CONICET | Buscador de Institutos y Recursos Humanos

This paper applies mathematical programming and superstructure-based optimization approaches to design multi-stage membrane systems for CO2 capture, with the aim to systematically determine the optimal number of membrane stages, membrane areas, power requirements, location of recycle streams, and operating conditions that satisfy desired CO2 recovery−purity value pairs ranging from 90 to 98% at minimum total cost. A superstructure that embeds several candidate configurations is represented as a NLP model which is used to investigate how these ranges of target values affect the optimal number of stages, membrane areas, operating conditions, and total cost.The results indicate that the optimal number of stages depend strongly on the desired CO2 purity. For CO2 purity ranging from 90 to 93%, the optimal configuration involves two stages and one recycle stream; from 94 to 96%, the optimal configuration involves three stages and two recycle streams; and finally, from 97 and 98%, the optimal configuration involves all the four stages proposed in the superstructure keeping the two recycle streams.For the same design specifications, the obtained process configurations compete not only with reference cases of multi-stage membrane systems in terms of total cost, but also with absorption-based CO2 capture processes in terms of required energy.