Experimental investigations of Co3O4, SiO2, cotton seed oil additive blends in the diesel engine and optimization by ANN-SVM process
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The demand for alternative fuel replaces the pure utilization of diesel fuel in the engines. Nano additives and the biodiesel areintroduced as the additives for reducing the emission characteristics and fulfil the energy requirements. The main objectiveof this research is to determine the emission characteristics of metal additives and cottonseed added diesel. Three combinationsof cobalt oxide contributeto the fuel blend preparation, namely, cobalt oxide + cottonseed oil, Cobalt oxide + silicon oxide andseparate cobalt oxide in the diesel fuel. Such combinations are mixed in the diesel fuel by adopting the method of ultrasonicdispersed technique with three different proportions such as 25 ppm, 50 ppm and 100 ppm of Nano additives and B20, B40and B60 blends of cottonseed oil. The strategy of Artificial Neural Network-Support Vector Machine (ANN-SVM) is appliedto optimize and predict the better-reduced emission characteristics of CO, HC and NOx through the mat lab platform. Thetesting is carried out with five different load conditions such as 0, 25, 50, 75 and 100 N and a constant compression ratio 17.5. Terminally, the comparison between predicted and experimented values are also presented for determining the better cobaltoxide fuel blend combinations with minimum emission outflow. The combination of both additives such as silicon oxide andthe cobalt oxide proceeds better emission characteristics from both the predicted and the experimented outcomes. In which,the predicted outcomes are 64.1% of NOx, 0.018% of CO and 1.610% of HC.
Abstract
The demand for alternative fuel replaces the pure utilization of diesel fuel in the engines. Nano additives and the biodiesel areintroduced as the additives for reducing the emission characteristics and fulfil the energy requirements. The main objectiveof this research is to determine the emission characteristics of metal additives and cottonseed added diesel. Three combinationsof cobalt oxide contributeto the fuel blend preparation, namely, cobalt oxide + cottonseed oil, Cobalt oxide + silicon oxide andseparate cobalt oxide in the diesel fuel. Such combinations are mixed in the diesel fuel by adopting the method of ultrasonicdispersed technique with three different proportions such as 25 ppm, 50 ppm and 100 ppm of Nano additives and B20, B40and B60 blends of cottonseed oil. The strategy of Artificial Neural Network-Support Vector Machine (ANN-SVM) is appliedto optimize and predict the better-reduced emission characteristics of CO, HC and NOx through the mat lab platform. Thetesting is carried out with five different load conditions such as 0, 25, 50, 75 and 100 N and a constant compression ratio 17.5. Terminally, the comparison between predicted and experimented values are also presented for determining the better cobaltoxide fuel blend combinations with minimum emission outflow. The combination of both additives such as silicon oxide andthe cobalt oxide proceeds better emission characteristics from both the predicted and the experimented outcomes. In which,the predicted outcomes are 64.1% of NOx, 0.018% of CO and 1.610% of HC.
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