Artificial intelligence based emission and performance prediction, and optimization of HHO-blended gasoline SI engine: A sustainable transition
| dc.contributor.author | Bashir, Muhammad Nasir | |
| dc.contributor.author | Riaz, Fahid | |
| dc.contributor.author | Usman, Muhammad | |
| dc.contributor.author | ETAL.. | |
| dc.date.accessioned | 2025-09-03T06:37:27Z | |
| dc.date.available | 2025-09-03T06:37:27Z | |
| dc.date.issued | 2024 | |
| dc.description | Modern transportation heavily relies on ICEs. Over 99.9 % of global vehicles, including 1.4 billion cars and 380 million commercial vehicles, utilize ICEs, with further growth anticipated [1]. ICEs utilizing fossil fuels account for 25 % of the world's power and, as a consequence, generate about 10 % of the world's greenhouse gas (GHG) emissions [2]. Industrial expansion and rapid population growth accelerate the use of conventional fuels, threatening the environment, energy security, and economic stability [3,4]. The shift to new technologies is promising, but the intricate shift requires consideration of infrastructure, economy, and society's complexities. | |
| dc.description.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 | |
| dc.identifier.citation | Bashir, M. N., Usman, M., Riaz, F., Ahmad, T., Fouad, Y., Basha, M. S., ... & Lee, J. S. (2024). Artificial intelligence based emission and performance prediction, and optimization of HHO-blended gasoline SI engine: A sustainable transition. Case Studies in Thermal Engineering, 64, 105562. | |
| dc.identifier.doi | https://doi.org/10.1016/j.csite.2024.105562 | |
| dc.identifier.uri | https://repository.adu.ac.ae/handle/1/7354 | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.title | Artificial intelligence based emission and performance prediction, and optimization of HHO-blended gasoline SI engine: A sustainable transition | |
| dc.type | Article |