Computational-cost-efficient surrogate model of vacuum pressure swing adsorption for CO separation process optimization

Abstract

We present a computational-cost-efficient surrogate model to optimize the separation of carbon monoxide (CO) from steel-mill off-gas when adsorbent material and the vacuum pressure swing adsorption process are used. We use a mathematical model to generate the full-factorial design data points, then use them to train, validate, and test the surrogate model, which we then use to suggest the best prediction algorithm for CO separation. The extra-tree regressor was selected by considering the test R2 score, and purity and recovery were well predicted (R2 = 0.999 for both). The optimized condition was 3.94 bar for the adsorption, and 0.05 bar for the desorption, which gave 99.99% purity and 71.3% recovery. Analysis of cost sensitivity of electricity price and CO selling price showed how the optimal conditions were moved from the base case; these results were consistent with the phenomenological knowledge which favors a large difference in pressure between the adsorption and desorption steps. Also, Pareto-front solutions of the productivity and purity implied that compared to a previous study, the productivity can be increased by 9.4% when the target purity is 90%, and by 7.5% when it is 99%.