Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups
| Autor: | Firas Hashim Kamar, Younis M. Younis, Gheorghe Nechifor, Farqad T. Najim, Salman H. Abbas |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
chemistry.chemical_classification
Artificial neural network Process equipment business.industry Materials Science (miscellaneous) Process Chemistry and Technology General Engineering General Chemistry General Medicine General Biochemistry Genetics and Molecular Biology Diesel fuel Hydrocarbon chemistry Petrochemistry Linear regression Materials Chemistry Heat of combustion General Pharmacology Toxicology and Pharmaceutics Process engineering business |
| Zdroj: | Revista de Chimie. 71:66-74 |
| ISSN: | 2668-8212 0034-7752 |
| DOI: | 10.37358/rc.20.6.8171 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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