATION,MODIFICATION, AND OPTIMIZATION

In this study, an industrial acetylene hydrogenation unit is simulated utilizing three available kinetic models. The results are compared against six-day experimental data and the best model is selected. Effects of feed temperature and the amount of injected hydrogen on ethylene selectivity are also studied. According to the simulation results, the unit is not working under its optimum conditions. Furthermore, by reduction of the hydrogen flow rate to 52 kg/h, process selectivity is increased. In addition, a new approach is proposed to modify the hydrogenation process and reduce undesired by-products. In the simulation of the modified process, hydrogenation reactors temperature, hydrogen flow rate, and H-1/H-2 ratio were regulated as adjustable parameters for the process optimization. The simulation shows that ethylene selectivity increases by 12%, while acetylene concentration and hydrogenation reactor temperature remains within acceptable ranges. Such selectivity could be achieved at the hydrogen flow rate of 50 kg/h with H-1/H-2 ratio of 0.1/0.9.     

Minimum risk sequential point estimation of the stress-strength reliability parameter for exponential distribution

Abstract

In this article, using purely and two-stage sequential procedures, the problem of minimum risk point estimation of the reliability parameter (R) under the stress–strength model, in case the loss function is squared error plus sampling cost, is considered when the random stress (X) and the random strength (Y) are independent and both have exponential distributions with different scale parameters. The exact distribution of the total sample size and explicit formulas for the expected value and mean squared error of the maximum likelihood estimator of the reliability parameter under the stress–strength model are provided under the two-stage sequential procedure. Using the law of large numbers and Monte Carlo integration, the exact distribution of the stopping rule under the purely sequential procedure is approximated. Moreover, it is shown that both proposed sequential procedures are finite and for special cases the exact distribution of stopping times has a degenerate distribution at the initial sample size. The performances of the proposed methodologies are investigated with the help of simulations. Finally, using a real data set, the procedures are clearly illustrated.     

The Effect of Temperature and Pressure on the Reversibility of Asphaltene Precipitation

A lot of hindrances are seen in petroleum operation, production, and transportation as a results of factors that related to asphaltene precipitation. It has great importance to investigate the reversibility of asphaltene precipitation under changes of effective factors on thermodynamic conditions such as pressure, temperature, and composition. In the present work the reversibility of asphaltene precipitation under changes of pressure and temperature was investigated for two kind of Iranian heavy oil. The stability test shows these samples are located at unstable region in aspect of asphaltene precipitation. The experimental procedure includes two parts, (a) decreasing pressure from initial reservoir pressure to near saturation pressure and surveying asphaltene content hysteresis with redissolution process at reservoir temperature, and (b) investigation of precipitated asphaltene in both precipitation and redissolution processes at different temperature and reservoir pressure. At each step IP143 standard test was used to measure precipitated asphaltene. It was concluded that above bubble point pressure, asphaltene precipitation is nearly reversible with respect to pressure for both samples and it was partially reversible with respect to the temperature for sample A, and accordingly pressurizing is acceptable method for solving the problem in both heavy asphaltenic crude oil samples and increasing temperature is acceptable method for solving asphaltene problem in crude oil sample A. Also density measurement of flashed oil confirmed that there is a little hysteresis in asphaltene content during redissolution and precipitation processes.     

Bit Selection Optimization Using Artificial Intelligence Systems

Abstract

Optimizing bit selection is one of the main challenges in drilling operations. Bit selection is based on the recorded performance of similar bits from offset wells. There are too many parameters intervening in drilling bit selection. Therefore, developing a logical relationship between them to assist in proper bit selection is extremely necessary and complicated though. In such a case, artificial neural networks (ANNs) have proven to be helpful in recognizing the complex relationship between variables. In this new approach, two models are developed with high proficiency using ANNs. The first model provides appropriate drilling bit selection based on the desired rate of penetration (ROP) to be obtained by applying specific drilling parameters. The second model uses proper drilling parameters obtained from an optimizing procedure to select the drilling bit that provides the maximum achievable rate of penetration. Genetic algorithms (GAs), as a class of optimizing methods for complex functions, are applied to help bit optimization and its related drilling parameters. With the given data sets, these new models predicted successfully the bit types and the optimum drilling parameters. The correlation coefficients for the predicted bit types and optimum drilling parameters in testing the obtained networks are 0.96 and 0.86, respectively. MATLAB software was used to perform ANN and GA solutions.