On the public citation graph
Predicting Comminution of Lignocellulosic Biomass: Physical Experiment, Population Balance Model, and Deep Learning
0 signed · 57 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/1096-9853(200101)25:1<83::aid-nag120>3.0.co;2-kopenalex-extracted
Modelling comminution devices using DEM
inferredcites →https://doi.org/10.1002/bbb.1755openalex-extracted
Corn stover as feedstock for the production of ethanol: chemical composition of different anatomical fractions and varieties
inferredcites →https://doi.org/10.1002/ceat.200800055openalex-extracted
Discrete Element Method Simulation of Effect of Aggregate Shape on Fragmentation of Particle Composite
inferredcites →https://doi.org/10.1007/s00466-023-02343-6openalex-extracted
Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading
inferredcites →https://doi.org/10.1007/s10068-017-0291-zopenalex-extracted
Effect of moisture content and particle size on grinding kinetics and flowability of balloon flower (Platycodon grandiflorum)
inferredcites →https://doi.org/10.1016/j.apt.2018.10.009openalex-extracted
Population balance modeling applied to the milling of pharmaceutical extrudate for use in scale-up
inferredcites →https://doi.org/10.1016/j.biombioe.2020.105649openalex-extracted
Discrete element modeling of switchgrass particles under compression and rotational shear
inferredcites →https://doi.org/10.1016/j.biombioe.2021.106276openalex-extracted
A review on the role of pretreatment technologies in the hydrolysis of lignocellulosic biomass of corn stover
inferredcites →https://doi.org/10.1016/j.biortech.2015.12.024openalex-extracted
Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach
inferredcites →https://doi.org/10.1016/j.biortech.2022.128547openalex-extracted
Machine learning for hydrothermal treatment of biomass: A review
inferredcites →https://doi.org/10.1016/j.biosystemseng.2022.02.016openalex-extracted
The elastoplastic flexural behaviour of corn stalks
inferredcites →https://doi.org/10.1016/j.biosystemseng.2023.10.008openalex-extracted
An experiment-informed discrete element modelling study of knife milling for flexural biomass feedstocks
inferredcites →https://doi.org/10.1016/j.ces.2004.07.020openalex-extracted
Scale-up method of planetary ball mill
inferredcites →https://doi.org/10.1016/j.ces.2004.10.002openalex-extracted
Analysis of the milling rate of pharmaceutical powders using the Distinct Element Method (DEM)
inferredcites →https://doi.org/10.1016/j.ces.2005.03.064openalex-extracted
From single particle impact behaviour to modelling of impact mills
inferredcites →https://doi.org/10.1016/j.ces.2017.09.029openalex-extracted
A bonded sphero-cylinder model for the discrete element simulation of elasto-plastic fibers
inferredcites →https://doi.org/10.1016/j.compag.2016.02.018openalex-extracted
A discrete element approach for modelling the compression of crop stems
inferredcites →https://doi.org/10.1016/j.compag.2016.03.022openalex-extracted
A discrete element approach for modelling bendable crop stems
inferredcites →https://doi.org/10.1016/j.dche.2023.100103openalex-extracted
Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product management
inferredcites →https://doi.org/10.1016/j.fuproc.2014.11.020openalex-extracted
Approach to the breakage behavior of comminuted poplar and corn stover under single impact
inferredcites →https://doi.org/10.1016/j.jiec.2016.06.002openalex-extracted
Production and utilization of biochar: A review
inferredcites →https://doi.org/10.1016/j.jmapro.2021.09.053openalex-extracted
Semiempirical heat flux model of hard-brittle bone material in ductile microgrinding
inferredcites →https://doi.org/10.1016/j.mineng.2003.07.003openalex-extracted
Further validation of DEM modeling of milling: effects of liner profile and mill speed
inferredcites →https://doi.org/10.1016/j.mineng.2003.08.007openalex-extracted
Applying discrete element modelling to vertical and horizontal shaft impact crushers
inferredcites →https://doi.org/10.1016/j.mineng.2006.08.011openalex-extracted
Discrete element method simulation of bed comminution
inferredcites →https://doi.org/10.1016/j.minpro.2006.08.003openalex-extracted
The future of comminution modelling
inferredcites →https://doi.org/10.1016/j.powtec.2004.04.017openalex-extracted
A preliminary simulation model for fine grinding in high speed hammer mills
inferredcites →https://doi.org/10.1016/j.powtec.2012.12.019openalex-extracted
