Criteria/correlations proposed in the literature to predict the performance of an adsorbent in a separation process fail in three ways for carbon dioxide (CO2) capture and concentration (CCC) from flue gas: (i) they cannot confirm if the stringent purity–recovery requirements will be met, (ii) the indices rank the adsorbents for a fixed set of operating conditions and not for the best performance for each adsorbent while satisfying the purity–recovery constraints, and (iii) the performance indicator for which the adsorbents are ranked is not explicitly stated, and certainly one index cannot apply for different performance indicators. In this study, a two-step method has been developed for rapid screening of the adsorbents for postcombustion CCC as a function of five easily quantifiable equilibrium isotherm characteristics of the CO2 isotherm, namely, its binary mixture selectivity over N2, equilibrium loading, local slope and nonlinearity at the feed concentration, and Henry’s constant. First, a study has been conducted using a central composite design of experiments for determining the effects of these isotherm characteristics. The performance of a four-step vacuum swing adsorption (VSA) process with light product pressurization has been optimized for each combination of the aforementioned CO2 isotherm characteristics, representing a possible adsorbent, with the objective to minimize energy consumption and maximize productivity while satisfying the purity–recovery constraints. Among the feasible combinations of isotherm characteristics, some were unable to meet the purity–recovery constraints. Next, a neural-network-based model has been proposed to predict whether an adsorbent can deliver CO2 at 95% purity and 90% recovery. Finally, for adsorbents that pass this first test, meta-models have been developed to predict the minimum energy consumption and maximum productivity of the process. The proposed screening method has been validated with a large number of adsorbents considered in recent published studies. In the process, we have identified several adsorbents that promise a much superior performance over that of 13X zeolite, the most studied adsorbent for postcombustion CCC.
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Khurana et al. (2016) studied this question.
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