abstract: Carbon Capture and Storage (CCS) is a climate stabilization strategy that prevents CO2 emissions from entering the atmosphere. Despite its benefits, impactful CCS projects require large investments in infrastructure, which could deter governments from implementing this strategy. In this sense, the development of innovative tools to support large-scale cost-efficient CCS deployment decisions is critical for climate change mitigation. This thesis proposes an improved mathematical formulation for the scalable infrastructure model for CCS (SimCCS), whose main objective is to design a minimum-cost pipe network to capture, transport, and store a target amount of CO2. Model decisions include source, reservoir, and pipe selection, as well as CO2 amounts to capture, store, and transport. By studying the SimCCS optimal solution and the subjacent network topology, new valid inequalities (VI) are proposed to strengthen the existing mathematical formulation. These constraints seek to improve the quality of the linear relaxation solutions in the branch and bound algorithm used to solve SimCCS. Each VI is explained with its intuitive description, mathematical structure and examples of resulting improvements. Further, all VIs are validated by assessing the impact of their elimination from the new formulation. The validated new formulation solves the 72-nodes Alberta problem up to 7 times faster than the original model. The upgraded model reduces the computation time required to solve SimCCS in 72% of randomly generated test instances, solving SimCCS up to 200 times faster. These formulations can be tested and then applied to enhance variants of the SimCCS and general fixed-charge network flow problems. Finally, an experience from testing a Benders decomposition approach for SimCCS is discussed and future scope of probable efficient solution-methods is outlined. / Dissertation/Thesis / Masters Thesis Industrial Engineering 2017

The integration of variable renewable energy sources (VRE) is likely to cause fundamental and structural changes to the operation of future power systems. In the United Kingdom (UK), large amounts of price-insensitive and variable-output wind generation is expected to be deployed to contribute towards renewable energy and carbon dioxide (CO2) emission targets. Wind generation, with near-zero marginal costs, limited predictability, and a limited ability to provide upward dispatch, displaces price-setting thermal power plants, with higher marginal costs, changing flexibility and reserve requirements. New-build, commercial-scale, and low-carbon generation capacity, such as CO2 capture and storage (CCS) and nuclear, may impact power system flexibility and ramping capabilities. Low-carbon generation portfolios with price-sensitive thermal power plants and energy storage are therefore likely to be required to manage increased levels of variability and uncertainty at operational timescales. This work builds on a high-resolution wind reanalysis dataset of UK wind sites. The locations of existing and proposed wind farms are used to produce plausible and internally consistent wind deployment scenarios that represent the spatial distribution of future UK wind capacity. Temporally consistent electricity demand data is used to characterise and assess demand-wind variability and net demand ramp events. A unit commitment and economic dispatch (UCED) model is developed to evaluate the likely operating regimes of thermal power plants and CCS-equipped units across a range of future UK wind scenarios. Security constraints for reserve and power plant operating constraints, such as power output limits, ramp rates, minimum up/down times, and start-up times, ensure the operational feasibility of dispatch schedules. The load factors, time spent at different loads, and the ramping and start-up requirements of thermal power plants are assessed. CO2 duration curves are developed to assess the impacts of increasing wind capacity on the distribution of CO2 emissions. A sensitivity analysis investigates the impacts of part-load efficiency losses, ramp rates, minimum up/down times, and start-up/shut-down costs on power plant operating regimes and flexibility requirements. The interactions between a portfolio of energy storage units and flexible CO2 capture units are then explored. This multi-disciplinary research presents a temporally-explicit and detailed assessment of operational flexibility requirements at full 8760 hour resolution, highlighting the non-linear impacts of increasing wind capacity. The methodological framework presented here uses high spatial-and temporal-resolution wind data but is expected to provide useful insights for other VREbased power systems to mitigate the implications of inadequate flexibility.

