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Diesel Powered engines are predominately used in transport and power generators. Due to rapid depletion of fossils fuels like diesel, there is need to find alternate resources for as a fuel for transport and power sector. The use of third generation fuels like algae seems to be a promising solution to satisfy our energy demands in future. In these work algae is used as an alternative fuel with different blend combination with diesel and the results obtained are summarized. Algae fuel is blended with diesel on a volume basis, namely A10, A20, A30, A40 and A100 and made to run in diesel engines. The properties of the fuels were found according to American Society for Testing and Materials standards. Experimental testing was carried out in a single cylinder direct injection diesel engine. Among the various blends tested A20 shows closer estimation with diesel results in higher thermal efficiency lower HC, CO, Smoke and particulate matter emissions. However, the other emissions like Nitrous oxide and carbon dioxide were shown marginally higher. Regarding cylinder pressure and heat release rate curves, the peak value obtained is considerably lower at higher load condition while compared with diesel.

Car hydraulic brakes are important safety components for passengers and are thus the good condition of brakes are essential for braking. By using the vibrational signatures, the state of the brake components can be determined. In this proposed study, electronic condition monitoring is suggested as a possible solution to such issues by using a machine learning method with a piezo-electric transducer and a dynamic data acquisition system. Ford EcoSport setup was used to acquire the vibration signals for both good and bad braking conditions. The mathematical Descriptive statistical features from the vibration signals were obtained and the feature selection has been done with the C4.5 decision tree classifier. The appropriate number of features needed to classify a particular problem is not determined by a specific method. A thorough study is, therefore, necessary to find the right number of features. The fault analysis of the Ford EcoSport hydraulic braking system has been established through the use of the C4.5 decision tree classifier and logistic model tree (LMT) classifier.