Input–Output Surrogate Models for Efficient Economic Evaluation of Amine Scrubbing CO₂ Capture Processes

Abstract

Carbon capture followed by utilization or storage enables carbon-intensive sectors to abate their CO2 emissions. However, complexity and nonlinearity of the capture processes hinder the incorporation of their first-principles models into analyses and optimizations of overall carbon management strategies. Accordingly, it is desirable to have a systematic method to develop a computationally less demanding surrogate model, which can replace the rigorous CO2 capture process model, for use in a high-level decision-making environment. For such purposes, the surrogate model should be able to provide multiple information needed to connect to subsequent processing steps under varying source stream conditions. This research addresses the development of surrogate models for CO2 capture processes that enjoy significantly lowered complexity while preserving the key information. A surrogate model can be constructed by fitting the input–output data generated by process simulation and optimization with the rigorous model. Following the proposed method, surrogate models for the amine-based CO2 capture processes with two representative types of amines, monoethanolamine (MEA) and piperazine (PZ), are constructed and validated. The constructed surrogate models predict the specific steam consumption rate and total equipment purchase cost based on the input information of desired capture rate and CO2 source stream condition. The predicted information is shown to agree well with the simulation result of the rigorous first-principles model. This surrogate modeling approach can be applied to compare different capture technologies in the context of analyzing and synthesizing a larger CCUS processing network.

Process modeling, techno-economic assessment, and life cycle assessment of the electrochemical reduction of CO2: a review

“…The second and more complex type includes mass transfer effects and the influence of design and operational variables on the selectivity (Orella et al, 2019). Surrogate models have been used to bridge the gap between complex mechanistic models and large-scale assessments in the modeling of carbon dioxide capture (Chung and Lee, 2020;Zhang and Sahinidis, 2013). Similar approaches can be used to model the effect of the main variables of ECO2R on its large-scale design and operational performance.…”

Accelerating net zero from the perspective of optimizing a carbon capture and utilization system

“…There are various pathways for either capture or utilization. Extensive studies have been done to optimize individual subsystems of CCU, e.g., pressure swing adsorption (PSA), [19][20][21][22] and monoethanolamine (MEA) for CO 2 capture, 23,24 methanol synthesis 11,15,25 or Fischer-Tropsch 26,27 for subsequent utilization. However, the performance of these sub-systems depends on each other, and thus individual optimal solutions cannot simultaneously co-exist.…”

Optimisation of carbon capture from flue gas from a Waste-to-Energy plant using surrogate modelling and global optimisation

“…Reducing energy requirement and CAPEX has been the subject of numerous studies using rigorous process simulation models, see e.g. [7][8][9][10][11][12][13][14]. Optimisation of CO 2 capture using amine absorption utilising process simulations is a complex task.…”

“…Lee et al [9] used an approach where an Aspen Plus simulation model was coupled to a multi-objective optimisation algorithm (NSGA-II), but also noted that a significant fraction of simulation evaluation failed to converge or were otherwise infeasible. Chung and Lee [14] developed a reduced order model fitted to simulated input-output data and utilised it for minimising the CO 2 avoidance cost.…”