Research graph
References from A data-driven framework for coarse-mesh CFD error correction and rapid flow-field prediction in a mechanical flotation cell. Local targets link to admitted publications; unresolved targets remain external evidence.
Electrical resistance tomography-assisted analysis of dispersed phase hold-up in a gas-inducing mechanically stirred vessel
10.1016/j.ces.2011.07.048 · 2011 · External reference
CFD study of two-phase flow hydrodynamics in the imhoflotTM G-06 flotation cell using eulerian and lagrangian approaches
10.1016/j.mineng.2025.110039 · 2026 · External reference
Machine learning technology in biodiesel research: A review
10.1016/j.pecs.2021.100904 · 2021 · External reference
A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation
10.1016/j.nucengdes.2019.04.023 · 2019 · External reference
Numerical and experimental investigation of single phase flow characteristics in stirred tanks using rushton turbine and flotation impeller
10.1016/j.mineng.2015.08.018 · 2015 · External reference
CFD study of homogenization with dual rushton turbines—comparison with experimental results: Part II: the multiple reference frame
2002 · External reference
An artificial neural network-based machine learning approach to correct coarse-mesh-induced error in computational fluid dynamics modeling of cell culture bioreactor
10.1016/j.fbp.2023.11.004 · 2024 · External reference
CFD investigation of chalcopyrite flotation prediction coupled with flotation kinetic model
10.1016/j.seppur.2024.127203 · 2024 · External reference
CFD modelling of stirred tanks: numerical considerations
10.1016/j.mineng.2006.04.001 · 2006 · External reference
An experimental and computational investigation of vortex formation in an unbaffled stirred tank
10.1016/j.ces.2017.04.002 · 2017 · External reference
Scale-up design and field pilot experimental study of pulsed jet flotation machine
10.1016/j.psep.2025.107176 · 2025 · External reference
Development of a machine learning-based symbolic regression model for mixing time in large petroleum storage tanks
10.1016/j.ces.2025.121903 · 2025 · External reference
Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network
10.1016/j.renene.2008.08.008 · 2009 · External reference
Prediction of grade and recovery in flotation from physicochemical and operational aspects using machine learning models
10.1016/j.mineng.2022.107627 · 2022 · External reference
Machine-learning based error prediction approach for coarse-grid computational fluid dynamics (CG-CFD)
10.1016/j.pnucene.2019.103140 · 2020 · External reference
Study on methane hydration process in a semi-continuous stirred tank reactor
10.1016/j.enconman.2006.08.007 · 2007 · External reference
A deep-learning model for predicting spatiotemporal evolution in reactive fluidized bed reactor
10.1016/j.renene.2024.120245 · 2024 · External reference
Explainable AI models for predicting drop coalescence in microfluidics device
10.1016/j.cej.2023.148465 · 2024 · External reference
A computational fluid dynamics model for the flotation rate constant, part I: Model development
10.1016/j.mineng.2014.03.028 · 2014 · External reference
A CFD-kinetic model for the flotation rate constant, part II: Model validation
10.1016/j.mineng.2014.05.014 · 2014 · External reference
CFD modeling of gas dispersion and bubble size in a double turbine stirred tank
10.1016/j.ces.2005.11.061 · 2006 · External reference
Data-driven correction of coarse grid CFD simulations
10.1016/j.compfluid.2023.105971 · 2023 · External reference
A grid-induced and physics-informed machine learning CFD framework for turbulent flows
10.1007/s10494-023-00506-2 · 2024 · External reference
An extension to the grid-induced machine learning CFD framework for turbulent flows
10.1007/s10494-025-00667-2 · 2025 · External reference
Using CFD and machine learning to explore chaotic mixing in laminar diameter-transformed stirred tanks
2024 · External reference
Modeling and analysis of droplet generation in microchannels using interpretable machine learning methods
