Influence of pyrolysis conditions and the nature of the wood on the quality of charcoal as a reducing agent
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Analytical and Applied Pyrolysis"
DOI: 10.1016/j.jaap.2018.10.013
Abstract: Abstract The production of charcoal for metallurgical applications requires careful selection of the wood and control of the pyrolysis conditions to ensure acceptable charcoal quality. The main properties of charcoal to be considered are density,… read more here.
Keywords: pyrolysis conditions; charcoal; nature wood; density ... See more keywords
Effects of pyrolysis conditions and ash formation on gasification rates of biomass char
Sign Up to like & getrecommendations! Published in 2017 at "Energy & Fuels"
DOI: 10.1021/acs.energyfuels.7b00688
Abstract: Pyrolysis conditions and the presence of ash-forming elements significantly influence char properties and its oxidation or gasification reactivity. In this study, intrinsic gasification rates of char from high heating rate pyrolysis were analyzed with isothermal… read more here.
Keywords: ash formation; char; pyrolysis conditions; gasification rates ... See more keywords
Effect of pH, Volatile Content, and Pyrolysis Conditions on Surface Area and O/C and H/C Ratios of Biochar: Towards Understanding Performance of Biochar Using Simplified Approach
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Hazardous, Toxic, and Radioactive Waste"
DOI: 10.1061/(asce)hz.2153-5515.0000545
Abstract: Abstract The objective of this study was to predict biochar properties [surface area and oxygen-to-carbon (O/C) and hydrogen-to-carbon (H/C) ratios] from pyrolysis conditions, volatile matter conte... read more here.
Keywords: pyrolysis conditions; surface area; biochar; effect volatile ... See more keywords
Machine learning prediction of pyrolytic products of lignocellulosic biomass based on physicochemical characteristics and pyrolysis conditions.
Sign Up to like & getrecommendations! Published in 2022 at "Bioresource technology"
DOI: 10.2139/ssrn.4191315
Abstract: This study predicts pyrolytic product yields via machine learning algorithms from biomass physicochemical characteristics and pyrolysis conditions. Random forest (RF), gradient boosting decision tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Adaptive Boost (Adaboost) algorithms are… read more here.
Keywords: machine learning; pyrolysis conditions; pyrolysis; characteristics pyrolysis ... See more keywords