فهرست مطالب محمد طبسی زاده

 The basic limitation of biopolymers compared to the petroleum-based polymers is their weak physical and mechanical properties. In recent years, efforts have been made to properly incorporate nanoparticles into the polymer to reduce the limitation of their use in the packaging industry by partially improving the physical and mechanical properties of these films. This study aimed to incorporate zinc oxide nanoparticles into gelatin-biopolymer-based nanocomposite films, in order to improve their physical, mechanical and thermal properties.

Gelatin-based nanocomposite films were prepared by adding different amounts of zinc oxide nanoparticles (0, 0.5, 1.5 and 3%) using the so-called casting method. By performing several tests, different properties of the manufactured nanocomposite films including thickness, density, water vapor permeability, mechanical properties, degree of transparency, color properties and finally, their biodegradability were investigated.

the results showed that increasing the concentration of zinc oxide nanoparticles increased the tensile strength and decreased the elongation-at-break of this biofilm. The results of physical tests showed that increase in nanoparticles concentration reduced the permeability to water vapor from 0.76 to 0.48. Incorporating zinc oxide nanoparticles affected the transparency of the biofilms i.e. their transparency reduced by increasing nanoparticles concentration. By adding nanoparticles to gelatin-based films, thermal properties including glass transition temperature and melting temperature increased. Also, the thermal stability of the biofilms increased from 533.38°C (0.5% nanoparticles) to 559.53°C (1.5% nanoparticles). The results of biodegradability in soil and light showed that with increasing the concentration of nanoparticles, the biodegradability was reduced. This is mainly due to the addition of nanoparticles, which results in a greater bond strength between the components, and consequently the delay in biodegradation.

In the current paper, the optimum conditions for bioethanol production from sugars were surveyed. Then the operational condition effect on some physical characteristics of bioethanol fuel was investigated. 27 types of bioethanol samples from sugarcane molasses, sugar beet molasses and sugar cane juice were produced at anaerobic fermentation condition in three different temperature levels of 25 ˚C, 30˚c and 35 ˚C and in three different time levels of 24h, 48 h and 72 h. Then the values of density, kinematic viscosity and flash point were calculated in triplets. The results showed that with increasing the temperature and time processing, the viscosity, density and flashpoint would be decreased. The sugar type had a significant effect on these parameters. The closest value of viscosity to its standard amount was related to sugarcane molasses, producing at 25 ˚C and after 72 h fermentation, which was 1.5174 cSt. The nearest amount of density to its standard value was reported 0.9560 which was attributed to sugarcane juice producing at 35˚C and after 72 hours and for flashpoint it was 24.5 ˚C which belonged to sugarcane juice at 30 ˚C and 72 hours. The most bioethanol concentration was 74 gr.L-1 which was produced from sugarcane juice at 35 c◦ and after 72h.

In recent years, with development of industrial products with complex and precise systems, the demand for CNC machines has been increasing, and as its technology has been progressed, more failure modes have been developed with complex and multi-purpose structures. The necessity of CNC machines’ reliability is also more evident than ever due to its impact on production and its implementation costs. Aiming at reducing the risks and managing the performance of the CNC machine parts in order to increase the reliability and reduce the stop time, it is important to identify all of the failure modes and prioritize them to determine the critical modes and take the proper cautionary maintenance actions approach.      In this study, conventional and fuzzy FMEA, which is a method in the field of reliability applications, was used to determine the risks in mechanical components of CNC lathe machine and all its potential failure modes. The extracted information was mainly obtained by asking from CNC machine experts and analysts, who provided detailed information about the CNC machining process. These experts used linguistic terms to prioritize the S, O and D parameters. In the conventional method, the RPN numbers were calculated and prioritized for different subsystems. Then in the fuzzy method, first the working process of the CNC machine and the mechanism of its components were studied. Also, in this step, all failure modes of mechanical components of the CNC and their effects were determined. Subsequently, each of the three parameters S, O, and D were evaluated for each of the failure modes and their rankings. For ranking using the crisp data, usually, the numbers in 1-10 scale are used, then using linguistic variables, the crisp values are converted into fuzzy values (fuzzification). 125 rules were used to control the output values for correcting the input parameters (Inference). For converting input parameters to fuzzy values and transferring qualitative rules into quantitative results, Fuzzy Mamdani Inference Algorithm was used (Inference). In the following, the inference output values are converted into non-fuzzy values (defuzzification). In the end, the fuzzy RPNs calculated by the fuzzy algorithm and defuzzified are ranked. In conventional FMEA method, after calculating the RPNs and prioritizing them, the results showed that this method grouped 30 subsystems into 30 risk groups due to the RPN equalization of some subsystems, while it is evident that by changing the subsystem, the nature of its failure and its severity would vary. Therefore, this result is not consistent with reality. According to the weaknesses of this method, fuzzy logic was used for better prioritization. In the fuzzy method, the results showed that, in the 5-point scale, with the Gaussian membership function and the Centroid defuzzification method, it was able to prioritize subsystems in 30 risk groups. In this method, gearboxes, linear guideway, and fittings had the highest priority in terms of the criticality of failure, respectively. The results of the fuzzy FMEA method showed that, among the mechanical systems of CNC lathe machine, the axes components and the lubrication system have the highest FRPNs and degree of criticality, respectively. Using the fuzzy FMEA method, the experts' problems in prioritizing critical modes were solved. In fact, using the linguistic variables enabled experts to have a more realistic judgment of CNC machine components, and thus, compared to the conventional method, the results of the prioritization of failure modes are more accurate, realistic and sensible. Also, using this method, the limitations of the conventional method were reduced, and failure modes were prioritized more effectively and efficiently. Fuzzy FMEA is found to be an effective tool for prioritizing critical failure modes of mechanical components in CNC lathe machines. The results can also be used in arranging maintenance schedule to take corrective measures, and thereby, it can increase the reliability of the machining process.

