☆ 4.7 Article

Predicting tobacco pyrolysis based on chemical constituents and heating conditions using machine learning approaches

FUEL (2023)

Related references

Note: Only part of the references are listed.

Article Energy & Fuels

Predicting co-pyrolysis of coal and biomass using machine learning approaches

Hao Wei et al.

Summary: The study developed machine learning models using the random forest algorithm to accurately predict and applied co-thermochemical conversion, with extremely trees model showing better performance in prediction and generalization. The results suggest that the known of biomass pyrolysis is better than known of coal pyrolysis, and recommendations for input feature groups and variable importance measurements are provided.

FUEL (2022)

Add to Collection

Article Chemistry, Multidisciplinary

Article http://pubs.acs.org/journal/acsodf Toba-CPD: An Extended Chemical Percolation Devolatilization Model for Tobacco Pyrolysis

Hao Wei et al.

Summary: The performance of the Bio-CPD model on tobacco pyrolysis was assessed and large deviations were found. To improve the model, a grid-search optimization strategy was used to modify the kinetic parameters and develop the Toba-CPD model, which could well reproduce the pyrolysis of different tobacco types under a wide range of heating rates.

ACS OMEGA (2022)

Add to Collection

Article Agricultural Engineering

Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics

Qinghui Tang et al.

Summary: This study aimed to predict pyrolytic gas yield and compositions based on pyrolysis conditions and biomass characteristics using machine learning algorithms and feature reduction. The results showed that six features were sufficient for accurate yield prediction, while compositions only required three. The study revealed the higher relative contribution of pyrolysis conditions to yield, CO2, and H2 compared to biomass characteristics.

BIORESOURCE TECHNOLOGY (2021)

Add to Collection

Article Agricultural Engineering

A comparative study of machine learning methods for bio-oil yield prediction-A genetic algorithm-based features selection

Zahid Ullah et al.

Summary: A novel genetic algorithm-based feature selection approach was used in this study to predict bio-oil yield using four different machine learning methods. The findings suggested that Random Forest model performed the best and was able to reliably predict bio-oil yield. The research provided new insights into the pyrolysis process of biomass and offered a new approach to improve bio-oil yield.

BIORESOURCE TECHNOLOGY (2021)

Add to Collection

Article Chemistry, Analytical

The effect of oxygen on in-situ evolution of chemical structures during the autothermal process of tobacco

Yuhan Peng et al.

Summary: The presence of oxygen in the pyrolysis system affects the removal of oxygen and hydrogen during biomass pyrolysis, promoting the generation of aromatic char structures with more ordered and larger aromatic crystallite sizes. Oxygen influences the relative content of volatile and semivolatile products, but not their types.

JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS (2021)

Add to Collection

Article Energy & Fuels

Comparative Study on Different Treatments of Coal Devolatilization for Pulverized Coal Combustion Simulation

Jiangkuan Xing et al.

ENERGY & FUELS (2020)

Add to Collection

Article Energy & Fuels

Novel Sensitivity Study for Biomass Directional Devolatilization by Random Forest Models

Jiangkuan Xing et al.

ENERGY & FUELS (2020)

Add to Collection

Article Energy & Fuels

Large eddy simulations and analysis of NO emission characteristics in a laboratory pulverized coal flame

Chunguang Zhao et al.

FUEL (2020)

Add to Collection

Article Thermodynamics

Comparative investigation on thermal degradation of flue-cured tobacco with different particle sizes by a macro-thermogravimetric analyzer and their apparent kinetics based on distributed activation energy model

Gaofei Guo et al.

JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY (2019)

Add to Collection

Article Green & Sustainable Science & Technology

Predictive single-step kinetic model of biomass devolatilization for CFD applications: A comparison study of empirical correlations (EC), artificial neural networks (ANN) and random forest (RF)

Jiangkuan Xing et al.

RENEWABLE ENERGY (2019)

Add to Collection

Article Thermodynamics

TG-FTIR study of evolved gas in the decomposition of different types of tobacco. Effect of the addition of SBA-15

Emilio Calabuig et al.

THERMOCHIMICA ACTA (2019)

Add to Collection

Article Energy & Fuels

Numerical Studies of Coal Devolatilization Characteristics with Gas Temperature Fluctuation

Jiangkuan Xing et al.

ENERGY & FUELS (2018)

Add to Collection

Article Energy & Fuels

Artificial neural network model for the prediction of kinetic parameters of biomass pyrolysis from its constituents

Sasithorn Sunphorka et al.

FUEL (2017)

Add to Collection

Review Thermodynamics

Lignocellulosic biomass pyrolysis mechanism: A state-of-the-art review

Shurong Wang et al.

PROGRESS IN ENERGY AND COMBUSTION SCIENCE (2017)

Add to Collection

Article Agricultural Engineering

Application of artificial neural networks to co-combustion of hazelnut husk-lignite coal blends

Zeynep Yildiz et al.

BIORESOURCE TECHNOLOGY (2016)

Add to Collection

Article Engineering, Chemical

Volatile Products Formed in the Thermal Decomposition of a Tobacco Substrate

Federica Barontini et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2013)

Add to Collection

Article Agricultural Engineering

Kinetic study on pyrolysis of tobacco residues from the cigarette industry

Wenhua Gao et al.

INDUSTRIAL CROPS AND PRODUCTS (2013)

Add to Collection

Review Substance Abuse

Health warning messages on tobacco products: a review

David Hammond

TOBACCO CONTROL (2011)

Add to Collection

Article Thermodynamics

Thermogravimetric study on stem biomass of Nicotiana tabacum

Yong Joo Sung et al.

THERMOCHIMICA ACTA (2009)

Add to Collection

Article Computer Science, Artificial Intelligence

Extremely randomized trees

P Geurts et al.

MACHINE LEARNING (2006)

Add to Collection

Article Chemistry, Analytical

The pyrolysis of tobacco ingredients

RR Baker et al.

JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS (2004)

Add to Collection

Article Thermodynamics

An artificial intelligence treatment of devolatilization for pulverized coal and biomass in co-fired flames

T Abbas et al.

COMBUSTION AND FLAME (2003)

Add to Collection

© Peeref 2019-2024. All rights reserved.