Machine Learning Approach for the Investigation of Metal Ion Concentration on Distillate Marine Diesel Fuels through Feed Forward Neural Networks

Author:

Savvides Ambrosios-Antonios12ORCID,Papadopoulos Leonidas2,Intzirtzis George3,Kalligeros Stamatios4ORCID

Affiliation:

1. Laboratory of Applied Mechanics, Department of Naval Science, Hellenic Naval Academy, 185 39 Piraeus, Greece

2. School of Civil Engineering, National Technical University of Athens, Zografou Campus, 9 Iroon Polytechniou str, 157 72 Athens, Greece

3. Piraeus Chemical Service, General Chemical State Laboratory, Independent Authority for Public Revenue, 185 45 Piraeus, Greece

4. Fuels and Lubricants Laboratory, Department of Naval Science, Hellenic Naval Academy, End of Hadjikyriakou Avenue, 185 39 Piraeus, Greece

Abstract

In this work, a set of Feed Forward Neural Networks (FNN) for the estimation of the metal ion concentration of diesel fuels is presented. The dataset vector is obtained through in situ measurements from distillate marine diesel fuel storage tanks all over Greece, in order to reduce the selection bias. It has been demonstrated that the most correlated ions among them are Aluminum (Al), Barium (Ba) and Calcium (Ca). Moreover, the FNN models are the most reliable models to be used for the model construction under discussion. The initial L2 error is relatively small, in the vicinity of 0.3. However, after removing a small dataset that includes 1–2 data points significantly deviating from the model trend, the error is substantially reduced to 0.05, ensuring the reliability and accuracy of the model. If this dataset is cleared, the estimated error is substantially reduced to 0.05, enhancing the reliability and accuracy of the model. The correlation between the sum of the concentrations of the model in relation with the Density and Viscosity are, respectively, 0.15 and 0.29 which are characterized as weak.

Publisher

MDPI AG

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