Techno-economic Assessment of Optimised Vacuum Swing Adsorption for Post-Combustion CO2 capture from Steam-Methane Reformer Flue Gas

This study focuses on the techno-economic assessment integrated with detailed optimisation of a four step vacuum swing adsorption (VSA) process for post-combustion CO2 capture and storage (CCS) from steam-methane reformer dried flue gas containing 20 mol% CO2. The comprehensive techno-economic optimisation model developed herein takes into account VSA process model, peripheral component models, vacuum pump performance, scale-up, process scheduling and a thorough cost model. Three adsorbents, namely, Zeolite 13X (current benchmark material for CO2 capture) and two metal–organic frameworks, UTSA-16 (widely studied metal–organic framework for CO2 capture) and IISERP MOF2 (good performer in recent findings) are optimised to minimise the CO2 capture cost. Monoethanolamine (MEA)-based absorption technology serves as a baseline case to assess and compare optimal techno-economic performances of VSA technology for three adsorbents. The results show that the four step VSA process with IISERP MOF2 outperforms other two adsorbents with a lowest CO2 capture cost (including flue gas pre-treatment) of 33.6 € per tonne of CO2 avoided and an associated CO2 avoided cost of 73.0 € per tonne of CO2 avoided. Zeolite 13X and UTSA-16 resulted in CO2 avoided costs of 90.9 and 104.9 € per tonne of CO2 avoided, respectively. The CO2 avoided costs obtained for the VSA technology remain higher than that of the baseline MEA-based absorption process which was found to be 66.6 € per tonne of CO2 avoided. The study also demonstrates the importance of using cost as means of evaluating the separation technique compared to the use of process performance indicators. Accounting for the efficiency of vacuum pumps and the cost of novel materials such as metal–organic frameworks is highlighted. © 2020 Elsevier B.V.acceptedVersio

How to accurately fast-track sorbent selection for post-combustion CO2 capture? A comparative assessment of data-driven and simplified physical models for screening sorbents

The recent discovery of a multitude of hypothetical materials for CO2 capture applications necessitated the development of reliable computational models to aid the quest for better-performing sorbents. Given the computational challenges associated with existing detailed adsorption process design and optimization frameworks, two types of screening methodologies based on computationally inexpensive models, namely, data-driven and simplified physical models, have been proposed in the literature. This study compares these two screening methodologies for their effectiveness in identifying best-performing sorbents from a set of 369 metal-organic frameworks (MOFs). The results showed that almost 60% of the MOFs in the top 20 best-performing materials ranked by each of these approaches were found to be common. The validation of these results against detailed process simulation and optimization-based screening approach is currently underway. © 2023 Elsevier B.V. Author keywords adsorption; machine learning; metal-organic frameworks; modelling and optimization; post-combustion CO2 captureHow to accurately fast-track sorbent selection for post-combustion CO2 capture? A comparative assessment of data-driven and simplified physical models for screening sorbentsacceptedVersio

Physics-based neural networks for simulation and synthesis of cyclic adsorption processes

A computationally faster and reliable modelling approach called a physics-based artificial neural network framework for adsorption and chromatography emulation (PANACHE) is developed. PANACHE uses deep neural networks for cycle synthesis and simulation of cyclic adsorption processes. The proposed approach focuses on learning the underlying governing partial differential equations in the form of a physics-constrained loss function to simulate adsorption processes accurately. The methodology developed herein does not require any system-specific inputs such as isotherm parameters. Accordingly, unique neural network models were built to fully predict the column dynamics of different constituent steps based on unique boundary conditions that are typically encountered in adsorption processes. The trained neural network model for each constituent step aims to predict the entire spatiotemporal solutions of different state variables by obeying the underlying physical laws. The proposed approach is tested by constructing and simulating four different vacuum swing adsorption cycles for post-combustion CO2 capture without retraining the neural network models. For each cycle, 50 simulations, each corresponding to a unique set of operating conditions, are carried out until the cyclic-steady state. The results demonstrated that the purity and recovery calculated from the neural network-based simulations are within 2.5% of the detailed model\u27s predictions. PANACHE reduced computational times by 100 times while maintaining similar accuracy of the detailed model simulations

Can a computer “learn” non-linear chromatography?: Experimental validation of physics-based deep neural networks for the simulation of chromatographic processes

This article presents the capabilities of machine learning in addressing the challenges related to the accurate description of adsorption equilibria in the design of chromatographic processes. Our previously developed physics-based artificial neural network framework for adsorption and chromatography emulation (PANACHE) approach is extended to simulate the dynamics of chromatographic columns without using adsorption isotherms. The incorporation of underlying conservation laws in the form of a physics-constrained loss function during training enables PANACHE models to infer the complex dynamics of chromatographic columns, even without the knowledge of adsorption isotherms. The isotherm-agnostic abilities of PANACHE models are tested by considering two binary systems with complex adsorption equilibria: 1) mixed Langmuir M1-type binary system, and 2) Tröger\u27s base enantiomers on Chiralpak AD that show inflection in the isotherm. For each case study, unique feed-forward deep neural networks with an input layer, five hidden layers, and an output layer are trained for two solute components using the elution data either from simulations or experiments. The results show that PANACHE models are successful in predicting the dynamics of binary solute mixtures in chromatographic columns even in the absence of adsorption isotherms, with prediction errors in the order of 10^-2 in the measure of relative L2-norm, for most of the cases. The ability of PANACHE models to infer the adsorption isotherms is also demonstrated

How much can novel solid sorbents reduce the cost of post-combustion CO2 capture? A techno-economic investigation on the cost limits of pressure-vacuum swing adsorption

