Abstract
A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.
Highlights
Diesel fuel technology has greatly improved over the last decade, with high-pressure direct injection being commonly used, with the addition of a new process for supercharging internal combustion engines to increasing power [1,2]
A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel
The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value
Summary
Diesel fuel technology has greatly improved over the last decade, with high-pressure direct injection being commonly used, with the addition of a new process for supercharging internal combustion engines to increasing power [1,2]. Combustion is a major source of power generation, significantly so in automotive applications, so operating systems that reduce fuel consumption and high-efficiency direct-injection diesel engines are used [4-6]. Diesel is one of numerous petroleum products that is used as a fuel in all types of compressionignition engines [7] [8] It is produced from crude oil, extracted from oil wells. Its carbon numbers are between 11 and 22 [9-11], and such compounds can be classified as paraffin, naphthene or aromatics These families can play a significant role in the chemical and physical properties of diesel, with different proportions of these being one of the factors that distinguishes diesel fuel from other diesel compounds, and affects the properties of diesel fuel performance and combustion [12].
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