Superstructure-Based Optimal Synthesis of Pressure Swing Adsorption Cycles for Precombustion CO2 Capture

Abstract

Pressure/vacuum swing adsorption (PSA/VSA) technology has been widely applied for H2 production from the effluent streams of a shift converter, which predominantly comprises H2 and CO2 with other trace components. It also offers significant advantages for precombustion CO2 capture in terms of performance, energy requirements, and operating costs since the shifted synthesis gas (syngas) is available for separation at a high pressure with a high CO2 concentration. Most commercial PSA cycles have been developed to recover H2 at very high purity and do not focus on enriching the strongly adsorbed CO2. Thus, a major limitation exists with the use of these conventional PSA cycles for high purity CO2 capture. Furthermore, complex dynamic behavior of PSA processes together with the numerical difficulties of the model governed by partial differential and algebraic equations (PDAEs) makes the evaluation and assessment of different operating steps and cycle configurations difficult and time-consuming. Therefore, a systematic methodology is essential to design and optimize PSA cycles to recover both H2 and CO2 at a high purity. Recent advances in large-scale optimization strategies for process synthesis have enabled us to address this issue with the help of a systematic optimization-based formulation. In particular, we present a superstructure-based approach to simultaneously determine optimal cycle configurations and design parameters for PSA units. The superstructure is capable to predict a rich set of different PSA operating steps, which are accomplished by manipulating the bed connections with the help of time dependent control variables. An optimal sequence of operating steps is achieved through the formulation of an optimal control problem with the PDAEs of the PSA system. Numerical results for case-studies related to precombustion CO2 capture from a shifted syngas feed mixture having hydrogen and carbon dioxide are presented. In particular, optimal PSA cycles are synthesized which maximize CO2 recovery or minimize overall power consumption. The results show the potential of the superstructure to predict PSA cycles with purities as high as 99% for H2 and 96% for CO2. Moreover, these cycles can recover more than 92% of CO2 with a power consumption as low as 46.8 kW h/tonne CO2 captured. The approach presented is therefore quite useful for evaluating the suitability of different operating strategies for PSA processes.