Predicting the Cetane Number, Yield Sooting Index, Kinematic Viscosity, and Cloud Point for Catalytically Upgraded Pyrolysis Oil Using Artificial Neural Networks

2021 ◽  
Author(s):  
Travis Kessler ◽  
Thomas Schwartz ◽  
Hsi-Wu Wong ◽  
John Mack
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cloud Point ◽  
Kinematic Viscosity ◽  
Cetane Number ◽  
Pyrolysis Oil ◽  
Artificial Neural

Predicting the Cetane Number, Yield Sooting Index, Kinematic Viscosity, and Cloud Point for Catalytically Upgraded Pyrolysis Oil Using Artificial Neural Networks

2020 ◽  
Author(s):  
Travis Kessler ◽  
Thomas Schwartz ◽  
Hsi-Wu Wong ◽  
J. Hunter Mack
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cloud Point ◽  
Diesel Fuel ◽  
Kinematic Viscosity ◽  
Fast Pyrolysis ◽  
Cetane Number ◽  
Alternative Methods ◽  
Pyrolysis Oil ◽  
Artificial Neural

Abstract The conversion of biomass using fast pyrolysis has the potential to be significantly less expensive at scale compared to alternative methods such as fermentation and gasification. Selective upgrading of the products of fast pyrolysis through chemical catalysis produces compounds with lower oxygen content and lower acidity; however, identifying the specific catalytic pathways for producing viable fuels and fuel additives often requires a trial-and-error approach. Specifically, key properties of the compounds must be experimentally tested to evaluate the viability of the resultant compounds. The present work proposes predictive models constructed with artificial neural networks (ANNs) for cetane number (CN), yield sooting index (YSI), kinematic viscosity (KV), and cloud point (CP), with blind test set median absolute errors of 5.14 cetane units, 3.36 yield sooting index units, 0.07 millimeters squared per second, and 4.89 degrees Celsius, respectively. Furthermore, the cetane number, yield sooting index, kinematic viscosity, and cloud point were predicted for over three hundred expected products from the catalytic upgrading of pyrolysis oil. It was discovered that 130 of these compounds have predicted cetane numbers greater than 40, with four of these compounds possessing predicted yield sooting index values significantly less than that of diesel fuel and predicted viscosities and cloud points comparable to that of diesel fuel.

Evaluating Diesel/Biofuel Blends Using Artificial Neural Networks and Linear/Nonlinear Equations

2021 ◽  
Author(s):  
Travis Kessler ◽  
Thomas Schwartz ◽  
Hsi-Wu Wong ◽  
J. Hunter Mack
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kinematic Viscosity ◽  
Linear Equations ◽  
Cetane Number ◽  
Heating Value ◽  
Lower Heating Value ◽  
Artificial Neural ◽  
Non Linear Equations ◽  
Lower Heating

Abstract The use of biomass-derived additives in diesel fuel mixtures has the potential to increase the fuel’s efficiency, decrease the formation of particulate matter during its combustion, and retain the fuel’s behavior in cold weather. To this end, identifying compounds that enable these behaviors is paramount. The present work utilizes a series of linear and non-linear equations in series with artificial neural networks to predict the cetane number, yield sooting index, kinematic viscosity, cloud point, and lower heating value of multi-component blends. Property values of pure components are predicted using artificial neural networks trained with existing experimental data, and these predictions and their expected errors are propagated through linear and non-linear equations to obtain property predictions for multi-component blends. Individual component property prediction errors, defined by blind prediction median absolute error, are 4.91 units, 7.84 units, 0.06 cSt, 4.00 °C, and 0.55 MJ/kg for cetane number, yield sooting index, kinematic viscosity, cloud point, and lower heating value respectively. On average, property predictions for blends are shown to be accurate to within 6% of the blends’ experimental values. Further, a multitude of compounds expected to be produced from catalytically upgrading products of fast pyrolysis are evaluated with respect to their behavior in diesel fuel blends.

scholarly journals THE INTELLIGENT SYSTEM FOR AUTOMOTIVE FUELS QUALITY DEFINITION

2013 ◽  
Vol 3 (3) ◽  
pp. 11-13
Author(s):  
Volodymyr Drevetskiy ◽  
Marko Klepach
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diesel Fuel ◽  
Octane Number ◽  
Intelligent System ◽  
Cetane Number ◽  
Cetane Index ◽  
Hydrodynamic Method ◽  
Artificial Neural ◽  
Automotive Fuels

An intelligent system, based on hydrodynamic method and artificial neural networks usage for automotive fuels quality definition have been developed. Artificial neural networks optimal structures for the octane number of gasoline, cetane number, cetane index of diesel fuel definition have been substantiated and their accuracy has been analyzed. The implementation of artificial neural networks by means of microcontroller-based systems has been considered.

Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression

2013 ◽  
Vol 65 ◽  
pp. 255-261 ◽  
Author(s):  
Ramón Piloto-Rodríguez ◽  
Yisel Sánchez-Borroto ◽  
Magin Lapuerta ◽  
Leonardo Goyos-Pérez ◽  
Sebastian Verhelst
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Cetane Number ◽  
Artificial Neural

scholarly journals Prediction of Cetane Number and Ignition Delay of Biodiesel Using Artificial Neural Networks

2014 ◽  
Vol 57 ◽  
pp. 877-885 ◽  
Author(s):  
Y. Sánchez-Borroto ◽  
R. Piloto-Rodriguez ◽  
M. Errasti ◽  
R. Sierens ◽  
S. Verhelst
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ignition Delay ◽  
Cetane Number ◽  
Artificial Neural

Artificial neural networks used for the prediction of the cetane number of biodiesel

2006 ◽  
Vol 31 (15) ◽  
pp. 2524-2533 ◽  
Author(s):  
A.S. Ramadhas ◽  
S. Jayaraj ◽  
C. Muraleedharan ◽  
K. Padmakumari
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cetane Number ◽  
Artificial Neural
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