• KIRAN KUMAR
• MATH M.C

Recent trends depict depletions of petroleum fuels and this situation of energy crisis needs exigent attention towards innovative alternative fuels which may be engendered from biomass such as alcohol. Experiments were conducted to examine the gasoline engine at different operating conditions the two engines were utilized for investigation of the performance. Investigation was conducted experiments to examine the operating variables at different speeds, fuel quantities in the range of 5%, 10%, 15%, 20%, 25%, 30% and 35%. Statistical techniques like Replication Surface Methodology (RSM) and Artificial Neural Network (ANN) used to optimize the performance parameters of a four-stroke SI engine which operates with ethanol coalesced gasoline as fuel. The main objective was to optimize the engine performance for minimum BSFC. Tests were also conducted by various researchers on a variable compression ratio engine test rig with different coalescences of Compression Ratio (CR), Blend Ratio (BR) and load. The experimentation was designed with statistical implements withal kenned as the design of experiments (DOE) predicated on RSM and ANN.

DOE, RSM, ANN, Gasoline, Spark Ignition Engine, Methanol, Ethanol.

"Review studies on the Prediction of Engine Performance and Exhaust Emissions with Statistical Parametric Optimization Techniques of Ethanol-Gasoline Blended Fuel.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.840-848, June 2019, Available :http://www.jetir.org/papers/JETIR1907720.pdf

"Review studies on the Prediction of Engine Performance and Exhaust Emissions with Statistical Parametric Optimization Techniques of Ethanol-Gasoline Blended Fuel.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp840-848, June 2019, Available at : http://www.jetir.org/papers/JETIR1907720.pdf