A Review on Artificial Neural Network and Alternative Fuels for Internal Combustion Engines
Main Article Content
Internal combustion engines are extensively used in energy generation and all transportation methods for people and goods. So, the demand of fuels used in IC engines has largely increased. The need of liquid fuels reaches to 3000 million tons oil equivalent per year and they are the cause of 10% of the world’s greenhouse emissions . Our need for transport energy (IC engines) increases rapidly for both SI (passenger cars) and CI (commercial road transport and marine sectors) engines. This increase is predicted to reach about 40% in 2040 depending on 90% petroleum based liquid fuels, 5% natural gas. Now, the main development in petrol engines is to reach maximum performance while in diesel engines is to reduce soot and NOx emissions without affecting efficiency . Internal combustion engines can be classified to spark ignition engines that use gasoline fuel where fuel-air are mixed before ignition and then combustion occurs, and compression ignition engines that uses diesel as a fuel that is compressed and injected so it reaches to self-ignition temperature. But at these days, there are many alternative fuels used instead of gasoline and diesel fuels such as ethanol, natural gas, liquefied petroleum gas, coal-derived liquid fuels, hydrogen, biodiesel, fuels other than alcohol, derived from biological materials, and non-petroleum fuels. Global transportation need of fuels is as in Figure 1. This indicates that biofuels reach 3% of the total fuel consumed in transportation sector . These alternative sources reduce emissions and Sulphur contents . Fuels of internal combustion engines in the general can be classified as shown in Figure 2. that indicates all types of conventional and alternative fuels . In the last years, internal combustion engines have been controlled by artificial neural network as this method has various advantages, they can save time and money. This method is used to give us an indication about performance, combustion, and emissions. The purpose of the study is to look into the network topologies used to design the model, followed by a statistical analysis of the created ANN models. Also provided is a comparison of the ANN model and other prediction models .
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