Artificial intelligence based emission and performance prediction, and optimization of HHO-blended gasoline SI engine: A sustainable transition

Abstract

In striving for sustainable alternatives to gasoline, Oxyhydrogen (HHO) has emerged as a promising substitute for Internal Combustion Engines (ICEs). HHO blends not only improve engine efficiency but also reduce harmful emissions. On-site, HHO utilization in the engine, eradicates low energy density and storage challenges. The current study combined cutting-edge machine learning (ML) techniques like Artificial Neural Network (ANN) and Gradient-based optimization to effectively utilize HHO with gasoline. Experimentation involved a single-cylinder spark ignition (SI) engine fueled by varying HHO-gasoline blends across different loads and speeds. Iterative tuning of the loss function led to the identification of the optimal architecture, denoted as 2HL-10N (2 hidden layers with 10 neurons each), with impressive correlation coefficients (0.99481 for training, 0.9781 for validation, 0.96914 for testing, and overall, 0.98819). Subsequently, ANN led Gradient-based optimization unveiled key performance metrics along with emissions. Upon implementing optimized conditions (HHO: 3.78 l/m, load: 100 %, and 3465 rpm), notable enhancements were observed. The torque and efficiency increased by 11.8 %, and 7.1 %, respectively. Furthermore, brake-specific fuel consumption, carbon monoxide, and hydrocarbon emissions showed a reduction of 11.5 %, 27.1 %, and 36.6 %, respectively. ANN based optimal engine operation revealed HHO as a potential replacement for conventional gasoline.

Keywords

• Artificial neural network (ANN)
• Hydrogen blends
• Internal combustion (IC) engines
• Emission reduction
• Engine optimization