Multi-output chemometrics model for gasoline compounding

PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives

(2020) Ricardo Santana et al.   ACS Combinatorial Science

Machine learning enabled high-throughput screening of hydrocarbon molecules for the design of next generation fuels

(2020) Guozhu Li et al.   FUEL

Influence of gasoline olefin and aromatic content on exhaust emissions of 15% ethanol blends

(2020) I. Schifter et al.   FUEL

Radial basis function (RBF) network for modeling gasoline properties

(2019) Afshin Tatar et al.   PETROLEUM SCIENCE AND TECHNOLOGY

Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks

(2019) Deyani Nocedo-Mena et al.   Journal of Chemical Information and Modeling

NIR-based octane rating simulator for use in gasoline compounding processes

(2019) Neirivaldo Cavalcante da Silva et al.   FUEL

PTML Model of Enzyme Subclasses for Mining the Proteome of Bio-fuel Producing Microorganisms

(2019) Riccardo Concu et al.   JOURNAL OF PROTEOME RESEARCH

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

(2019) Emilia Vásquez-Domínguez et al.   MOLECULAR PHARMACEUTICS

Chemical compositions, properties, and standards for different generation biodiesels: A review

(2019) Digambar Singh et al.   FUEL

Feasibility study on prediction of gasoline octane number using NIR spectroscopy combined with manifold learning and neural network

(2019) Shutao Wang et al.   SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

Permittivity of gasoline/methanol blends. Application to blend composition estimation

(2019) J. Corach et al.   FUEL

An exploratory analysis of biofuel under the utopian environment

(2019) Swarup Paul et al.   FUEL

Formulating gasoline surrogate for emulating octane blending properties with ethanol

(2019) Hao Yuan et al.   FUEL

Prediction of higher heating value based on elemental composition for lignin and other fuels

(2019) Yury Maksimuk et al.   FUEL

A survey on machine learning for data fusion

(2019) Tong Meng et al.   Information Fusion

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics

(2018) Joana Ferreira da Costa et al.   ACS Chemical Neuroscience

Correlation Coefficients

(2018) Patrick Schober et al.   ANESTHESIA AND ANALGESIA

Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies

(2018) Lorena Simón-Vidal et al.   Journal of Chemical Information and Modeling

Expert-guided optimization for 3D printing of soft and liquid materials

(2018) Sara Abdollahi et al.   PLoS One

Perturbation Theory–Machine Learning Study of Zeolite Materials Desilication

(2018) Vincent Blay et al.   Journal of Chemical Information and Modeling

PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer

(2018) Harbil Bediaga et al.   ACS Combinatorial Science

Purely predictive method for density, compressibility, and expansivity for hydrocarbon mixtures and diesel and jet fuels up to high temperatures and pressures

(2018) Houman B. Rokni et al.   FUEL

Significance of RON and MON to a modern DISI engine

(2017) Chongming Wang et al.   FUEL

Classification of Fuel Blends Using Exploratory Analysis with Combined Data from Infrared Spectroscopy and Stable Isotope Analysis

(2016) Victor Hugo J. M. dos Santos et al.   ENERGY & FUELS

A tutorial on the validation of qualitative methods: From the univariate to the multivariate approach

(2015) M. Isabel López et al.   ANALYTICA CHIMICA ACTA

The octane numbers of ethanol blended with gasoline and its surrogates

(2013) Tien Mun Foong et al.   FUEL

Combination of Similarity Rankings Using Data Fusion

(2013) Peter Willett   Journal of Chemical Information and Modeling

Support vector machine regression (SVR/LS-SVM)—an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data

(2011) Roman M. Balabin et al.   ANALYST

Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends

(2011) Harun Mohamed Ismail et al.   APPLIED ENERGY

Gasoline classification using near infrared (NIR) spectroscopy data: Comparison of multivariate techniques

(2010) Roman M. Balabin et al.   ANALYTICA CHIMICA ACTA

Prediction of Long-Residue Properties of Potential Blends from Mathematically Mixed Infrared Spectra of Pure Crude Oils by Partial Least-Squares Regression Models

(2009) Peter de Peinder et al.   ENERGY & FUELS

A Machine Learning Approach to Weighting Schemes in the Data Fusion of Similarity Coefficients

(2009) Jenny Chen et al.   Journal of Chemical Information and Modeling

Motor oil classification by base stock and viscosity based on near infrared (NIR) spectroscopy data

(2008) Roman M. Balabin et al.   FUEL

Investigation of ash deposit formation during co-firing of coal with sewage sludge, saw-dust and refuse derived fuel

(2008) Tomasz Kupka et al.   FUEL

Determination of motor gasoline adulteration using FTIR spectroscopy and multivariate calibration

(2008) Mohammad A. Al-Ghouti et al.   TALANTA

Gasoline classification by source and type based on near infrared (NIR) spectroscopy data

(2007) Roman M. Balabin et al.   FUEL