10.1016/j.cej.2025.161972 · 2025 · External reference
Implementation of a data-driven model for mesh-induced error corrections in CFD simulations of stirred tanks
10.1016/j.cherd.2025.09.050 · 2025 · External reference
Assessment of k – ε models using tetrahedral grids to describe the turbulent flow field of a PBT impeller and validation through the PIV technique
10.1016/j.cjche.2018.02.012 · 2018 · External reference
Numerical simulations of the dependency of flow pattern on impeller clearance in stirred vessels
10.1016/s0009-2509(01)00089-6 · 2001 · External reference
Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network
10.1016/j.apenergy.2008.09.017 · 2009 · External reference
Physics-informed machine learning for grade prediction in froth flotation
10.1016/j.mineng.2025.109297 · 2025 · External reference
Isotropic turbulence surpasses gravity in affecting bubble-particle collision interaction in flotation
10.1016/j.mineng.2018.03.033 · 2018 · External reference
A review of stochastic description of the turbulence effect on bubble-particle interactions in flotation
10.1016/j.minpro.2016.05.002 · 2016 · External reference
Improving zinc processing using computational fluid dynamics modelling – successes and opportunities
10.1016/j.mineng.2012.02.005 · 2012 · External reference
Sequential multi-scale modelling of mineral processing operations, with application to flotation cells
10.1016/j.mineng.2015.09.021 · 2016 · External reference
Modelling and measurement of multi-phase hydrodynamics in the outotec flotation cell
10.1016/j.mineng.2019.106033 · 2019 · External reference
Data-driven approach for design and optimization of rotor–stator mixers for miscible fluids with different viscosities
10.1016/j.cej.2024.155954 · 2024 · External reference
A modified model based on CFD simulation and experiments of chalcopyrite flotation
10.1016/j.seppur.2024.129634 · 2025 · External reference
Effects of turbulence modeling on the prediction of flow characteristics of mixing non-newtonian fluids in a stirred vessel
10.1016/j.cherd.2019.05.001 · 2019 · External reference
Learning time-aware multi-phase flow fields in coal-supercritical water fluidized bed reactor with deep learning
10.1016/j.energy.2022.125907 · 2023 · External reference
Comprehensive particle image velocimetry measurement and numerical model validations on the gas–liquid flow field in a lab-scale cyclonic flotation column
10.1016/j.cherd.2021.07.024 · 2021 · External reference
A rational interpretation of the role of turbulence in particle-bubble interactions
10.3390/min11091006 · 2021 · External reference
Characterization of large size flotation cells
10.1016/j.mineng.2005.09.005 · 2006 · External reference
Challenges in flotation scale-up: the impact of flotation kinetics and froth transport
10.1016/j.mineng.2023.108541 · 2024 · External reference
A novel scale-up approach for mechanical flotation cells
10.1016/j.mineng.2010.05.004 · 2010 · External reference
Large eddy simulation of passive scalar transport in a stirred tank for different diffusivities
10.1016/j.ijheatmasstransfer.2015.08.030 · 2015 · External reference
10.1016/j.mineng.2025.109814
10.1016/j.mineng.2025.109814 · External reference
Energy consumption, flow characteristics and energy-efficient design of cup-shape blade stirred tank reactors: Computational fluid dynamics and artificial neural network investigation
10.1016/j.energy.2021.122474 · 2022 · External reference
Predicting spatiotemporal distributions in a bubbling fluidized bed for biomass fast pyrolysis using convolutional neural networks
10.1021/acs.iecr.3c03812 · 2024 · External reference
Neural operator-based super-fidelity: a warm-start approach for accelerating steady-state simulations
10.1016/j.jcp.2025.113871 · 2025 · External reference
The effect of energy input on bubble-particle collision, attachment, detachment, and collection efficiencies in a mechanical flotation cell
10.1016/j.powtec.2025.120659 · 2025 · External reference
The application of machine learning methods for prediction of metal sorption onto biochars
