Model Adaptation and Optimization for the Evaluation and Investigation of Novel Amine Blends in a Pilot-plant Scale CO₂ Capture Process under Industrial Conditions

Abstract

Carbon capture processes are highly energy intensive and the main driving force during the process design is the necessity to reduce the energy consumption for the solvent regeneration. The energy efficiency of absorption desorption processes is driven by the plant design and operation conditions, but also to a large extent by the choice of the scrubbing liquid (Wang et al., 2011). Absorbent screening for CO2 capture is time-consuming and costly. Apart from the energy efficiency, aspects such as loading capacity and robustness towards industrial impurities and disturbances have to be investigated before designing a large-scale plant and optimally operating it. In this contribution, a systematic approach is presented to carry out evaluation tests for a novel solvent in an industrial pilot-plant and at the same time to determine an optimal operation point with maximum energy efficiency. The three-step approach is based on the assumption that for a novel absorbent little to no thermodynamic data is available. Hence, the investigation is solely based on simulation data from similar solvents and experimental data on the novel one. Monoethanolamine (MEA) still is widely used as reference absorbent for removing CO2. Consequently, simulation data and the properties of MEA are taken as a baseline for the general performance and behaviour of amine-based absorbents. As a first step, data from rigorous simulations is used to develop a surrogate model describing the general behaviour of a carbon capture process for MEA. Subsequently, pilot-plant-scale experiments are carried out to investigate the application of MEA in practice. Secondly, the surrogate model is then updated to account for the plant characteristics as given by the experimental data for MEA. Finally, by means of the MEA-based data-driven model, the new solvent is experimentally investigated. By successive approaches the surrogate model’s maximum in energy efficiency is identified and repeatedly updated for the novel solvent’s experimental behaviour. In terms of the energy efficiency and based on this workflow the performance of the novel solvent is compared with MEA.