scholarly journals Characterizing Erythrophleum Suaveolens Charcoal as a Viable Alternative Fuel to Coke in Iron Melting In Nigeria

2013 ◽  
Vol 10 (3) ◽  
pp. 06-11
Author(s):  
Keyword(s):  
Alternative Fuel ◽  
Viable Alternative

E Diesel: A Viable Alternative Fuel

2004 ◽  
Author(s):  
Arun V Pantar ◽  
Keith C Corkwell
Keyword(s):  
Alternative Fuel ◽  
Viable Alternative

scholarly journals Physicochemical Properties of Biobutanol as an Advanced Biofuel

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 914
Author(s):  
Michal Obergruber ◽  
Vladimír Hönig ◽  
Petr Procházka ◽  
Viera Kučerová ◽  
Martin Kotek ◽  
...  
Keyword(s):  
Energy Density ◽  
Vapor Pressure ◽  
Physicochemical Properties ◽  
Octane Number ◽  
Alternative Fuel ◽  
Viable Alternative ◽  
Advanced Biofuel ◽  
Gasoline Engines ◽  
Lower Temperature

Biobutanol is a renewable, less polluting, and potentially viable alternative fuel to conventional gasoline. Biobutanol can be produced from same sources as bioethanol, and it has many advantages over the widespread bioethanol. This paper systematically analyzes biobutanol fuel as an alternative to bioethanol in alcohol–gasoline mixtures and the physicochemical properties. Based on the conducted analyses, it was found that biobutanol mixtures have a more suitable behavior of vapor pressure without the occurrence of azeotrope, do not form a separate phase in lower temperature, it has higher energy density, but slightly reduce the octane number and a have higher viscosity. However, in general, biobutanol has many advantageous properties that could allow its use in gasoline engines instead of the commonly used bioethanol.

Minimally Empirical Double Hybrid Functionals Trained Against the GMTKN55 Database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4

2019 ◽  
Author(s):  
Golokesh Santra ◽  
Nitai Sylvetsky ◽  
Gershom Martin
Keyword(s):  
Substantial Improvement ◽  
Viable Alternative ◽  
Mean Absolute Deviation ◽  
Dispersion Correction ◽  
Training Set ◽  
Weighted Mean ◽  
Absolute Deviation ◽  
Hybrid Functionals ◽  
Scaling Algorithms

We present a family of minimally empirical double-hybrid DFT functionals parametrized against the very large and diverse GMTKN55 benchmark. The very recently proposed wB97M(2) empirical double hybrid (with 16 empirical parameters) has the lowest WTMAD2 (weighted mean absolute deviation over GMTKN55) ever reported at 2.19 kcal/mol. However, our xrevDSD-PBEP86-D4 functional reaches a statistically equivalent WTMAD2=2.22 kcal/mol, using just a handful of empirical parameters, and the xrevDOD-PBEP86-D4 functional reaches 2.25 kcal/mol with just opposite-spin MP2 correlation, making it amenable to reduced-scaling algorithms. In general, the D4 empirical dispersion correction is clearly superior to D3BJ. If one eschews dispersion corrections of any kind, noDispSD-SCAN offers a viable alternative. Parametrization over the entire GMTKN55 dataset yields substantial improvement over the small training set previously employed in the DSD papers.

Minimally Empirical Double Hybrid Functionals Trained Against the GMTKN55 Database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4

2019 ◽  
Author(s):  
Golokesh Santra ◽  
Nitai Sylvetsky ◽  
Gershom Martin
Keyword(s):  
Substantial Improvement ◽  
Viable Alternative ◽  
Mean Absolute Deviation ◽  
Dispersion Correction ◽  
Training Set ◽  
Weighted Mean ◽  
Absolute Deviation ◽  
Hybrid Functionals ◽  
Scaling Algorithms

We present a family of minimally empirical double-hybrid DFT functionals parametrized against the very large and diverse GMTKN55 benchmark. The very recently proposed wB97M(2) empirical double hybrid (with 16 empirical parameters) has the lowest WTMAD2 (weighted mean absolute deviation over GMTKN55) ever reported at 2.19 kcal/mol. However, our xrevDSD-PBEP86-D4 functional reaches a statistically equivalent WTMAD2=2.22 kcal/mol, using just a handful of empirical parameters, and the xrevDOD-PBEP86-D4 functional reaches 2.25 kcal/mol with just opposite-spin MP2 correlation, making it amenable to reduced-scaling algorithms. In general, the D4 empirical dispersion correction is clearly superior to D3BJ. If one eschews dispersion corrections of any kind, noDispSD-SCAN offers a viable alternative. Parametrization over the entire GMTKN55 dataset yields substantial improvement over the small training set previously employed in the DSD papers.

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