Experimental Analysis And Parameter Optimization On The Reduction of NOx From Diesel Engine Using RSM And ANN Model

Abstract The feasibility of the Non-thermal Plasma (NTP) process is examined by four operating parameters including NOx concentration (300-400 ppm), gas flow rate (2-6 lpm), voltage (20-30 kV) and electrode gap (3-5 mm) using a Dielectric Barrier Discharge (DBD) reactor for removing NOx from diesel engine exhaust. Based on the NTP study, the NOx removal efficiency and energy efficiency of the NTP reactor are measured. Optimization of process parameters have been carried out using response surface-based Box Behnken Design (BBD) method and Artificial Neural Network (ANN) method. ANN based optimization is carried out using feed-forward network algorithm which has 4 input nodes, 10 hidden nodes and 2 output nodes. Based on the RSM and ANN model study, R2 value are obtained as 0.98 and 0.99 respectively. These models demonstrates that they have strong agreement with the experimental results. The results are indicated that the RSM model's optimum conditions resulted in a maximum NOx reduction of 60.5% and an energy efficiency of 66.24 g/J. The comparison between the two models confirmed the findings, whereas this ANN model displayed a stronger correlation to the experimental evidence.