Multi-objective optimization of modified nanofluid fuel blends at different TiO2 nanoparticle concentration in diesel engine: Experimental assessment and modeling

Application of metal nanoparticles as combustion catalyst in diesel biodiesel fuel blends has grown recently. Efficient utilization of modified nanofluid fuels (MNF) is possible only when engine operating, fuel injection parameters are optimized accordingly. In the present research, experimental and statistical analysis is carried out on a commercial diesel engine (3.5 KW) with the aim to determine the optimal doping rate of nanoparticles and engine operating parameters using response surface methodology (RSM) and desirability function approach (DFA). The modified nanofluid (MNF) fuels used are blend of Acacia Concinna biodiesel (40% by vol.) and diesel (60% by vol.) mixed with titanium dioxide (TiO2) metal nanoparticles in different concentrations. Initially, the prepared fuel blends are characterized by SEM, TEM, blend stability (Uv-Vis spectrophotometery and sedimentation analysis) and various other properties. The optimal value, TiO2 doping rate of 150 mg/liter (MNF150), injection timing of 22.5 degrees CA btdc and 82.37% engine load is found to be the most suitable combination. Under these condition, brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), ignition delay (ID), hydrocarbon (HC), smoke emissions are improved by 3.25%, 18.42%,7%, 38%, 20% respectively with slightly higher NOx emissions in comparison to diesel. This is observed with an overall high desirability value of 0.707. The modeling of engine output responses are (assuming quadratic model order) found to be statistically fit at 95.0% C.I level with residuals to be normally distributed. Further, a close agreement between experimental and model predicted values of responses, prove the adequacy of developed models.