Artificial intelligence based emission and performance prediction, and optimization of HHO-blended gasoline SI engine: A sustainable transition
Muhammad Nasir Bashir, Muhammad Usman, Fahid Riaz, Touqeer Ahmad, Yasser Fouad, M. Shameer Basha, Muhammad Mujtaba Abbas, Joon Sang Lee
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.