This paper focuses on identifying the cost limits of two single-stage pressure-vacuum swing adsorption (PVSA) cycles for post-combustion CO2 capture if the ``ideal\u27\u27 zero-cost adsorbent can be discovered. Through an integrated techno-economic optimisation, we simultaneously optimise the adsorbent properties (adsorption isotherms and particle morphology) and process design variables to determine the lowest possible cost of CO2 avoided (excluding the CO2 conditioning, transport and storage) for different industrial flue gas CO2 compositions and flow rates. The CO2 avoided cost for PVSA ranges from 87.1 to 10.4 € per tonne of CO2 avoided, corresponding to CO2 feed compositions of 3.5 mol% to 30 mol%, respectively. The corresponding costs for a monoethanolamine based absorption process, using heat from a natural gas plant, are 76.8 to 54.8 EUR per tonne of CO2 avoided, respectively showing that PVSA can be attractive for flue gas streams with high CO2 compositions. The ``ideal" adsorbents needed to attain the lowest possible CO2 avoided costs have a range of CO2 affinities with close to zero N2 adsorption, demonstrating promise for adsorbent discovery and development. The need for simultaneously optimizing the particle morphology and the process conditions are emphasized

How much can novel solid sorbents reduce the cost of post-combustion CO2 capture? A techno-economic investigation on the cost limits of pressure–vacuum swing adsorption

This paper focuses on identifying the cost limits of two single-stage pressure–vacuum swing adsorption (PVSA) cycles for post-combustion CO2 capture if the ‘‘ideal’’ zero-cost adsorbent can be discovered. Through an integrated techno-economic optimisation, we simultaneously optimise the adsorbent properties (adsorption isotherms and particle morphology) and process design variables to determine the lowest possible cost of CO2 avoided (excluding the CO2 conditioning, transport and storage) for different industrial flue gas CO2 compositions and flow rates. The CO2 avoided cost for PVSA ranges from 87.1 to 10.4 e per tonne of CO2 avoided, corresponding to CO2 feed compositions of 3.5 mol% to 30 mol %, respectively. The corresponding costs for a monoethanolamine based absorption process, using heat from a natural gas plant, are 76.8 to 54.8 e per tonne of CO2 avoided, respectively showing that PVSA can be attractive for flue gas streams with high CO2 compositions. The ‘‘ideal’’ adsorbents needed to attain the lowest possible CO2 avoided costs have a range of CO2 affinities with close to zero N2 adsorption, demonstrating promise for adsorbent discovery and development. The need for simultaneously optimising the particle morphology and the process conditions are emphasised

How much can novel solid sorbents reduce the cost of post-combustion CO2 capture? A techno-economic investigation on the cost limits of pressure–vacuum swing adsorption

This paper focuses on identifying the cost limits of two single-stage pressure–vacuum swing adsorption (PVSA) cycles for post-combustion CO2 capture if the ‘‘ideal’’ zero-cost adsorbent can be discovered. Through an integrated techno-economic optimisation, we simultaneously optimise the adsorbent properties (adsorption isotherms and particle morphology) and process design variables to determine the lowest possible cost of CO2 avoided (excluding the CO2 conditioning, transport and storage) for different industrial flue gas CO2 compositions and flow rates. The CO2 avoided cost for PVSA ranges from 87.1 to 10.4 e per tonne of CO2 avoided, corresponding to CO2 feed compositions of 3.5 mol% to 30 mol %, respectively. The corresponding costs for a monoethanolamine based absorption process, using heat from a natural gas plant, are 76.8 to 54.8 e per tonne of CO2 avoided, respectively showing that PVSA can be attractive for flue gas streams with high CO2 compositions. The ‘‘ideal’’ adsorbents needed to attain the lowest possible CO2 avoided costs have a range of CO2 affinities with close to zero N2 adsorption, demonstrating promise for adsorbent discovery and development. The need for simultaneously optimising the particle morphology and the process conditions are emphasised.publishedVersio

Is Carbon Capture and Storage (CCS) Really So Expensive? An Analysis of Cascading Costs and CO₂ Emissions Reduction of Industrial CCS Implementation on the Construction of a Bridge

Carbon capture and storage (CCS) is an essential technology to mitigate global CO2 emissions from power and industry sectors. Despite the increasing recognition of its importance to achieve the net-zero target, current CCS deployment is far behind targeted ambitions. A key reason is that CCS is often perceived as too expensive. The costs of CCS have however traditionally been looked at from the industrial plant perspective, which does not necessarily reflect the end user’s one. This paper addresses the incomplete view by investigating the impact of implementing CCS in industrial facilities on the overall costs and CO2 emissions of end-user products and services. As an example, we examine the extent to which an increase in costs of raw materials (cement and steel) due to CCS impacts the costs of building a bridge. Results show that although CCS significantly increases cement and steel costs, the subsequent increment in the overall bridge construction cost remains marginal (∼1%). This 1% cost increase, however, enables a deep reduction in CO2 emissions (∼51%) associated with the bridge construction. Although more research is needed in this area, this work is the first step to a better understanding of the real cost and benefits of CCS.Energie and Industri

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – A state-of-the-art review

Carbon Capture, Utilisation and Storage (CCUS) will play a critical role in future decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of climate change. Whilst there are many well developed CCUS technologies there is the potential for improvement that can encourage CCUS deployment. A time and cost-efficient way of advancing CCUS is through the application of machine learning (ML). ML is a collective term for high-level statistical tools and algorithms that can be used to classify, predict, optimise, and cluster data. Within this review we address the main steps of the CCUS value chain (CO2 capture, transport, utilisation, storage) and explore how ML is playing a leading role in expanding the knowledge across all fields of CCUS. We finish with ten recommendations for further work and research that will help develop the role that ML plays in CCUS and enable greater deployment of the technologies