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In this study, biodiesel fuel with a ratio of 20% volume (B20) was used on vehicles that are used in common rail injection systems, complying with Euro2 emission regulations. Laboratory and road tests were conducted to evaluate the effects of B20 on performance, emissions and engine components. Using diesel fuel and B20 as reference fuels, tests were conducted using Euro2 vehicle technology to investigate the effects on emissions, fuel consumption, and power. Durability testing was run for travel distances covering 40,000 km under various road and environmental conditions, while vehicle performance and emissions tests were conducted using the ECE R84-03 and ECE R101 test methods, respectively. The results show that B20 has lower CO and hydrocarbon (HC) emissions for every distance travelled, with an average of around 30%. Particulate emission was a bit lower, averaging 3.4% for B20 compared to B0, while NOx was found to slightly increase at around 2% for B20. Due to its lower calorific value, for an average distance traveled, the fuel economy of B20 was around 0.5% higher compared to B0. Furthermore, the maximum power of B20 was 3% lower compared to that of B0 for the entire distance traveled. However, an evaluation of engine components after 40,000 km showed that B20 and B0 were similar. Moreover, vehicles using B20 tend to have a comparable durability of engine components when compared with B0.

The privatization of electricity industry in various parts of the world has increased the significance of the load prediction problem and in particular there is a need to understand and predict the demand for power with greater accuracy, even in case of imprecise input data. Prediction of power demand is essential for an efficient operation of any utility company. In this paper, a fuzzy version of neural network, namely Fuzzy back propagation network (Fuzzy BP) has been developed for short term electric load prediction. The load is predicted using fuzzy back propagation algorithm. This model is capable of handling imprecise information in input data. The proposed architecture consists of a module with 51 inputs and 24 outputs. The inputs are fuzzified and the outputs are crisp values representing the predicted load. The proposed method is implemented in MATLAB. The simulation results are presented for each day (24 hours) of the week. Besides this, a multi layer perceptron (MLP) was also implemented separately and the load was predicted using back propagation algorithm. The results obtained from Fuzzy BP were found to be satisfactory when compared to those of MLP network.

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