Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis

• Published in: Kemija u industriji; časopis kemičara i tehnologa Jugoslavije Vol. 69; no. 7-8; pp. 355 - 364
• Main Authors: Hamadache, Mabrouk, Si-Moussa, Cherif, Laidi, Maamar, Hanini, Salah, Belmadani, Souad
• Format: Journal Article
• Language: English
• Published: Croatian Society of Chemical Engineers 01.06.2020
• Subjects: biofuels; density; kinematic viscosity; modelling; neural network
• ISSN: 0022-9830; 1334-9090
• DOI: 10.15255/KUI.2019.053

In the present article, two models based on the artificial neural network methodology (ANN) have been optimised to predict the density ( ρ ) and kinematic viscosity ( μ ) of different systems of biofuels and their blends with diesel fuel. An experimental database of 1025 points, including 34 systems (15 pure systems, 14 binary systems, and 5 ternary systems) was used for the development of these models. These models use six inputs, which are temperature ( T ) in the range of −10 – 200 °C, volume fractions ( X 1, X 2, X 3) in the range of 0–1, and to distinguish these systems, we used kinematic viscosity at 20 °C in the range of 0.67–74.19 mm2 s–1 and density at 20 °C in the range of 0.7560–0.9188 g cm–3. The best results were obtained with the architecture of {6-26-2: 6 neurons in the input layer – 26 neurons in the hidden layer – 2 neurons in the output layer}. Results of comparison between experimental and simulated values in terms of the correlation coefficients were: R 2 = 0.9965 for density, and R 2 = 0.9938 for kinematic viscosity. A 238 new database experimental of 4 systems (2 pure systems, 1 binary system, and 1 ternary system) was used to check the accuracy of the two ANN models previously developed. Results of prediction performances in terms of the correlation coefficients were: R 2 = 0.9980 for density, and R 2 = 0.9653 for kinematic viscosity. Comparison of validation results with those of the other studies shows that the neural network models gave far better results. U ovom članku dva modela zasnovana na metodologiji umjetne neuronske mreže (ANN) optimizirana su za predviđanje gustoće ( ρ ) i kinematičke viskoznosti ( μ ) različitih sustava biogoriva i njihovih mješavina s dizelskim gorivom. Za razvoj tih modela upotrijebljena je eksperimentalna baza podataka od 1025 točaka, uključujući 34 sustava (15 čistih sustava, 14 binarnih sustava i 5 ternarnih sustava). Ti modeli koriste šest ulaza: temperatura ( T ) u rasponu od −10 do 200 °C, volumni udjeli ( X 1, X 2, X 3) u rasponu 0 – 1, a za razlikovanje tih sustava korištena je kinematička viskoznost pri 20 °C u rasponu 0,67 – 74,19 mm2 s–1 i gustoća pri 20 °C u rasponu 0,7560 – 0,9188 g cm–3. Najbolji rezultati dobiveni su arhitekturom {6-26-2: 6 neurona u ulaznom sloju – 26 neurona u skrivenom sloju – 2 neurona u izlaznom sloju}. Rezultati usporedbe eksperimentalnih i simuliranih vrijednosti u smislu korelacijskih koeficijenata bili su: R 2 = 0,9965 za gustoću i R 2 = 0,9938 za kinematičku viskoznost. Za provjeru točnosti dva prethodno razvijena modela ANN upotrijebljeno je 238 novih eksperimentalnih baza podataka s 4 sustava (2 čista sustava, 1 binarni sustav i 1 ternarni sustav). Rezultati performansi predviđanja s obzirom na korelacijske koeficijente bili su: R 2 = 0,9980 za gustoću i R 2 = 0,9653 za kinematičku viskoznost. Usporedba rezultata validacije s rezultatima drugih studija pokazuje da su modeli neuronske mreže dali znatno bolje rezultate.