10.1016/j.jhazmat.2019.06.004 · 2019 · External reference
A grid-induced and physics-informed machine learning CFD framework for turbulent flows
10.1007/s10494-023-00506-2 · ExternalCitation · doi-reference
An extension to the grid-induced machine learning CFD framework for turbulent flows
10.1007/s10494-025-00667-2 · ExternalCitation · doi-reference
Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network
10.1016/j.apenergy.2008.09.017 · ExternalCitation · doi-reference
Explainable AI models for predicting drop coalescence in microfluidics device
10.1016/j.cej.2023.148465 · ExternalCitation · doi-reference
Data-driven approach for design and optimization of rotor–stator mixers for miscible fluids with different viscosities
10.1016/j.cej.2024.155954 · ExternalCitation · doi-reference
Modeling and analysis of droplet generation in microchannels using interpretable machine learning methods
10.1016/j.cej.2025.161972 · ExternalCitation · doi-reference
CFD modeling of gas dispersion and bubble size in a double turbine stirred tank
10.1016/j.ces.2005.11.061 · ExternalCitation · doi-reference
Electrical resistance tomography-assisted analysis of dispersed phase hold-up in a gas-inducing mechanically stirred vessel
10.1016/j.ces.2011.07.048 · ExternalCitation · doi-reference
An experimental and computational investigation of vortex formation in an unbaffled stirred tank
10.1016/j.ces.2017.04.002 · ExternalCitation · doi-reference
Development of a machine learning-based symbolic regression model for mixing time in large petroleum storage tanks
10.1016/j.ces.2025.121903 · ExternalCitation · doi-reference
Effects of turbulence modeling on the prediction of flow characteristics of mixing non-newtonian fluids in a stirred vessel
10.1016/j.cherd.2019.05.001 · ExternalCitation · doi-reference
Comprehensive particle image velocimetry measurement and numerical model validations on the gas–liquid flow field in a lab-scale cyclonic flotation column
10.1016/j.cherd.2021.07.024 · ExternalCitation · doi-reference
Implementation of a data-driven model for mesh-induced error corrections in CFD simulations of stirred tanks
10.1016/j.cherd.2025.09.050 · ExternalCitation · doi-reference
Assessment of k – ε models using tetrahedral grids to describe the turbulent flow field of a PBT impeller and validation through the PIV technique
10.1016/j.cjche.2018.02.012 · ExternalCitation · doi-reference
Data-driven correction of coarse grid CFD simulations
10.1016/j.compfluid.2023.105971 · ExternalCitation · doi-reference
Study on methane hydration process in a semi-continuous stirred tank reactor
10.1016/j.enconman.2006.08.007 · ExternalCitation · doi-reference
Energy consumption, flow characteristics and energy-efficient design of cup-shape blade stirred tank reactors: Computational fluid dynamics and artificial neural network investigation
10.1016/j.energy.2021.122474 · ExternalCitation · doi-reference
Learning time-aware multi-phase flow fields in coal-supercritical water fluidized bed reactor with deep learning
10.1016/j.energy.2022.125907 · ExternalCitation · doi-reference
An artificial neural network-based machine learning approach to correct coarse-mesh-induced error in computational fluid dynamics modeling of cell culture bioreactor
10.1016/j.fbp.2023.11.004 · ExternalCitation · doi-reference
Large eddy simulation of passive scalar transport in a stirred tank for different diffusivities
10.1016/j.ijheatmasstransfer.2015.08.030 · ExternalCitation · doi-reference
Neural operator-based super-fidelity: a warm-start approach for accelerating steady-state simulations
10.1016/j.jcp.2025.113871 · ExternalCitation · doi-reference
The application of machine learning methods for prediction of metal sorption onto biochars
10.1016/j.jhazmat.2019.06.004 · ExternalCitation · doi-reference
Characterization of large size flotation cells
10.1016/j.mineng.2005.09.005 · ExternalCitation · doi-reference
CFD modelling of stirred tanks: numerical considerations
10.1016/j.mineng.2006.04.001 · ExternalCitation · doi-reference
