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THE PREDICTION AND OPTIMIZATION OF ALTERNATIVE FUEL CONSUMPTION IN VEHICLES USING ARTIFICIAL NEURAL NETWORKS WITH THE AIM OF REDUCING ATMOSPHERIC POLLUTION
Abstract
The purpose of this work is to explore the potential for predicting and improving the fuel efficiency of vehicles with thermal engines, especially in the context of reducing exhaust gas emissions that contribute to environmental pollution. With rising fuel costs, consumers need to make informed decisions about the fuel consumption and environmental impact of purchasing vehicles with internal combustion engines. To achieve this goal, the researchers utilized artificial intelligence and created a model using neural networks, drawing on data from vehicle manufacturers that incorporated various factors such as engine power, maximum torque, vehicle weight, engine cylinder volume, and the number of cylinders. The aim of this approach was to optimize fuel consumption for such vehicles. The study involved predicting and optimizing fuel consumption through the selection and fine-tuning of neural network architecture based on the data being analyzed. This included establishing the connections between input and output data, training the network to minimize errors, and validating the model to ensure high generalization. Various neural networks with 1, 2, or 3 layers were examined, taking into account the number of learning cycles, the sensitivity of input data, and the overall importance of each factor in order to minimize prediction errors. The findings of the study indicate that the construction parameters of vehicles significantly impact both fuel consumption and environmental pollution. The application of this fuel consumption prediction and optimization method could lead to the increased use of thermal engine vehicles that are chosen based on practical utility and transportation needs, while also helping to reduce pollution.
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References5
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