Publications, Activities, and Awards
- Adsorbent Agnostic Machine-Assisted Adsorption Process Learning and Emulation (MAPLE) Framework
- Artificial Neural Network-Based Surrogate Models for Rapid Simulation, Optimization of Pressure Swing Adsorption
- 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
- Experimental validation of an adsorbent-agnostic artificial neural network (ANN ) framework for the design and optimization of cyclic adsorption processes
- 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?
- Hybrid-AI Based Modelling of Pressure Swing Adsorption
- Introduction to Machine Learning: A Practical Workshop
- Machine Learning and Models: How we find optimal materials for Solar and CCS technologies
- Machine Learning-Based Multiobjective Optimization of Pressure Swing Adsorption
- 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
- Real-Time Production Optimization of Steam-Assisted-Gravity-Drainage Reservoirs Using Adaptive and Gain-Scheduled Model-Predictive Control: An Application to a Field Model
- Reduced-order modelling of Pressure-swing adsorption processes for Pre-combustion CO2 capture