In this study, the effective parameters on the amount of biodiesel production from date seed oil via three heating methods of conventional, microwave, and ultrasonic were assessed. In order to carry out the biodiesel production process, the esterification reaction using sulfuric acid as catalyst was firstly performed to reduce the free fatty acid content of date seed oil to the standard amount for transesterification reaction. The results showed that 1 wt.% of catalyst and 8-9 molar ratios of methanol to oil were the optimum amount for all of biodiesel production methods. In other words, utilized method has no effect on the amount of catalyst and methanol in the reaction. The temperature of 56 °C was selected as an optimum temperature in the conventional heating method and the power of around 300 W provided the highest yield in both microwave and ultrasonic methods. However, the major different was in the reduction of reaction time. It decreased from 90 min for conventional method to equilibrium point 7.5 and 3 min for ultrasonic and microwave methods, respectively. According to energy consumption, microwave method was known more suitable than ultrasonic method. The results of modeling presented that suggested models were in good agreement with the experimental values. Methanol/oil molar ratio and reaction temperature in the conventional method, amount of catalyst and methanol in the microwave method and power of ultrasonic waves in the ultrasonic method had the highest influence on the conversion of date seed oil to biodiesel.

Performance optimization of diesel engines is the subject of most researchers in the field of internal combustion engine in recent decades. One of the the most effective ways toward optimizing the performance of heating systems is exergy analysis. In this study, energy and exergy concepts are applied to evaluate the performance of a compression ignition engine with premixed gasoline fuel at various loads and constant speed. To reach proper performance of diesel engine with premixed fuel, in addition to first and second-law efficiencies, exhaust emission characteristics are reviewed. The results of this study indicated that greater portion of exergy transfer was due to the irreversibility, averaged 50% while at high loads exergy destruction rate decreased to %5 by using premixed gasoline fuel. Also, energy and exergy losses through exhaust emissions with premixed fuel will be reduced to about %8.5 and %16/5 (averaged on all engine loads), respectively. The use of premixed fuel gasoline can increase energy and exergy efficiency (maximum 8% at high loads) at all loads while NOX pollutants will be reduced. It was also found that both HC and CO emissions with premixed gasoline fuel will be increased at all loads.

In this research drying kinetics, moisture diffusivity and determination of most appropriate mathematical modeling and activation energy of Mazafati dates species were studied under a cabinet solar dryer.Drying experiments were carried out at three air temperatures of 50, 65, and 80ºC and three air velocities of 1.0, 1.5, and 2.0 m/s. Effect of temperature and air velocity evaluated on drying time and shrinkage by using a completely randomized statistical design. Results showed that the effects of temperature and air velocity are significant on drying time. The average increase of temperature from 50ºC to 80ºC caused the drying time to decrease by 70.77%. For determination of most appropriate model, the highest value of R2 and the lowest values of c2 and RMSE were used. For mathematical modeling, eight empirical models were fitted on experimental data and the best model was selected. The result of regression analysis showed that Page model has the best fitting with data. Moreover, the lowest and highest value of moisture diffusivity were obtained as 4.309×10-10 and 0.0188×10-9 m2/s at temperatures of 80°C and 50°C, respectively. Also the values of activation energy in the drying of Mazafati dates species were found between 31.22 to 42.27 kJ/mol.

