Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies.

Process modeling has become a fundamental tool to guide experimental work. Unfortunately, process models based on first principles can be expensive to develop and evaluate, and hard to use, particularly when convergence issues arise. This work proves that Bayesian symbolic learning can be applied to derive simple closed-form expressions from rigorous process simulations, streamlining the process modeling task and making process models more accessible to experimental groups. Compared to conventional surrogate models, our approach provides analytical expressions that are easier to communicate and manipulate algebraically to get insights into the process. We apply this method to synthetic data obtained from two basic CO2 capture processes simulated in Aspen HYSYS, identifying accurate simplified interpretable equations for key variables dictating the process economic and environmental performance. We then use these expressions to analyze the process variables' elasticities and benchmark an emerging CO2 capture process against the business as usual technology.

Navigating within the Safe Operating Space with Carbon Capture On-Board.

Despite the global pandemic that recently affected human and cargo transportation, the emissions of the maritime sector are projected to keep growing steadily. The International Maritime Organization focused on boosting the fleets' efficiency to improve their environmental performance, while more sustainable fuels are currently under investigation. Here, we assess the economic, technical, and environmental feasibility of an interim solution for low-carbon shipping using state-of-the-art CO2 capture technology, namely, chemical absorption, on-board cargo ships. We compute the carbon footprint of this alternative and perform an absolute sustainability study based on seven planetary boundaries. Our results show that the capture on-board scenario can achieve 94% efficiency on the net CO2 emissions at 85 $/tCO2 while substantially reducing impacts on core planetary boundaries (relative to the business as usual) and outperforming a direct air capture scenario in global warming and all planetary boundaries, except for the nitrogen flow. Hence, capture on-board seems an appealing solution to decarbonize shipping in the short term while alternative carbon-free fuels and related infrastructure are developed and deployed.