Mathematical modeling and economic optimization of a piperazine-based post-combustion carbon capture process

Given the urgent need to mitigate increasing CO${}_2$ emissions and alleviate the climate crisis, amine-based post-combustion capture (PCC) processes have emerged as a prominent method to reduce the emissions from industrial point sources. While many technological advancements have been introduced for such processes, leading to decreased energy requirements for capture, there are still only a few commercial installations because of their high costs. Therefore, these processes can benefit from process optimization to enhance their economic viability. This work presents a new open-source, rate-based, equation-oriented model of a novel PCC process that uses piperazine as the amine solvent. The model was implemented in Python, in accordance with the Pyomo-based IDAES modeling and optimization framework. The proposed nonlinear model can be used for both simulation and optimization. To ensure its robust convergence, we further devise a rigorous, multi-level cascade initialization scheme, whose principles can further be applied towards the initialization of similar process models. The model was validated with published pilot plant data and then optimized for pilot and commercial scales with an economic objective that considers both capital and operational costs. Results show that process optimization can indeed improve the economics of this technology, leading to 15.6% yearly savings at the pilot scale compared to the baseline case considered in the study. Additional parametric analyses were performed to understand how the flue gas flowrate and CO${}_2$ concentration, as well as the target capture rate, affects the cost of capture.