Depletion of fossil fuels and environmental degradation are two major problems faced by the world. Today fossil fuels take up to 80% of the primary energy consumed in the world, of which 58% is consumed by the transport sector alone (Mard et al., 2012). The combustion products cause global warming, which is caused of emissions like carbon monoxide (CO), sulfur dioxide (SO2) and nitrogen oxides (NOX). Thus it is essential that low emission alternative fuels to be developed for useing in diesel engines. Many researchers have concluded that biodiesel holds promise as an alternative fuel for diesel engines. Biodiesel is oxygenated, biodegradable, non-toxic, and environmentally friendly (Qi et al., 2010).
In this study transesterification method was used to produce biodiesel, because of its simplicity in biodiesel production process and holding the highest conversion efficiency.
Transesterification of poultry fat oil and the properties of the fuels: Fatty acid methyl ester of poultry fat oil was prepared by transesterification of oil with methanol in the presence of KOH as catalyst. The fuel properties of poultry fat oil methyl ester and diesel fuel were determined. These properties are presented in Table 1.
Tests of engine performance and emissions: After securing the qualitative characteristics of produced biodiesel, different biodiesel fuels of 5%, 10%, 15%, and 20% blended with diesel fuel were prepared. A schematic diagram of the engine setup is shown in Fig.1. The MF-399 tractor engine was used in the tests. The basic specifications of the engine are shown in Table 3. The engine was loaded with an electromagnetic dynamometer. The Σ5 model dynamometer manufactured by NJ-FROMENT was used to measure the power and the torque of the tractor engine. The speed range and capacity of this device are shown in Table 2. A FTO Flow Meter, manufactured by American FLOWTECH Company, was used to measure the fuel consumption (Fig.3). Its measuring range is 37-1537 ml min-1.
The engine performance was evaluated in terms of engine power, engine torque and specific fuel consumption at different engine speeds. The variation of engine torques with B5, B10, B15, B20 and diesel fuel are presented in Fig. 4. The engine torque for biodiesel blends was more than that by diesel fuel only. The mean engine torques for B5, B10, B15 and B20 were 2.5%, 2.8%, 3%, and 3.5% higher than that by only diesel, respectively. This is due to the better combustion of biodiesel compared to diesel fuel. The variation of engine powers with B5, B10, B15, B20 and diesel fuel are presented in Fig. 5. The engine powers for biodiesel blends were more than that by diesel fuel. The mean engine powers for B5, B10, B15 and B20 were higher than that by diesel by 2.5%, 3%, 3.5%, and 4%, respectively. This is because of good combustion of biodiesel resulted from higher oxygen content. The mean specific fuel consumptions for B5, B10, B15 and B20 were higher than diesel fuel about 4.1%, 7%, 8.8%, and 2%, respectively (Fig. 8). The density of biodiesel was higher than that of diesel fuel, which means the same fuel consumption on volume basis results in higher specific fuel consumption in case of biodiesel.
The values of viscosity, density and flash point of poultry fat oil biodiesel were found to be closely matched with ASTM D-6751 standard specifications. Viscosity and density of biodiesel were found more than those for diesel. The calorific value of biodiesel was found to be lower than that of diesel. Poultry fat oil biodiesel cannot be used as a neat diesel fuel in cold weather conditions due to its relatively low cloud point. Preheating and lowering freezing point is required to eliminate this problem. The engine performance with poultry fat oil biodiesel and its blends are comparable with those of pure diesel fuel. Results indicated that B20 blend had the best performance and the lowest specific fuel consumption.

Harvesting of Kiwifruit (Actinidiadeliciosa, family: Actinidiaceae) is usually performed in mid-October in Iran. The average weight of this fruit is about 70 g. Hayward is the most popular kiwifruit variety in the world mainly due to its large size, oval shape and high shelf life. Drying fresh products is a long-standing method for conservation of food products. This method reduces water-borne and microbiological activities in fresh products while only minor physical and chemical changes occur in these products. Drying, therefore, is regarded as a common method used for food product conversation. There have been several researches on modeling drying of food products. Wang et al. (2007) worked on a mathematical modeling for drying apple slices in a hot air drying process and determining the effective thermal diffusivity. These researchers stated that Midili model was found to be the best for predicting the moisture content changes during drying. Torgul (2005) confirmed this finding in modeling the drying of apple slices in an infrared drying system. How ever not much research has been carned out on drying kiwifruit slices. Therefore, in this research, the drying process of kiwi slices in a vacuum dryer was examined in order to understand their behavior during the process and to determine a best predictive model for drying and also study the diffusivity coefficient for this product.

