The demand for alternative fuel replaces the pure utilization of diesel fuel in the engines. Nano additives and the biodiesel are introduced as the additives for reducing the emission characteristics and fulfil the energy requirements. The main objective of this research is to determine the emission characteristics of metal additives and cottonseed added diesel. Three combinations of cobalt oxide contributeto the fuel blend preparation, namely, cobalt oxide + cottonseed oil, Cobalt oxide + silicon oxide and separate cobalt oxide in the diesel fuel. Such combinations are mixed in the diesel fuel by adopting the method of ultrasonic dispersed technique with three different proportions such as 25 ppm, 50 ppm and 100 ppm of Nano additives and B20, B40 and B60 blends of cottonseed oil. The strategy of Artificial Neural Network-Support Vector Machine (ANN-SVM) is applied to optimize and predict the better-reduced emission characteristics of CO, HC and NO_x through the mat lab platform. The testing 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 cobalt oxide fuel blend combinations with minimum emission outflow. The combination of both additives such as silicon oxide and the cobalt oxide proceeds better emission characteristics from both the predicted and the experimented outcomes. In which, the predicted outcomes are 64.1% of NO_x, 0.018% of CO and 1.610% of HC.

Keywords: : Cobalt oxide, Cottonseed oil, Silicon oxide, Additives, ANN-SVM, Brake thermal efficiency