Research graph
References from Fast prediction of FCC regenerator temperature fields using digital – twin based on convolutional autoencoder dimensionality reduction and machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
A review of modelling of the FCC unit. Part I: the riser
10.3390/en15010308 · 2022 · External reference
State-of-the-art review of fluid catalytic cracking (FCC) catalyst regeneration Intensification technologies
10.3390/en15062061 · 2022 · External reference
Novel fluid catalytic cracking processes
2017 · External reference
Fluid catalytic cracking: recent developments on the grand old lady of zeolite catalysis
10.1039/c5cs00376h · 2015 · External reference
Progress in the CO2 capture technologies for fluid catalytic cracking (FCC) units—a review
10.3389/fenrg.2020.00062 · 2020 · External reference
Materials of construction for refinery units
2014 · External reference
Contributions of key countries, enterprises, and refineries to greenhouse gas emissions in global oil refining, 2000–2021
2023 · External reference
CFD modeling of the coke combustion in an industrial FCC regenerator
10.1016/j.ces.2016.12.055 · 2017 · External reference
Modeling FCC spent catalyst regeneration with computational fluid dynamics
10.1016/j.powtec.2016.10.058 · 2017 · External reference
Computational investigation of a turbulent fluidized-bed FCC regenerator
10.1021/ie3013659 · 2013 · External reference
Hydrodynamic modeling of an industrial turbulent fluidized bed reactor with FCC particles
10.1016/j.powtec.2016.04.048 · 2016 · External reference
CFD modeling investigation of oxy-fuel combustion application in an industrial-scale FCC regenerator
10.1016/j.joei.2024.101796 · 2024 · External reference
Fast prediction of temperature and chemical species distributions in pulverized coal boiler using POD reduced-order modeling for CFD
10.1016/j.energy.2023.127663 · 2023 · External reference
Three multi-fidelity data hybrid-driven strategies for multi-objective combustion optimization in coal-fired boilers using POD and XGBoost
10.1016/j.energy.2025.138442 · 2025 · External reference
A digital twin of coal-fired boiler for physical fields prediction using flow-field-informed POD-XGBoost reduced-order modeling
10.1016/j.energy.2025.136732 · 2025 · External reference
A mechanism-data hybrid-driven approach to reduce NOx emission and slagging for coal-fired boiler using POD reduced order modeling and XGBoost
10.1016/j.applthermaleng.2025.126709 · 2025 · External reference
A machine learning model for quickly predicting the inner states of ironmaking blast furnaces
10.1016/j.powtec.2024.120137 · 2024 · External reference
Surrogate modeling-based multi-objective optimization for the integrated distillation processes
10.1016/j.cep.2020.108224 · 2021 · External reference
Reduced-order modeling via convolutional autoencoder for emulating combustion of hydrogen/methane fuel blends
10.1016/j.combustflame.2025.113981 · 2025 · External reference
Reconstruction and fast prediction of 3D heat and mass transfer based on a variational autoencoder
10.1016/j.icheatmasstransfer.2023.107112 · 2023 · External reference
High-fidelity reconstruction of 3D temperature fields using attention-augmented CNN autoencoders with optimized latent space
10.1109/access.2024.3512873 · 2024 · External reference
CFD data-driven CNN-LSTM for fast prediction of instantaneous flow characteristics in hydrocyclones
10.1016/j.mineng.2025.109814 · 2026 · External reference
Numerical study of a fluid catalytic cracking regenerator hydrodynamics
10.1016/j.powtec.2016.09.082 · 2017 · External reference
.Numerical simulation and optimization of an industrial fluid catalytic cracking regenerator
2017 · External reference
Projection-based model-order reduction via graph autoencoders suited for unstructured meshes
10.1017/dce.2025.10030 · 2025 · External reference
Applying convolutional Neural networks to data on unstructured meshes with space-filling curves
10.1016/j.neunet.2024.106198 · 2024 · External reference
Masked smoothing using separable kernels for CT perfusion images
10.1186/1471-2342-14-28 · 2014 · External reference
Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics
10.1016/j.cma.2020.113379 · 2020 · External reference
A convolutional auto-encoder regression for parametric modeling of mixed convection in concentric annulus
10.1016/j.ijthermalsci.2023.108293 · 2023 · External reference
Effects of sample size and network depth on a deep learning approach to species distribution modeling
10.1016/j.ecoinf.2020.101137 · 2020 · External reference
An ECG-based blood pressure estimation using U-net auto-encoder and random forest regressor
2023 · External reference
Unresolved reference
External reference
A review of modelling of the FCC unit—part II: the regenerator
10.3390/en15010388 · 2022 · External reference