A novel scale-up approach for mechanical flotation cells
10.1016/j.mineng.2010.05.004 · ExternalCitation · doi-reference
Improving zinc processing using computational fluid dynamics modelling – successes and opportunities
10.1016/j.mineng.2012.02.005 · ExternalCitation · doi-reference
A computational fluid dynamics model for the flotation rate constant, part I: Model development
10.1016/j.mineng.2014.03.028 · ExternalCitation · doi-reference
A CFD-kinetic model for the flotation rate constant, part II: Model validation
10.1016/j.mineng.2014.05.014 · ExternalCitation · doi-reference
Numerical and experimental investigation of single phase flow characteristics in stirred tanks using rushton turbine and flotation impeller
10.1016/j.mineng.2015.08.018 · ExternalCitation · doi-reference
Sequential multi-scale modelling of mineral processing operations, with application to flotation cells
10.1016/j.mineng.2015.09.021 · ExternalCitation · doi-reference
Isotropic turbulence surpasses gravity in affecting bubble-particle collision interaction in flotation
10.1016/j.mineng.2018.03.033 · ExternalCitation · doi-reference
Modelling and measurement of multi-phase hydrodynamics in the outotec flotation cell
10.1016/j.mineng.2019.106033 · ExternalCitation · doi-reference
Prediction of grade and recovery in flotation from physicochemical and operational aspects using machine learning models
10.1016/j.mineng.2022.107627 · ExternalCitation · doi-reference
Challenges in flotation scale-up: the impact of flotation kinetics and froth transport
10.1016/j.mineng.2023.108541 · ExternalCitation · doi-reference
Physics-informed machine learning for grade prediction in froth flotation
10.1016/j.mineng.2025.109297 · ExternalCitation · doi-reference
10.1016/j.mineng.2025.109814
10.1016/j.mineng.2025.109814 · ExternalCitation · doi-reference
CFD study of two-phase flow hydrodynamics in the imhoflotTM G-06 flotation cell using eulerian and lagrangian approaches
10.1016/j.mineng.2025.110039 · ExternalCitation · doi-reference
A review of stochastic description of the turbulence effect on bubble-particle interactions in flotation
10.1016/j.minpro.2016.05.002 · ExternalCitation · doi-reference
A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation
10.1016/j.nucengdes.2019.04.023 · ExternalCitation · doi-reference
Machine learning technology in biodiesel research: A review
10.1016/j.pecs.2021.100904 · ExternalCitation · doi-reference
Machine-learning based error prediction approach for coarse-grid computational fluid dynamics (CG-CFD)
10.1016/j.pnucene.2019.103140 · ExternalCitation · doi-reference
The effect of energy input on bubble-particle collision, attachment, detachment, and collection efficiencies in a mechanical flotation cell
10.1016/j.powtec.2025.120659 · ExternalCitation · doi-reference
Scale-up design and field pilot experimental study of pulsed jet flotation machine
10.1016/j.psep.2025.107176 · ExternalCitation · doi-reference
Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network
10.1016/j.renene.2008.08.008 · ExternalCitation · doi-reference
A deep-learning model for predicting spatiotemporal evolution in reactive fluidized bed reactor
10.1016/j.renene.2024.120245 · ExternalCitation · doi-reference
CFD investigation of chalcopyrite flotation prediction coupled with flotation kinetic model
10.1016/j.seppur.2024.127203 · ExternalCitation · doi-reference
A modified model based on CFD simulation and experiments of chalcopyrite flotation
10.1016/j.seppur.2024.129634 · ExternalCitation · doi-reference
Numerical simulations of the dependency of flow pattern on impeller clearance in stirred vessels
10.1016/s0009-2509(01)00089-6 · ExternalCitation · doi-reference
Predicting spatiotemporal distributions in a bubbling fluidized bed for biomass fast pyrolysis using convolutional neural networks
10.1021/acs.iecr.3c03812 · ExternalCitation · doi-reference
A rational interpretation of the role of turbulence in particle-bubble interactions
10.3390/min11091006 · ExternalCitation · doi-reference