In thes research Hayward variety of kiwifruit for was used Sinco this variety is commonly grown in Iran. The fruits were purchased from local market in mid-October and transferred to a cool storage (50 C) in a lab at the Department of Biosystems Engineering at the Ferdowsi University of Mashhad. The samples used in this study were of medium size and suitable for cutting in a cylinder-shape cutter. The initial moisture content was determined by so-called oven-drying method on wet basis according to the following equation (Mohesnin, 1986):〖%MC〗_wb=(Initial weight-Final weight (after drying in oven))/(Initial weight) 100 (1) The moisture content was determined as 80.23% on a wet basis. The kiwifruits were sliced at 3 mm thickness using a 35 mm-diameter cylinder and weighed with a digital scale. The slices were moved out of the dryer and weighed every 30 min to monitor their moisture content. Weighing continued until the sample’s moisture content reached to 15-20% on a wet basis. Moisture ratio of kiwifruit slices during drying process was determined according to the follow equation:……………… ……….(2) where MR is moisture ratio (dimensionless), Mt is moisture content at any desirable time, Me equilibrium moisture content, percent, dry basis, and M0 is the initial moisture content (kg H2O/kg of dry matter). The value of Me is very small compared with Mt and M0, hence, the error involved in the simplification of above equation by omitting Me is negligible. The experimental drying data were fitted in various drying models commonly used for monitoring the trend of being-dried products. A few of which models are as follows: MR=exp (-kt): Newton model MR = exp (_ktn): Page model MR = 1 + a.t + bt2: Wang and Singh model MR = a.exp (_ktn) + b.t: Midilli model In this research, two statistical proameters were used to evaluate the goodness of fit of the tested models to the experimental data: the coefficient of determination (R2) and root mean square error (RMSE) between the experimental and the predicted moisture ratio values. Diffusivity coefficient for each slice was determined from the following equation: MR=8/π^2 ∑_(n=0)^∞▒〖1/(2n+1)^2 exp⁡[-(π^2 (2n+1)^2)/4 (D_eff t)/a^2] 〗 (3) where a is sample thickness (in meter), t drying time (in seconds), n is the number of observations and Deff is effective diffusivity coefficient (in m2.s-1). In long drying process, the following simplified equation is used: MR=8/π^2 exp⁡[-(〖π^2 D〗_eff t)/(4a^2)] (4) The diffusivity coefficient is the slope of the straight line when experimental drying data in terms of Ln (MR) is plotted versus drying time (t).

The results of this research revealed that the best prediction curve of moisture content against time was drawn using of MTLAB software. In this regards the rational function with first degree in both numerator and denominator and the third degree polynomial function with maximum coefficient of determination (R2) of 0.9991 and 0.9977 and minimum root mean square error (RMSE) of 0.01267 and 0.02412 were the best prediction models, respectively (Table 2). Furthermore, the drying time becomes shorter as the thickness of kiwifruit slices becomes thinner. This is mainly due to the higher thermal gradient within the thinner slices and hence faster moisture removal due evaporation. The heat diffusivity coefficient was also determined from “Ln (MR) – Time” curves (Figure 3). It was observed that with increase of fruit’s thickness, the heat diffusivity coefficient increases. This phenomenon may be related to the molecular dynamics and the surface tension of materials being dried. In other words the minimum and maximum values of the diffusivity coefficient were observed as 2.0904E-6 and 7.1303E-6 m2.s-1 for fruit thicknesses of 3 and 9 mm, respectively (table 3).

The trend of moisture content evolution against drying time during vacuum drying of kiwifruit was investigated using MTLAB software. Different prediction models were examined for the prediction of moisture removal during vacuum drying of kiwifruit. The rational and polynomial functions were determined as the most accurate prediction models with the coefficient of determination (R2) of higher than 0.99 and RMSE of about 0.02. Furthermore, the heat diffusivity coefficient of kiwifruit slices was investigated as a function of slice thickness. A general increasing trend observed for this coefficient as the thickness of the slices increased.