Post Combustion Capture of CO2 using Solid Sorbents

Most fossil-fuel based power plants use post-combustion technology. Addressing the CO2 capture problem in these power plants is critical if a dent in CO2 emissions is to be expected. Post-combustion CO2 capture using solid sorbents has been identified as a powerful alternative for solvent based processes. Some accessible novel solid sorbents have the potential to further reduce parasitic energy.


However, the design, optimization, and integration of processes involving solid sorbents are poorly understood. Current models are computationally expensive making their use in process and systems optimization challenging. Further, the solution to the problem of finding optimal design and integration of these processes into existing plants remains elusive.


We propose to attack this problem by developing meta-models and surrogate models using machine-learning techniques, that significantly reduce the computational efforts; validating them against detailed models and experiments, and finally integrating them into systems-level models that provide the optimal way to integrate CO2 capture processes into power plants. This approach will enable further reduction in parasitic energy and allow for the scale-up and implementation of solid-based capture processes into power plants.

Adsorbent Agnostic Machine-Assisted Adsorption Process Learning and Emulation (MAPLE) Framework

Arvind Rajendran, Vinay Prasad, Kasturi Nagesh Pai

Conference Proceedings

Analysis of a Batch Adsorber Analogue for Rapid Screening of Adsorbents for Postcombustion CO2 Capture

Arvind Rajendran

Peer-Reviewed Journal Article

Artificial Neural Network-Based Surrogate Models for Rapid Simulation, Optimization of Pressure Swing Adsorption

Arvind Rajendran, Vinay Prasad, Kasturi Nagesh Pai

Conference/Symposium/Workshop Contribution

Batch Adsorber Analogue Model Based Screening of Large Adsorbent Databases for Post-Combustion CO2 Capture

Arvind Rajendran, Vishal Subramanian Balashankar

Conference Proceedings

Bridging molecular properties to systems level indicators for adsorbent based post-combustion carbon capture using machine learning

Arvind Rajendran, Vinay Prasad, Zukui Li, Kasturi Nagesh Pai, Gokul Sai Subraveti

Conference/Symposium/Workshop Contribution

Can a computer "learn" nonlinear chromatography?: Physics-based deep neural networks for simulation and optimization of chromatographic processes

Arvind Rajendran, Vinay Prasad, Zukui Li, Gokul Sai Subraveti

Peer-Reviewed Journal Article

Cost Limits of Pressure-Vacuum Swing Adsorption for Post-Combustion CO2 Capture

Arvind Rajendran, Gokul Sai Subraveti

Conference Proceedings

Evaluation of diamine-appended metal-organic frameworks for post-combustion CO2 capture by vacuum swing adsorption

Arvind Rajendran, Kasturi Nagesh Pai

Peer-Reviewed Journal Article

Experimental Validation of PSA Optimization Techniques

Arvind Rajendran

Conference Proceedings

Experimental validation of an adsorbent-agnostic artificial neural network (ANN ) framework for the design and optimization of cyclic adsorption processes

Arvind Rajendran, Vinay Prasad, Kasturi Nagesh Pai

Peer-Reviewed Journal Article

Experimental validation of multi-objective optimization techniques for design of vacuum swing adsorption processes

Arvind Rajendran

Peer-Reviewed Journal Article

Experimentally validated machine learning frameworks for accelerated prediction of cyclic steady state and optimization of pressure swing adsorption processes

Arvind Rajendran, Vinay Prasad, Kasturi Nagesh Pai

Peer-Reviewed Journal Article

Generalized, Adsorbent-Agnostic, Artificial Neural Network Framework for Rapid Simulation, Optimization, and Adsorbent Screening of Adsorption Processes

Arvind Rajendran, Vinay Prasad, Kasturi Nagesh Pai

Peer-Reviewed Journal Article

How Can (or Why Should) Process Engineering Aid the Screening and Discovery of Solid Sorbents for CO2 Capture?

Arvind Rajendran, Vinay Prasad, Zukui Li, Kasturi Nagesh Pai, Gokul Sai Subraveti

Peer-Reviewed Journal Article

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

Arvind Rajendran, Gokul Sai Subraveti

Peer-Reviewed Journal Article

Hybrid-AI Based Modelling of Pressure Swing Adsorption

Arvind Rajendran, Vinay Prasad, Zukui Li, Gokul Sai Subraveti

Conference Proceedings

Improving the performance of vacuum swing adsorption based CO2 capture under reduced recovery requirements

Arvind Rajendran, Kasturi Nagesh Pai

Peer-Reviewed Journal Article

Large-scale Integrated Molecular Simulation and Process Optimization Screening of Metal Organic Frameworks for PostCombustion CO2 Capture

Arvind Rajendran, Kasturi Nagesh Pai, Gokul Sai Subraveti

Conference Proceedings

Machine Learning and Models: How we find optimal materials for Solar and CCS technologies

Arthur Mar, Arvind Rajendran, Vinay Prasad, Alex Gzyl, Anton Oliynyk, Jan Poehls, Kasturi Nagesh Pai, Gokul Sai Subraveti

Conference/Symposium/Workshop Contribution

Machine Learning-Based Multiobjective Optimization of Pressure Swing Adsorption

Arvind Rajendran, Vinay Prasad, Zukui Li, Gokul Sai Subraveti

Peer-Reviewed Journal Article

Machine learning-based design and techno-economic assessments of adsorption processes for CO2 capture

Gokul Sai Subraveti

Doctoral Thesis/Dissertation

Measurement of competitive CO2 and H2O adsorption on zeolite 13X for post-combustion CO2 capture

Arvind Rajendran, Nicholas Wilkins

Peer-Reviewed Journal Article

Physics-Based Neural Networks for Simulation and Synthesis of Cyclic Adsorption Processes

Arvind Rajendran, Vinay Prasad, Zukui Li, Gokul Sai Subraveti

Peer-Reviewed Journal Article

Practically Achievable Process Performance Limits for Pressure-Vacuum Swing Adsorption Based Post-Combustion CO2 Capture

Arvind Rajendran, Vinay Prasad, Kasturi Nagesh Pai

Conference Proceedings

Practically Achievable Process Performance Limits for Pressure-Vacuum Swing Adsorption-Based Postcombustion CO2 Capture

Arvind Rajendran, Vinay Prasad, Kasturi Nagesh Pai

Peer-Reviewed Journal Article

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

Arvind Rajendran, Kasturi Nagesh Pai, Gokul Sai Subraveti

Peer-Reviewed Journal Article

Process Optimization-Based Screening of Zeolites for Post-Combustion CO2 Capture by Vacuum Swing Adsorption

Arvind Rajendran, Vishal Subramanian Balashankar

Peer-Reviewed Journal Article

Reduced-order modelling of Pressure-swing adsorption processes for Pre-combustion CO2 capture

Arvind Rajendran, Vinay Prasad, Zukui Li, Kasturi Nagesh Pai, Gokul Sai Subraveti

Conference/Symposium/Workshop Contribution

Techno-economic assessment of optimised vacuum swing adsorption for post-combustion CO2 capture from steam-methane reformer flue gas

Arvind Rajendran, Gokul Sai Subraveti

Peer-Reviewed Journal Article

Technoeconomic Assessment of Optimized Adsorption Processes for Post-Combustion CO2 Capture in Hydrogen Plants

Arvind Rajendran, Gokul Sai Subraveti

Conference/Symposium/Workshop Contribution

Unified Machine learning based design of adsorption separation processes

Kasturi Nagesh Pai

Doctoral Thesis/Dissertation