Publications, Activities, and Awards
- Adsorbent Agnostic Machine-Assisted Adsorption Process Learning and Emulation (MAPLE) Framework
- Analysis of a Batch Adsorber Analogue for Rapid Screening of Adsorbents for Postcombustion CO2 Capture
- Artificial Neural Network-Based Surrogate Models for Rapid Simulation, Optimization of Pressure Swing Adsorption
- Batch Adsorber Analogue Model Based Screening of Large Adsorbent Databases for Post-Combustion CO2 Capture
- Bridging molecular properties to systems level indicators for adsorbent based post-combustion carbon capture using machine learning
- Can a computer "learn" nonlinear chromatography?: Physics-based deep neural networks for simulation and optimization of chromatographic processes
- Cost Limits of Pressure-Vacuum Swing Adsorption for Post-Combustion CO2 Capture
- Evaluation of diamine-appended metal-organic frameworks for post-combustion CO2 capture by vacuum swing adsorption
- Experimental validation of an adsorbent-agnostic artificial neural network (ANN ) framework for the design and optimization of cyclic adsorption processes
- Experimental validation of multi-objective optimization techniques for design of vacuum swing adsorption processes
- Experimental Validation of PSA Optimization Techniques
- Experimentally validated machine learning frameworks for accelerated prediction of cyclic steady state and optimization of pressure swing adsorption processes
- Generalized, Adsorbent-Agnostic, Artificial Neural Network Framework for Rapid Simulation, Optimization, and Adsorbent Screening of Adsorption Processes
- How Can (or Why Should) Process Engineering Aid the Screening and Discovery of Solid Sorbents for CO2 Capture?
- How much can novel solid sorbents reduce the cost of post-combustion [formula omitted] capture? A techno-economic investigation on the cost limits of pressure–vacuum swing adsorption
- Hybrid-AI Based Modelling of Pressure Swing Adsorption
- Improving the performance of vacuum swing adsorption based CO2 capture under reduced recovery requirements
- Large-scale Integrated Molecular Simulation and Process Optimization Screening of Metal Organic Frameworks for PostCombustion CO2 Capture
- Machine Learning and Models: How we find optimal materials for Solar and CCS technologies
- Machine Learning-Based Multiobjective Optimization of Pressure Swing Adsorption
- Measurement of competitive CO2 and H2O adsorption on zeolite 13X for post-combustion CO2 capture
- Physics-Based Neural Networks for Simulation and Synthesis of Cyclic Adsorption Processes
- Practically Achievable Process Performance Limits for Pressure-Vacuum Swing Adsorption Based Post-Combustion CO2 Capture
- Practically Achievable Process Performance Limits for Pressure-Vacuum Swing Adsorption-Based Postcombustion CO2 Capture
- Prediction of MOF performance in Vacuum-Swing Adsorption systems for post-combustion CO2 capture based on integrated molecular simulation, process optimizations, and machine learning models
- Process Optimization-Based Screening of Zeolites for Post-Combustion CO2 Capture by Vacuum Swing Adsorption
- Reduced-order modelling of Pressure-swing adsorption processes for Pre-combustion CO2 capture
- Techno-economic assessment of optimised vacuum swing adsorption for post-combustion CO2 capture from steam-methane reformer flue gas
- Technoeconomic Assessment of Optimized Adsorption Processes for Post-Combustion CO2 Capture in Hydrogen Plants