Experimental investigation with optimal prediction of emission in diesel engine using combined NSR–SCR catalyzer

The utilization of catalytic converters is one of the well-known strategies to clean the exhaust. The catalytic converters oxidize the destructive carbon monoxide and hydrocarbon emissions into harmless CO₂ and water vapor. In this experiment, the essential objective is to develop a catalytic converter with combined methods like selective catalytic reduction, NO_x storage and reduction (SCR-NSR) procedure and test its effectiveness in the NO_x reduction from the exhaust of diesel engine. The combination of the SCR-NSR catalytic systems is proposed in this experimentation for building up an effective catalytic converter and distinguishing the optimal emission processes. The fabricated SCR-NSR catalytic converter is fitted to the exhaust system and the emission rates are estimated with the help of a gas analyzer. The testing is done by utilizing diesel as fuel in the Kirloskar TV1 engine with NSR, non-filter, and combined NSR–SCR system. The investigation is experienced for 20 trials with different emission parameters analyzed. For improving the discharge level in the engine, the test data are predicted with the proposed numerical model and tested in Mat Lab software. The invasive weed optimization and particle swarm optimization together with a recurrent neural network are utilized for the prediction and optimization process and parameters like compression ratio, input power, and load are utilized as input to the experimentation. In the 20 trials, a low level of discharge is attained in the 17th trial. Subsequently, the prediction goes for the 20 sets individually and compared with the 17th set and the experimental results demonstrate that the discharge rates acquired for consolidated NSR–SCR were observed to be 0.01% of CO, 3% of HC, 0.1% of CO₂, 20.74% of O₂, and 68 ppm of NO_x. The point when compared to predicted outcomes demonstrates the ideal level of 0.001% of CO, 0.5% of HC, 0% of CO₂, 21.76% of O₂, and 60.6 ppm of NO_x, respectively. The proposed invasive weed optimization and particle swarm optimization obviously reduce the emission rates of diesel engines more than the other comparative methods.