Prediction of Yield Sooting Index Utilizing Artificial Neural Networks and Adaptive-Network-Based Fuzzy Inference Systems

[1]  S. M. Sarathy,et al.  Artificial intelligence-driven design of fuel mixtures , 2022, Communications Chemistry.

[2]  Abdul Gani Abdul Jameel.  Identification and Quantification of Hydrocarbon Functional Groups in Gasoline Using 1H-NMR Spectroscopy for Property Prediction , 2021, Molecules.

[3]  Abdul Gani Abdul Jameel,et al.  Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks , 2021, Processes.

[4]  Abdul Gani Abdul Jameel,et al.  A Comprehensive Model for Cetane Number Prediction Using Machine Learning , 2021 .

[5]  S. M. Sarathy,et al.  Probing the Chemical Kinetics of Minimalist Functional Group Gasoline Surrogates , 2021 .

[6]  Ibrahim A. Alghamdi,et al.  Swirling Flame Combustion of Heavy Fuel Oil: Effect of Fuel Sulfur Content , 2020, Journal of Energy Resources Technology.

[7]  Peter C. St. John,et al.  A comparison of computational models for predicting yield sooting index , 2020 .

[8]  W. Pitz,et al.  Sooting tendencies of 20 bio-derived fuels for advanced spark-ignition engines , 2020 .

[9]  Tong Zhang,et al.  Numerical Simulation of Particle Size Influence on the Breakage Mechanism of Broken Coal , 2020 .

[10]  M. Rezakazemi,et al.  Fluid Velocity Prediction Inside Bubble Column Reactor Using ANFIS Algorithm Based on CFD Input Data , 2020, Arabian Journal for Science and Engineering.

[11]  C. McEnally,et al.  Numerical investigation of the pressure-dependence of yield sooting indices for n-alkane and aromatic species , 2019, Fuel.

[12]  C. Sung,et al.  The blending effect on the sooting tendencies of alternative/conventional jet fuel blends in non-premixed flames , 2019, Fuel.

[13]  Nadrah A. Alawani,et al.  Characterization of aromatic hydrocarbons and sulfur heterocycles in Saudi Arabian heavy crude oil by gel permeation chromatography and ultrahigh resolution mass spectrometry , 2019, Fuel.

[14]  S. M. Sarathy,et al.  A minimalist functional group (MFG) approach for surrogate fuel formulation , 2018, Combustion and Flame.

[15]  Peter C. St. John,et al.  Measuring and predicting sooting tendencies of oxygenates, alkanes, alkenes, cycloalkanes, and aromatics on a unified scale , 2018 .

[16]  Peter C. St. John,et al.  A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure , 2017 .

[17]  N. Balasubramanian,et al.  Treatment of Tannery Effluent Using a Rotating Disc Electrochemical Reactor , 2017, Water environment research : a research publication of the Water Environment Federation.

[18]  S. Mani Sarathy,et al.  Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression , 2016 .

[19]  William L. Roberts,et al.  Calculation of Average Molecular Parameters, Functional Groups, and a Surrogate Molecule for Heavy Fuel Oils Using 1H and 13C Nuclear Magnetic Resonance Spectroscopy , 2016 .

[20]  Dhrubajyoti D. Das,et al.  Sooting tendencies of unsaturated esters in nonpremixed flames , 2015 .

[21]  S. Simonich,et al.  Occurrence of High Levels of Persistent Organic Pollutants (POPs) in Particulate Matter of the Ambient Air of Riyadh, Saudi Arabia , 2014, Arabian Journal for Science and Engineering.

[22]  A. Boehman,et al.  Effects of the Chemical Structure and Composition of Surrogate Gasoline Fuels on Homogeneous Charge Compression Ignition Combustion in a Single-Cylinder Engine , 2014 .

[23]  Cyrus Aghanajafi,et al.  Prediction of Free Convection from Vertical and Inclined Rows of Horizontal Isothermal Cylinders Using ANFIS , 2014 .

[24]  Magín Lapuerta,et al.  Group additivity in soot formation for the example of C-5 oxygenated hydrocarbon fuels , 2013 .

[25]  B. DeAngelo,et al.  Bounding the role of black carbon in the climate system: A scientific assessment , 2013 .

[26]  Alan D. Lopez,et al.  A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 , 2012, The Lancet.

[27]  A. Saeedi,et al.  Experimental and Numerical Study on the Effect of Soot Injection on NOx Reduction and Radiation Enhancement in a Natural Gas Turbulent Flame , 2012, Arabian Journal for Science and Engineering.

[28]  Adel F Sarofim,et al.  Pollutant emissions from gasoline combustion. 1. Dependence on fuel structural functionalities. , 2008, Environmental science & technology.

[29]  C. McEnally,et al.  Improved sooting tendency measurements for aromatic hydrocarbons and their implications for naphthalene formation pathways , 2007 .

[30]  S. Razzak,et al.  A machine learning model for predicting threshold sooting index (TSI) of fuels containing alcohols and ethers , 2022, Fuel.

[31]  S. M. Sarathy,et al.  Environmental Challenges and Opportunities in Marine Engine Heavy Fuel Oil Combustion , 2019, Lecture Notes in Civil Engineering.

[32]  Dhrubajyoti D. Das,et al.  Two-dimensional soot volume fraction measurements in flames doped with large hydrocarbons , 2017 .

[33]  A. Rushdi,et al.  Occurrence of High Levels of Persistent Organic Pollutants (POPs) in Particulate Matter of the Ambient Air of Riyadh, Saudi Arabia , 2015 .

[34]  C. McEnally,et al.  Sooting tendencies of nonvolatile aromatic hydrocarbons , 2009 .

[35]  A. Drews.  Standard Test Method for Smoke Point of Kerosine and Aviation Turbine Fuel , 1998 .

[36]  H. F. Calcote,et al.  Effect of molecular structure on incipient soot formation , 1983 .

[37]  Abdul Gani Abdul Jameel,et al.  Smoke point prediction of oxygenated fuels using neural networks , 2022, Fuel.