| 1 |
Hybrid process using cryogenic and pressure swing adsorption process for CO2 capture and extra H2 production from a tail gas in an SMR plant |
Energy Conver. & Manag. |
2025 |
IF: 10.4 / Top 4.97% |
| 2 |
Industrial-scale 12-layered-bed vacuum pressure swing adsorption for fuel cell-grade H2 production from carbon-captured steam methane reforming syngas |
Chem. Eng. J. |
2024 |
IF: 15.1 / Top 3.2% |
| 3 |
Statistical Mechanic and Machine Learning Approach for competitive adsorption of CO2/CH4 on coals and shales for CO2-Enhanced Methane Recovery |
Chem. Eng. J. |
2024 |
IF: 15.1 / Top 3.2% |
| 4 |
Advanced process integration and machine learning-based optimization to enhance techno-economic-environmental performance of CO2 capture and conversion to methanol |
Energy |
2024 |
IF: 8.9 / Top 4.0% |
| 5 |
Blended-amine CO2 capture process without stripper for high-pressure syngas |
Chem. Eng. J.. |
2024 |
IF: 15.1 / Top 3.2% |
| 6 |
Comparative Performance and Machine Learning-based Optimization of TSA Configurations for NH3 Removal from NH3 Cracking Gas |
Chem. Eng. J. |
2023 |
IF: 15.1 / Top 3.2% |
| 7 |
Performance and ANN-based Optimization of a Novel Process for Wet CO2 to Methanol using a Catalytic Fluidized Bed Reactor integrated with Separators |
Fuel |
2023 |
IF:8.0 / Top 12.94% |
| 8 |
Dynamic Model and Deep Neural Network-based Surrogate Model to Predict Dynamic Behaviors and Steady-state Performance of Solid Propellant Combustion |
Combustion and Flame |
2023 |
IF: 5.7 / Top 9.12% |
| 9 |
Techno-economic analysis and optimization of a CO2 absorption process with a solvent looping system at the absorber using an MDEA/PZ blended solvent for steam methane reforming |
Chem. Eng. J. |
2023 |
IF: 15.1 / Top 3.2% |
| 10 |
Design guideline for CO2 to methanol conversion process supported by generic model of various bed reactors |
Energy Conver. & Manag. |
2022 |
IF: 10.4 / Top 4.97% |
| *11 |
Facile and Accurate Calculation of the Density of Amino Acid Salt Solutions: A Simple and General Correlation vs Artificial Neural Networks |
Energy and Fuels |
2022 |
IF: 4.6 / Top 31.12% |
| 12 |
re-combustion CO2 capture using amine-based absorption process for blue H2 production from steam methane reformer |
Energy Conver. & Manag. |
2022 |
IF: 10.4 / Top 4.97% |
| *13 |
Dynamic modeling and machine learning of commercial-scale simulated moving bed chromatography for application to multi-component normal paraffin separation |
Separ. Purif. Tech. |
2022 |
IF: 8.6 / Top 8.2% |
| *14 |
Prediction of CO2 capture capability of 0.5 MW MEA demo plant using three different deep learning pipelines |
Fuel |
2022 |
IF:8.0 / Top 12.94% |
| 15 |
Sensitivity analysis and artificial neural network-based optimization for low-carbon H2 production via a sorption-enhanced steam methane reforming (SESMR) process integrated with separation process |
Inter’l J. of Hydrogen Energy |
2022 |
IF: 7.2 / Top 25.2% |
| 16 |
Actor-critic reinforcement learning to estimate the optimal operating conditions of the hydrocracking process |
Computers & Chem. Eng. |
2021 |
IF: 4.3 / Top 37.7% |
| *17 |
Artificial neural network modelling for solubility of carbon dioxide in various aqueous solutions from pure water to brine |
J. of CO2 Utilization |
2021 |
IF: 8.3 / Top 17.5% |
| 18 |
Deep reinforcement learning optimization framework for a power generation plant considering performance and environmental issues |
J. of Cleaner Production |
2021 |
IF: 11.1 / Top 13.6% |
| *19 |
Prediction of SOx-NOx Emission from a Coal-Fired CFB Power Plant with Machine Learning: Plant Data Learned by Deep Neural Network and Least Square Support Vector Machine |
J. of Cleaner Production |
2020 |
IF: 11.1 / Top 13.6% |
| 20 |
Dynamic Model-based Artificial Neural Network for H2 Recovery and CO2 Capture from Hydrogen Tail Gas |
Applied Energy |
2020 |
IF: 11.2 / Top 6.1% |
| *21 |
Prediction of CO2 solubility in multicomponent electrolyte solutions up to 709 bar: Analogical bridge between hydrophobic solvation and adsorption model |
Chem. Eng. J. |
2020 |
IF: 15.1 / Top 3.2% |
| 22 |
Combined approach using mathematical modelling and artificial neural network for chemical industries: Steam methane reformer |
Applied Energy |
2019 |
IF: 11.2 / Top 6.1% |
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