Operability-economics trade-offs in adsorption-based CO$_2$ capture process

Abstract

Low-carbon dispatchable power underpins a sustainable energy system, providing load balancing complementing wide-scale deployment of intermittent renewable power. In this new context, fossil fuel-fired power plants must be coupled with a post-combustion carbon capture (PCC) process capable of highly transient operation. To tackle design and operational challenges simultaneously, we have developed a computational framework that integrates process design with techno-economic assessment. The backbone of this is a high-fidelity PCC mathematical model of a pressure-vacuum swing adsorption process. We demonstrate that the cost-optimal design has limited process flexibility, challenging reactiveness to disturbances, such as those in the flue gas feed conditions. The results illustrate that flexibility can be introduced by relaxing the CO$_2$ recovery constraint on the operation, albeit at the expense of the capture efficiency of the process. We discover that adsorption-based processes can accommodate for significant flexibility and improved performance with respect to the operational constraints on CO$_2$ recovery and purity. The results herein demonstrate a trade-off between process economics and process operability, which must be effectively rationalised to integrate CO$_2$ capture units in the design of low-carbon energy systems.

Figures (6)

• Figure 1: Low-carbon energy system. A schematic representation of a flexible low-carbon energy system with intermittent renewables (non-dispatchable power) and low-carbon dispatchable power. Power generation by fossil fuels is expected to provide a variable power output to match supply and demand of energy at any given time. Downstream post-combustion capture (PCC) is subject to operation under variable flue gas feed flowrate and CO$_2$ composition.
• Figure 2: Pareto front comparison between formal optimization (NSGA-II) and Sobol sampling.a Unconstrained purity-recovery Pareto front. b Constrained energy-productivity Pareto front. In both a and b, the solid lines correspond to the NSGA-II Pareto fronts, while the scattered points correspond to Sobol sampling. The cost-optimal design of optimization using NSGA-II is highlighted as a red square in each Pareto plane, and that of the Sobol sampling is a blue circle. The corresponding design decisions and KPI values are summarised in Table \ref{['Table:CostOptimal']}.
• Figure 3: Identification of the design space (DSp) and quantification of the acceptable operating region (AOR). The design space (DSp) representing the region of the KSp for which combinations of the design decisions satisfy CO$_2$ purity $\geq$ 95% and recovery $\geq$ 90% is shown as the shaded grey region. Points in the quasi-random sample which satisfy the constraints are shown in orange (Sat). Points which do not satisfy the constraints have been excluded for clarity. The nominal operating point (NOP) and corresponding acceptable operating region (AOR) for the cost-optimal design (blue) and the maximum flexibility design (green) are provided. The circle corresponds to the NOP and the box corresponds to the AOR.
• Figure 4: Design spaces identification with progressive relaxation on the recovery constraint. a Design space with the original recovery constraint ($\ge$ 90%). b - d Design space with relaxed recovery constraint of 89%, 88%, and 85%, respectively. The design space for each case is shown as the shaded grey region. The nominal operating point and corresponding acceptable operating region for each case are shown in blue. The quasi-random sampled points satisfying the constraints of each case are shown. The colour of each point corresponds to the capture cost of each operating point.
• Figure 5: Flexibility metrics comparison between three relaxed cost-optimal design casesa Acceptable operating region (AOR) volume. b Multivariate proven acceptable ranges (MPARs) of the design decisions. c Distribution of all monitored KPIs within the identified acceptable operating region. The dashed line represents the mean value of the KPIs, while the blue dash dot line shows the KPI value at the cost-optimal point.