Predicting Biodiesel Properties and its Optimal Fatty Acid Profile Via Explainable Machine Learning

[1]  Xiaonan Wang,et al.  Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening , 2021 .

[2]  Yingru Zhao,et al.  A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification , 2021, Applied Energy.

[3]  Anand Krishnasamy,et al.  A critical review on available models to predict engine fuel properties of biodiesel , 2021, Renewable and Sustainable Energy Reviews.

[4]  Y. Ok,et al.  Applied Machine Learning for Prediction of CO2 Adsorption on Biomass Waste-Derived Porous Carbons. , 2021, Environmental science & technology.

[5]  Soteris A. Kalogirou,et al.  Machine learning technology in biodiesel research: A review , 2021, Progress in Energy and Combustion Science.

[6]  Yee Shee Tan,et al.  A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing , 2021 .

[7]  M.I. Jahirul,et al.  Investigation of correlation between chemical composition and properties of biodiesel using principal component analysis (PCA) and artificial neural network (ANN) , 2021 .

[8]  Meng Wang,et al.  Improving the CFPP property of biodiesel via composition design: An intelligent raw material selection strategy based on different machine learning algorithms , 2021 .

[9]  Salah I. Yahya,et al.  Estimation of kinematic viscosity of biodiesel-diesel blends: Comparison among accuracy of intelligent and empirical paradigms , 2021 .

[10]  Jiajia Cai,et al.  Online prediction of mechanical properties of hot rolled steel plate using machine learning , 2021 .

[11]  M. E. Günay,et al.  Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning , 2021 .

[12]  N. Tippayawong,et al.  Machine learning application to predict yields of solid products from biomass torrefaction , 2020, Renewable Energy.

[13]  P. Balasubramanian,et al.  Predicting algal biochar yield using eXtreme Gradient Boosting (XGB) algorithm of machine learning methods , 2020 .

[14]  M. H. Doranehgard,et al.  Modeling of cetane number of biodiesel from fatty acid methyl ester (FAME) information using GA-, PSO-, and HGAPSO- LSSVM models , 2020 .

[15]  D. Bui,et al.  Feature validity during machine learning paradigms for predicting biodiesel purity , 2020 .

[16]  A. Luna,et al.  Multivariate regression models obtained from near-infrared spectroscopy data for prediction of the physical properties of biodiesel and its blends , 2020 .

[17]  A. Olabi,et al.  Fuzzy-modeling with Particle Swarm Optimization for enhancing the production of biodiesel from Microalga , 2018, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[18]  Evangelos G. Giakoumis,et al.  Estimation of biodiesel cetane number, density, kinematic viscosity and heating values from its fatty acid weight composition , 2018, Fuel.

[19]  Yi-hung Chen,et al.  Dependence of cold filter plugging point on saturated fatty acid profile of biodiesel blends derived from different feedstocks , 2017 .

[20]  Niraj Kumar Oxidative stability of biodiesel: Causes, effects and prevention , 2017 .

[21]  Filiz Al-Shanableh,et al.  Prediction of Cold Flow Properties of Biodiesel Fuel Using Artificial Neural Network , 2016 .

[22]  Soleiman Hosseinpour,et al.  Exact estimation of biodiesel cetane number (CN) from its fatty acid methyl esters (FAMEs) profile using partial least square (PLS) adapted by artificial neural network (ANN) , 2016 .

[23]  Baharak Sajjadi,et al.  A comprehensive review on properties of edible and non-edible vegetable oil-based biodiesel: Composition, specifications and prediction models , 2016 .

[24]  Pezhman Kazemi,et al.  Fatty Acid Methyl Ester (FAME) composition used for estimation of biodiesel cetane number employing random forest and artificial neural networks: A new approach , 2016 .

[25]  R. Lanjekar,et al.  A review of the effect of the composition of biodiesel on NOx emission, oxidative stability and cold flow properties , 2016 .

[26]  B. Moser Fuel property enhancement of biodiesel fuels from common and alternative feedstocks via complementary blending. , 2016 .

[27]  J. Marchetti,et al.  A review on recent advancement in catalytic materials for biodiesel production. , 2015 .

[28]  Purnanand V. Bhale,et al.  Biodiesel properties and automotive system compatibility issues , 2015 .

[29]  Teuku Meurah Indra Mahlia,et al.  A comparative evaluation of physical and chemical properties of biodiesel synthesized from edible and non-edible oils and study on the effect of biodiesel blending , 2013 .

[30]  Ramkrishna Sen,et al.  Fuel properties, engine performance and environmental benefits of biodiesel produced by a green process , 2013 .

[31]  Evangelos G. Giakoumis,et al.  A statistical investigation of biodiesel physical and chemical properties, and their correlation with the degree of unsaturation , 2013 .

[32]  Haji Hassan Masjuki,et al.  Non-edible vegetable oils: A critical evaluation of oil extraction, fatty acid compositions, biodiesel production, characteristics, engine performance and emissions production , 2013 .

[33]  Gholamhassan Najafi,et al.  Current biodiesel production technologies: A comparative review , 2012 .

[34]  S. Hoekman,et al.  Review of biodiesel composition, properties, and specifications , 2012 .

[35]  J. Rodríguez-Rodríguez,et al.  Predicting cetane number, kinematic viscosity, density and higher heating value of biodiesel from its fatty acid methyl ester composition , 2012 .

[36]  M. P. Dorado,et al.  Multiple response optimization of vegetable oils fatty acid composition to improve biodiesel physical properties. , 2011, Bioresource technology.

[37]  Ching-Yuan Chang,et al.  Biodiesel production from tung (Vernicia montana) oil and its blending properties in different fatty acid compositions. , 2010, Bioresource technology.

[38]  Naoko Ellis,et al.  Perspectives on biodiesel as a sustainable fuel , 2010 .

[39]  Deog-Keun Kim,et al.  Blending effects of biodiesels on oxidation stability and low temperature flow properties. , 2008, Bioresource technology.