Artificial neural network modeling of performance, emission, and vibration of a CI engine using alumina nano-catalyst added to diesel-biodiesel blends

Seyyed Hassan Hosseini, Ahmad Taghizadeh-Alisaraei, Barat Ghobadian and Ahmad Abbaszadeh-Mayvan

Renewable Energy, 2020, vol. 149, issue C, 951-961

Abstract: In recent years, added nano-catalysts to fuels has improved their thermo-physical properties. In present study, the alumina as additive with concentrations of 30, 60, and 90 ppm were added to B5 and B10 blends for evaluation of the engine performance, emissions, and vibration levels. An ANN model based on standard back-propagation learning algorithm for the engine was developed. Multi-layer perception network (MLP) was used for a non-linear mapping between the input and target parameters. The input or independent parameters were fuel blend, engine speed, fuel density, fuel viscosity, LHV, intake manifold pressure, fuel consumption, exhaust gas temperature, oxygen contained in exhaust gases, oil temperature, relative humidity, and ambient air pressure. Whereas, the target parameters separately were engine power, torque, emissions of CO, CO2, UHC, NO, RMS and Kurtosis of engine’s vibration. The results for optimum ANN model showed, the training algorithm of back-propagation with 25-25 neurons in hidden layers (logsig-logsig) is able to predict different parameters of engine for different conditions. The corresponding R-values for training, validation and testing were 0.9999, 0.9994 and 0.9995, respectively. The performance and accuracy of the proposed ANN model was completely satisfactory.

Keywords: Alumina; Biodiesel; Diesel engines; Performance; Vibration; ANN (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096014811931571X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:149:y:2020:i:c:p:951-961

DOI: 10.1016/j.renene.2019.10.080

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().