Opportunity to Improve Diesel-Fuel Cetane-Number Prediction from Easily Available Physical Properties and Application of the Least-Squares Method and Artificial Neural Networks

2015 ◽  
Vol 29 (3) ◽  
pp. 1520-1533 ◽  
Author(s):  
Dicho Stratiev ◽  
Ivaylo Marinov ◽  
Rosen Dinkov ◽  
Ivelina Shishkova ◽  
Ilian Velkov ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Physical Properties ◽  
Least Squares ◽  
Diesel Fuel ◽  
Least Squares Method ◽  
Cetane Number ◽  
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.

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.

scholarly journals Constraints of Biological Neural Networks and Their Consideration in AI Applications

2010 ◽  
Vol 2010 ◽  
pp. 1-6 ◽  
Author(s):  
Richard Stafford
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Physical Properties ◽  
Ecological Niche ◽  
Neural Systems ◽  
Ecological Constraints ◽  
Process Information ◽  
Spiking Networks ◽  
Artificial Neural ◽  
Biological Organisms

Biological organisms do not evolve to perfection, but to out compete others in their ecological niche, and therefore survive and reproduce. This paper reviews the constraints imposed on imperfect organisms, particularly on their neural systems and ability to capture and process information accurately. By understanding biological constraints of the physical properties of neurons, simpler and more efficient artificial neural networks can be made (e.g., spiking networks will transmit less information than graded potential networks, spikes only occur in nature due to limitations of carrying electrical charges over large distances). Furthermore, understanding the behavioural and ecological constraints on animals allows an understanding of the limitations of bio-inspired solutions, but also an understanding of why bio-inspired solutions may fail and how to correct these failures.

Sign in / Sign up

Export Citation Format

Share Document