Prediction and optimization of diesel engine characteristics for various fuel injection timing: Operated by third generation green fuel with alumina nano additive

•Azolla pinnata algae were utilized to produce green biodiesel.•ANN & RSM models were used for predicting the CI Engine characteristics.•The desirability approach was applied for optimizing the CI Engine characteristics.•The effects of Alumina nano additive dosed green biodiesel blend for various fuel injection timing were analysed. Azolla pinnata is a macroalgae commonly known as mosquito weed that is widely utilized for biomass production and biodiesel because of its capacity to thrive in low nitrogen environments. In this study, the various concentration (25 ppm, 50 ppm and 75 ppm) of alumina nanoparticles dosed Azolla pinnata biodiesel blend performance and emission parameters were analyzed on a single-cylinder compression ignition engine for different load conditions and fuel injection timings. Using the experimental results, the artificial neural network and response surface methodology regression models were developed and compared its prediction capabilities of output parameters as brake thermal efficiency, brake specific fuel consumption, hydrocarbon, carbon monoxide, nitrogen oxide and smoke. The identified best model was applied to optimize the input parameters of nano additive, engine load and fuel injection timing. The attained R2 (0.9992) and root mean square error 2.437 values exposed that the developed response surface methodology model was more accurate than artificial neural network. The best possible responses provided through the desirability function approach were brake thermal efficiency (19.05 %), brake specific fuel consumption (785.66 g/kWh), hydrocarbon (25.18 ppm), carbon monoxide (0.0576 %), nitrogen oxide (443.79 ppm), smoke (7.48 %) respectively for optimized working factors of nano additive (44 ppm), engine load (1.368 kW) and fuel injection timing (26° before top dead centre). The overall error percentage calculated through the validation study was observed to be under 5 %. The developed response surface methodology model produced good results, which are helpful in predicting and optimizing engine emissions and performance parameters.