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.

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 Subraveti

Conference/Workshop

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 Subraveti

Conference/Workshop

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

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

Conference/Workshop

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

Arvind Rajendran

Scholarly Refereed Journal

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

Arvind Rajendran, Kasturi Nagesh Pai

Scholarly Refereed Journal

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

Arvind Rajendran, Kasturi Nagesh Pai

Scholarly Refereed Journal

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

Arvind Rajendran

Scholarly Refereed Journal

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

Scholarly Refereed Journal

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

Arvind Rajendran, Kasturi Nagesh Pai

Scholarly Refereed Journal

Machine Learning-Based Multiobjective Optimization of Pressure Swing Adsorption

Arvind Rajendran, Vinay Prasad, Zukui Li

Scholarly Refereed Journal

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

Arvind Rajendran

Scholarly Refereed Journal

Measurement of competitive CO2 and N2 adsorption on Zeolite 13X for postcombustion CO2 capture

Arvind Rajendran

Scholarly Refereed Journal

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 Subraveti

Scholarly Refereed Journal

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

Arvind Rajendran, Vishal Subramanian Balashankar

Scholarly Refereed Journal