Effect of poultry fat oil biodiesel on tractor engine performance

Introduction: 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). Materials and Methods: 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. Results and Discussion: 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. Conclusions: 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

Prioritization and Evaluation of Mechanical Components Failure of CNC Lathe Machine based on Fuzzy FMEA Approach

Introduction 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. Materials and Methods      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. Results and Discussion 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. Conclusions 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