Capturing industrial CO2 emissions in Spain: Infrastructures, costs and break-even prices

This paper examines the conditions for the deployment of large-scale pipeline and storage infrastructure needed for the capture of CO2 in Spain by 2040. It details a modeling framework that allows us to determine the optimal infrastructure needed to connect a geographically disaggregated set of emitting and storage clusters, along with the threshold CO2 values necessary to ensure that the considered emitters will make the necessary investment decisions. This framework is used to assess the relevance of various policy scenarios, including (i) the perimeter of the targeted emitters for a CCS uptake, and (ii) the relevance of constructing several regional networks instead of a single grid to account for the spatial characteristics of the Spanish peninsula. We find that three networks naturally emerge in the north, center and south of Spain. Moreover, the necessary CO2 break-even price critically depends on the presence of power stations in the capture perimeter. Policy implications of these findings concern the elaboration of relevant, pragmatic recommendations to envisage CCS deployment locally, focusing on emitters with lower substitution options toward low-carbon alternatives

Leveraging Artificial Intelligence and Geomechanical Data for Accurate Shear Stress Prediction in CO2 Sequestration within Saline Aquifers (Smart Proxy Modeling)

This research builds upon the success of a previous project that used a Smart Proxy Model (SPM) to predict pressure and saturation in Carbon Capture and Storage (CCS) operations into saline aquifers. The Smart Proxy Model is a data-driven machine learning model that can replicate the output of a sophisticated numerical simulation model for each time step in a short amount of time, using Artificial Intelligence (AI) and large volumes of subsurface data. This study aims to develop the Smart Proxy Model further by incorporating geomechanical datadriven techniques to predict shear stress by using a neural network, specifically through supervised learning, to construct Smart Proxy Models, which are critical to ensuring the safety and effectiveness of Carbon Capture and Storage operations. By training the Smart Proxy Model with reservoir simulations that incorporate varying geological properties and geomechanical data, we will be able to predict the distribution of shear stress. The ability to accurately predict shear stress is crucial to mitigating the potential risks associated with Carbon Capture and Storage operations. The development of a geomechanical Smart Proxy Model will enable more efficient and reliable subsurface modeling decisions in Carbon Capture and Storage operations, ultimately contributing to the safe and effective storage of CO2 and the global effort to combat climate change

Carbon-Dioxide Pipeline Infrastructure Route Optimization And Network Modeling For Carbon Capture Storage And Utilization

Carbon capture, utilization, and storage (CCUS) is a technology value-chain which can help reduce CO2 emissions while ensuring sustainable development of the energy and industrial sectors. However, CCUS requires large-scale deployment of infrastructure for capturing feasible amounts of CO2 that can be capital intensive for stakeholders. In addition, CCUS deployment leads to the development of extensive pipeline corridors, which can be inconsistent with the requirements for future CCUS infrastructure expansion. With the implementation and growth of CCUS technology in the states of North Dakota, Montana, Wyoming, Colorado and Utah in mind, this dissertation has two major goals: (a) to identify feasible corridors for CO2 pipelines; and (b) to develop a CCUS infrastructure network which minimizes project cost. To address these goals, the dissertation introduces the CCSHawk methodology that develops pipeline routes and CCUS infrastructure networks using a variety of techniques such as multi-criteria decision analysis (MCDA), graph network algorithms, natural language processing and linear network optimization. The pipeline route and CCUS network model are designed using open-source data, specifically: geo-information, emission quantities and reservoir properties. The MCDA of the study area reveals that North Dakota, central Wyoming and Eastern Colorado have the highest amount of land suitable for CO2 pipeline corridors. The optimized graph network routing algorithm reduces the overall length of pipeline routes by an average of 4.23% as compared to traditional routing algorithms while maintaining low environmental impact. The linear optimization of the CCUS infrastructure shows that the cost for implementing the technology in the study area can vary between $24.05/tCO2 to$42/tCO2 for capturing 20 to 90MtCO2. The analysis also reveals that there would be a declining economic impact of existing pipeline infrastructure on the future growth of CCUS networks ranging between 0.01 to 1.62$/tCO2 with increasing CO2 capture targets. This research is significant, as it establishes a technique for pipeline route modeling and CCUS economic analysis highly adaptable to various geographic regions. To the best of the author\u27s knowledge, it is also the first economic analysis that considers the effect of pre-existing infrastructure on the growth of CCUS technology for the region. Furthermore, the pipeline route model establishes a schema for considering not only environmental factors but also ecological factors for the study area

Modeling the development of a carbon capture and transportation infrastructure for Swedish industry