The scope of the present thesis is the development of a Computational Fluid Dynamics model to describe the multiphase flow inside a structured packing absorber for postcombustion carbon capture. The work focuses mainly on two flow characteristics: the interface tracking and the reactive mass transfer between the gas and the liquid. The interface tracking brings the possibility of studying the liquid maldistribution phenomenon, which strongly affects the mass transfer performance. The development of a user-defined function to account for the reactive mass transfer between phases constitutes the second major concept considered in this thesis. Numerical models found in the literature are divided into three scales due to the current computational capacity: small-, meso- and large-scale. Small-scale has usually dealt with interface tracking in 2D computational domains. Meso-scale has usually been considered to assess the dry pressure drop performance of the packing (considering only the gas phase). Large-scale studies the liquid distribution over the whole column assuming that the structured packing behaves as a porous medium. This thesis focuses on small- and meso-scale. The novelty of this work lies in expanding the capabilities of the aforementioned scales. At small-scale, the interfacial tracking is implemented in a 3D domain, instead of 2D. The user-defined function that describes the reactive mass transfer of CO2 into the aqueous MEA solution is also included to assess the influence of the liquid maldistribution on the mass transfer performance. At the meso-scale, the Volume of Fluid method for interface tracking is included (instead of only the gas phase) to describe flow characteristics such as the liquid hold-up, the interfacial area and the mass transfer. At the theoretical level, this model presents the particularity of including both a mass and a momentum source term in the conservation equations. A comprehensive mathematical development shows the influence of the mass source terms on the momentum equation.

An examination of the stationary power sector is performed using The Natural Step framework and Sustainability Principles (SP), in order to aid decision makers in developing policy to balance energy needs while reducing carbon dioxide (CO2) emissions in order to address climate change. Carbon capture and storage (CCS) is evaluated for its sustainability aspects, and is found to be a potentially sustainable approach which can be a bridging technology to a more sustainable energy mix, as well as a remediation technology which can remove CO2 from the atmosphere when utilized in combination with biomass fuel. Initial actions for restructuring the stationary power sector should emphasise demand reduction and efficiency efforts, followed by switching to renewable energy sources. If the first two strategies can not provide sufficient CO2 reductions, then investments in CCS technology may be an appropriate choice. CCS with coal-fired power can be a means to decouple CO2 emissions from fossil fuel use, but other SP violations associated with coal use must also be fully addressed before this strategy can be considered a truly sustainable option.

Carbon capture and storage technologies are necessary to start lowering greenhouse gas emissions while continuing to utilize existing thermal power generation infrastructure. Calcium looping is a promising technology based on cyclic calcination/carbonation reactions which utilizes limestone as a sorbent. Steam is present in combustion flue gas and in the calciner used for sorbent regeneration. The effect of steam during calcination on sorbent performance has not been extensively studied in the literature. Here, experiments were conducted using a thermogravimetric analyzer (TGA) and subsequently a dual-fluidized bed pilot plant to determine the effect of steam injection during calcination on sorbent reactivity during carbonation. In a TGA, various levels of steam (0-40% vol.) were injected during sorbent regeneration throughout 15 calcination/carbonation cycles. All concentrations of steam were found to increase sorbent reactivity during carbonation. A level of 15% steam during calcination had the largest impact. Steam changes the morphology of the sorbent during calcination, likely by shifting the pore volume to larger pores, resulting in a structure which has an increased carrying capacity. This effect was then examined at the pilot scale to determine if the phase contacting patterns and solids heat-up rates in a fluidized bed were factors. Three levels of steam (0%, 15%, 65%) were injected during sorbent regeneration throughout 5 hours of steady state operation. Again, all levels of steam were found to increase sorbent reactivity and reduce the required sorbent make-up rate with the best performance seen at 65% steam.