Investigation of comminution in a Wiley Mill: Experiments and DEM Simulations
inferredcites →https://doi.org/10.1016/j.powtec.2013.05.032openalex-extracted
The contribution of DEM to the science of comminution
inferredcites →https://doi.org/10.1016/j.powtec.2015.01.060openalex-extracted
Population balance model for biomass milling
inferredcites →https://doi.org/10.1016/j.powtec.2017.07.002openalex-extracted
Feeding properties and behavior of hammer- and knife-milled pine
inferredcites →https://doi.org/10.1016/j.powtec.2018.12.072openalex-extracted
Discrete element modeling of deformable pinewood chips in cyclic loading test
inferredcites →https://doi.org/10.1016/j.powtec.2021.03.008openalex-extracted
A nonlinear elasto-plastic bond model for the discrete element modeling of woody biomass particles
inferredcites →https://doi.org/10.1016/j.powtec.2022.117535openalex-extracted
Characterization of particle size and moisture content effects on mechanical and feeding behavior of milled corn (Zea mays L.) stover
inferredcites →https://doi.org/10.1016/j.ress.2020.107312openalex-extracted
Random forests for global sensitivity analysis: A selective review
inferredcites →https://doi.org/10.1016/s0009-2509(01)00386-4openalex-extracted
Population balance modeling. Promise for the future
inferredcites →https://doi.org/10.1016/s0032-5910(00)00349-1openalex-extracted
On predicting roller milling performance
inferredcites →https://doi.org/10.1016/s0032-5910(02)00217-6openalex-extracted
Breakage behaviour of different materials—construction of a mastercurve for the breakage probability
inferredcites →https://doi.org/10.1016/s0032-5910(99)00120-5openalex-extracted
Numerical simulations of agglomerate impact breakage
inferredcites →https://doi.org/10.1016/s0892-6875(98)00093-4openalex-extracted
Predicting charge motion, power draw, segregation and wear in ball mills using discrete element methods
inferredcites →https://doi.org/10.1016/s0960-8524(01)00212-7openalex-extracted
Hydrolysis of lignocellulosic materials for ethanol production: a review
inferredcites →https://doi.org/10.1021/acssuschemeng.0c00402openalex-extracted
A Review of Computational Models for the Flow of Milled Biomass Part I: Discrete-Particle Models
inferredcites →https://doi.org/10.1021/acssuschemeng.0c00412openalex-extracted
A Review of Computational Models for the Flow of Milled Biomass Part II: Continuum-Mechanics Models
inferredcites →https://doi.org/10.1038/s42256-021-00302-5openalex-extracted
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
inferredcites →https://doi.org/10.1038/scientificamerican0792-66openalex-extracted
Genetic Algorithms
inferredcites →https://doi.org/10.1088/0022-3727/29/2/021openalex-extracted
Numerical simulation of the impact fracture and fragmentation of agglomerates
inferredcites →https://doi.org/10.1109/72.392253openalex-extracted
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
inferredcites →https://doi.org/10.1137/19m1274067openalex-extracted
DeepXDE: A Deep Learning Library for Solving Differential Equations
inferredcites →https://doi.org/10.1146/annurev-chembioeng-060713-040241openalex-extracted
Population Balance Modeling: Current Status and Future Prospects
inferredcites →https://doi.org/10.1208/s12249-020-01834-6openalex-extracted
Applications of the Linear Mass-Sectional Breakage Population Balance to Various Milling Process Configurations
inferredcites →https://doi.org/10.13031/aim.202300067openalex-extracted
Defining Particle Size Distribution of Milled Biomass: Sieve Diameter versus Surface Area
inferredcites →https://doi.org/10.15376/biores.15.4.9337-9350openalex-extracted
A discrete element method model of corn stalk and its mechanical characteristic parameters
inferredcites →https://doi.org/10.3389/fenrg.2022.859248openalex-extracted
Flowability of Crumbler Rotary Shear Size-Reduced Granular Biomass: An Experiment-Informed Modeling Study on the Angle of Repose
inferredcites →https://doi.org/10.3390/ijms22084107openalex-extracted
Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data
inferredcites →https://doi.org/10.3390/pr6060069openalex-extracted
Effect of Moisture Content on the Grinding Process and Powder Properties in Food: A Review
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-extracted
Scikit-learn: Machine Learning in Python
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.