This work presents and applies a mixed integer programming (MIP) optimization model that minimizes the net present costs for CO2 capture and storage (CCS) systems for cases with defined emissions costs and/or capture targets. The model covers capture from existing large point sources of CO2 emissions in Sweden, liquefaction, intermediate storage and transportation using trucks to hubs on the coast, followed by ship transport to a storage location (excluding storage cost). The results show that the capture and transportation infrastructure, in terms of both the sites chosen for capture and the associated transportation setup, differs depending on whether the system is incentivized to capture biogenic or fossil CO2, or both. Waste-fired combined heat and power (CHP) plants are only chosen for capture at scale when biogenic capture targets and fossil emissions costs are combined, since the emissions from these sites comprise a combination of biogenic and fossil CO2. The value for the system in mitigating the costs from fossil CO2 emissions exceeds the increased cost of BECCS at waste-fired CHPs compared to larger pulp mills given the fossil emissions cost development assumed in this work. Although the cost for capture and liquefaction dominates the total cost of the CCS system, it is not the only factor determining the choice of sites for capture. Proximity to transport hubs with short offshore transportation distances to the final storage location is also an important factor. For the transportation infrastructure, it is shown that the cost for ships is the main cost driver

Feasibility Study of Carbon Capture and Storage Process to implement in Maritime Industry

The CO2 emission is considered as one of the main elements for causing the global climatic change. The maritime industry contributes about 2.5% of the worldwide CO2 gas emission annually. The International Maritime Industry (IMO) has set the target of has set the target of reducing the total annual emission from international shipping to 70% by 2050. So, the carbon capture, utilization, and storage (CCUS) unit are considered as the principal alternative to reduce CO2 emission by capturing it from vessels. This thesis investigates the various post combustion carbon capture technologies with the possibility of carbon transportation and long-term storage of captured carbon. This thesis performed the technical and economic analysis of installing the carbon capture and storage technology in the specified vessel according to with the methodology established

Building infrastructures for Fossil- and Bio-energy with Carbon Capture and Storage: insights from a cooperative game-theoretic perspective

This paper examines the deployment of a shared CO2 transportation infrastructure needed to support the combined emergence of Bio-energy with Carbon Capture and Storage (BECCS) and Fossil energy with Carbon Capture and Storage (CCS). We develop a cooperative game-theoretic approach to: (i) examine the conditions needed for its construction to be decided, and (ii) determine the break-even CO2 value needed to build such a shared infrastructure. In particular, we highlight that, as biogenic emissions are overlooked in currently-implemented carbon accounting frameworks, BECCS and CCS emitters face asymmetric conditions for joining a shared infrastructure. We thus further examine the influence of these carbon accounting considerations by assessing and comparing the break-even CO2 values obtained under alternative accounting rules. We apply this modeling framework to a large contemporary BECCS/CCS case-study in Sweden. Our results indicate that sustainable and incentive-compatible cooperation schemes can be implemented if the value of CO2 is high enough and show how that value varies depending on the carbon accounting framework retained for negative emissions and the nature of the infrastructure operators. In the most advantageous scenario, the CO2 value needs to reach 112€/tCO2, while the current Swedish carbon tax amounts to 110€/tCO2. Overall, these findings position pragmatic policy recommendations for local BECCS deployment

Multi-period whole system optimisation of an integrated carbon dioxide capture, transportation and storage supply chain

Carbon dioxide capture and storage (CCS) is an essential part of the portfolio of technologies to achieve climate mitigation targets. Cost efficient and large scale deployment of CCS necessitates that all three elements of the supply chain (capture, transportation and storage) are coordinated and planned in an optimum manner both spatially and across time. However, there is relatively little experience in combining CO2 capture, transport and storage into a fully integrated CCS system and the existing research and system planning tools are limited. In particular, earlier research has focused on one component of the chain or they are deterministic steady-state supply chain optimisation models. The very few multi-period models are unable to simultaneously make design and operational decisions for the three components of the chain. The major contribution of this thesis is the development for the first time of a multi-period spatially explicit least cost optimization model of an integrated CO2 capture, transportation and storage infrastructure under both a deterministic and a stochastic modelling framework. The model can be used to design an optimum CCS system and model its long term evolution subject to realistic constraints and uncertainties. The model and its different variations are validated through a number of case studies analysing the evolution of the CCS system in the UK. These case studies indicate that significant cost savings can be achieved through a multi-period and integrated system planning approach. Moreover, the stochastic formulation of the model allows analysing the impact of a number of uncertainties, such as carbon pricing or plant decommissioning schedule, on the evolution of the CSS system. In conclusion, the model and the results presented in this thesis can be used for system planning purposes as well as for policy analysis and commercial appraisal of individual elements of the CCS network.Open Acces

