Journal
ENERGY
Volume 270, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2023.126901
Keywords
Post-combustionCO2 capture; Artificial neural network; Process design and optimization; Deep eutectic solvents
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In this study, an inverse design method was proposed to reduce the energy requirement of post-combustion CO2 capture. Four physical solvents were modeled and optimized using artificial neural network-simulated annealing models. The optimization results highlighted the importance of absorbent properties for energy savings. A new strategy was proposed for adjusting the physical properties of deep eutectic solvents, and a promising CO2 absorbent was identified through experimental tests and computer modeling.
In this work, an inverse design method for absorbent development was proposed to reduce the energy requirement of post-combustion CO2 capture (PCC) with 4 steps. 1. Four physical solvents were modeled for PCC and the artificial neural network (ANN)-simulated annealing (SA) models were developed for fast and accurate multi-objective optimization. 2. The optimization results show that the physical properties of absorbents are important for energy savings. Further analysis on liquid pump energy demand and optimal operating parameters of each absorbent identified absorbents with low viscosity, small average molecular weight, and high CO2 capture ability from flue gas are desirable for PCC. 3. A new strategy was proposed to adjust the physical properties of deep eutectic solvents (DESs) and overcome the disadvantages of aqueous DESs-low thermal stability-by using two ingredients as hydrogen bond donors. 4. Through experimental tests and computer modeling, a DES (TEAC/2Gly/PDO) consisting of tetraethylammonium chloride, glycerol, and 1,3-propanediol in a mole ratio of 1:2:1 was identified as a promising CO2 absorbent with energy demand of 1.06 MJ/kg CO2.
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