Data Driven Modelling and Optimization of MEA Absorption Process for CO2 Capture

Global warming is a rising issue and there are many research studies aiming to reduce greenhouse gas emissions. Carbon capture and storage technologies improved throughout the years to contribute as a solution to this problem. In this work the post-combustion carbon capture unit is used to develop surrogated models for operation optimization. Previous work included mechanistic and detailed modeling of steady-state and dynamic systems. Furthermore, control structures and optimization approaches have been studied. Moreover, various solutions such as MEA, DEA, and MDEA have been tested and simulated to determine the efficiency and the behavior of the system. In this work a dynamic model with MEA solution developed by (Nittaya, 2014) and (Harun, 2012) is used to generate operational data. The system is simulated using gProms v.5.1 with six PI controllers. The model illustrated that the regeneration of the solvent is the most energy-consuming part of the process. Due to the changes in electricity supply and demand, also, the importance of achieving a specific %CC and purity of carbon dioxide as outputs of this process, surrogated models are developed and used to predict the outputs and to optimize the operating conditions of the process. Multiple machine learning and data-driven models has been developed using simulation data generated after a proper choice of the operating variables and the important outputs. Steady-state and transient state models have been developed and evaluated. The models were used to predict the outputs of the process and used later to optimize the operating conditions of the process. The flue gas flow rate, temperature, pressure, reboiler pressure, reboiler, and condenser duties were selected as the operating variables of the system (inputs). The system energy requirements, %CC, and the purity of carbon dioxide were selected to be the outputs of the process. For steady-state modeling, artificial neural network (ANN) model with backpropagation and momentum was developed to predict the process outputs. The ANN model efficiency was compared to other machine learning models such as Gaussian Process Regression (GPR), rational quadratic GPR, squared exponential GPR, tree regression and matern GPR. The ANN excelled all other models in terms of prediction and accuracy, however, the other model’s regression coefficient (R2) was never below 0.95. For dynamic modelling, recurrent neural networks (RNN) have been used to predict the outputs of the system. Two training algorithms have been used to create the neural network: Levenberg-Marquardt (LM) and Broyden-Fletcher-Goldfrab-Shanno (BFGS). The RNN was able to predict the outputs of the system accurately. Sequential quadratic programming (SQP) and genetic algorithm (GA) were used to optimize the surrogated models and determine the optimum operating conditions following an objective of maximizing the purity of CO2 and %CC and minimizing the system energy requirements

Preliminary Feasibility of Transporting and Geologically Sequestering Carbon Emissions in the Florida Pan-Handle

According to the United States Department of Energy, fossil-fueled power plants account for 78% of stationary source CO2 emission in the United States and Canada. This has led electric utilities across the globe to research different alternatives for energy. Carbon sequestration has been identified as a bridge between fossil fuels and clean energy. This thesis will present research results regarding the transportation costs of CO2 and the suitability of geology in the Florida Pan-Handle for sequestration infrastructure. The thesis will utilize various evaluation tools including GIS, numerical models, and optimization models. Analysis performed for this thesis and review of published literature produced estimated carbon storage capacities for two areas in and near the Florida Pan-Handle. These areas were labeled Disposal Area 1 and Disposal Area 3. Disposal Area 1 was estimated to contain capacity for the storage of 5.58 gigatonnes of CO2. Disposal Area 3 was estimated to contain capacity for the storage of 2.02 gigatonnes of CO2. Transportation scenarios were analyzed over a 25 year period and the capacities above are sufficient to store the CO2 emissions from the Pan-Handle network of power plants for the study period. Four transportation routing scenarios were investigated using transportation costs from the Poiencot and Brown CO2 pipeline capital cost model. The scenarios (models) consisted of the Right-Of-Way, Solo-Funded, Piece-Wise, and Authority models. Each presents a different method for the overall funding of the Florida Pan-Handle CO2 network and produced different total levelized and mean unit costs. The cheapest network on a mean unit cost basis was the network for Disposal Area 1 in the Authority Model, producing a mean unit cost of $0.64 per tonne of CO2

A systematic review of keys challenges of CO2 transport via pipelines

Transport of carbon dioxide (CO2) via pipeline from the point of capture to a geologically suitable location for either sequestration or enhanced hydrocarbon recovery is a vital aspect of the carbon capture and storage (CCS) chain. This means of CO2 transport has a number of advantages over other means of CO2 transport, such as truck, rail, and ship. Pipelines ensure continuous transport of CO2 from the capture point to the storage site, which is essential to transport the amount of CO2 captured from the source facilities, such as fossil fuel power plants, operating in a continuous manner. Furthermore, using pipelines is regarded as more economical than other means of CO2 transport The greatest challenges of CO2 transport via pipelines are related to integrity, flow assurance, capital and operating costs, and health, safety and environmental factors. Deployment of CCS pipeline projects is based either on point-to-point transport, in which case a specific source matches a specific storage point, or through the development of pipeline networks with a backbone CO2 pipeline. In the latter case, the CO2 streams, which are characterised by a varying impurity level and handled by the individual operators, are linked to the backbone CO2 pipeline for further compression and transport. This may pose some additional challenges. This review involves a systematic evaluation of various challenges that delay the deployment of CO2 pipeline transport and is based on an extensive survey of the literature. It is aimed at confidence-building in the technology and improving economics in the long run. Moreover, the knowledge gaps were identified, including lack of analyses on a holistic assessment of component impurities, corrosion consideration at the conceptual stage, the effect of elevation on CO2 dense phase characteristics, permissible water levels in liquefied CO2, and commercial risks associated with project abandonment or cancellation resulting from high project capital and operating costs