abstract: As the global community raises concerns regarding the ever-increasing urgency of climate change, efforts to explore innovative strategies in the fight against this anthropogenic threat is growing. Along with other greenhouse gas mitigation technologies, Direct Air Capture (DAC) or the technology of removing carbon dioxide directly from the air has received considerable attention. As an emerging technology, the cost of DAC has been the prime focus not only in scientific society but also between entrepreneurs and policymakers. While skeptics are concerned about the high cost and impact of DAC implementation at scales comparable to the magnitude of climate change, industrial practitioners have demonstrated a pragmatic path to cost reduction. Based on the latest advancements in the field, this dissertation investigates the economic feasibility of DAC and its role in future energy systems. With a focus on the economics of carbon capture, this work compares DAC with other carbon capture technologies from a systemic perspective. Moreover, DAC’s major expenses are investigated to highlight critical improvements necessary for commercialization. In this dissertation, DAC is treated as a backstop mitigation technology that can address carbon dioxide emissions regardless of the source of emission. DAC determines the price of carbon dioxide removal when other mitigation technologies fall short in meeting their goals. The results indicate that DAC, even at its current price, is a reliable backup and is competitive with more mature technologies such as post-combustion capture. To reduce the cost, the most crucial component of a DAC design, i.e., the sorbent material, must be the centerpiece of innovation. In conclusion, DAC demonstrates the potential for not only negative emissions (carbon dioxide removal with the purpose of addressing past emissions), but also for addressing today’s emissions. The results emphasize that by choosing an effective scale-up strategy, DAC can become sufficiently cheap to play a crucial role in decarbonizing the energy system in the near future. Compared to other large-scale decarbonization strategies, DAC can achieve this goal with the least impact on our existing energy infrastructure. / Dissertation/Thesis / Doctoral Dissertation Sustainable Engineering 2020

The master’s thesis addresses the transmedia education strategies applied in the CO2 degrees challenge, a project by the Global Carbon Capture and Storage Institute (GCCSI), dedicated to educating students about the low-carbon technology carbon capture and storage (CCS). Based on the literature review, the thesis theoretically discusses the public perception of low-carbon technology and carbon capture and storage in connection to transmedia storytelling and education. The thesis is structured upon a qualitative approach and conducts a detailed study of a selected single case. In addition, expert interviews are used to support the in-depth analysis of the case study with the use of the transmedia design analytical and operational model by Gambarato et al. (2020). The analysis provides insights into how this technical process was presented to the educators and students and identifies challenges and opportunities of the transmedia education project in the realm of low-carbon technologies. The results of the study show that the CO2 degrees challenge offered a significant contribution to the knowledge dissemination about the CCS technology among students, helped them to understand current problems, and to discuss real-time solutions. The results also point to the challenge of communicating a scientific and engineering topic with the audience outside of the technological and engineering industry, the complicated structure of the project, and difficulties connected with possible financial interests of the private companies supporting and funding the educational material and the project itself.

This thesis explores the use of computational chemistry and machine learning techniques to aid in the design of Metal-Organic Frameworks (MOFs) for use in postcombustion carbon capture and storage (PoC-CCS). PoC-CCS is an ongoing field of research which aims to selectively remove carbon dioxide, an important greenhouse gas, from the exhaust of fossil-fuel burning powerplants. By using a suite of advanced simulation techniques, high-throughput screenings were performed on thousands of MOFs to study their behaviour in a pressure swing adsorption (PSA) system. To develop a comprehensive picture of a material’s performance, the behaviour of individual gas molecules within the pores of the crystal structures to the material’s performance in industrial scale PSA columns was evaluated. To study the behaviour of individual gas molecules within the pores of a MOF, a new algorithm which can accurately determine the locations of gas binding sites was developed. This algorithm, which relies on probability distributions generated through grand canonical Monte Carlo simulations (GCMC), was optimized for CO2 with the goal of use in high-throughput screening. By tuning the user-controlled parameters for a desired gas, this algorithm, which was named the Guest Atom Localization Algorithm (GALA), was shown to accurately reproduce experimentally determined binding sites while being run in a high-throughput manner with no user intervention. Studying MOFs at the pore or crystal scale in this manner provides valuable insights into the behaviour of gases within the materials. A major shortcoming, however, is the lack of direct insight into the material’s behaviour in industrial systems. Materials scientists and MOF chemists have historically focused on a set of performance metrics measured at this scale; however, no clear connection can be made between such metrics and the performance of that sorbent material in a PSA column. To bridge this gap between MOF chemists and the process engineers studying the PSA systems, a large-scale screening of MOFs was performed using a sophisticated PSA simulator designed to reproduce the performance of an 80 kg PSA column. By supplying isotherms obtained using GCMC simulations to be used as inputs into the PSA simulator, a multi-scale high-throughput screening of MOFs for PoC-CCS was performed for the first time under coal-fired powerplant conditions. This multi-scale screening provided the ideal conditions to study the materials science performance metrics and their relationships to industrial PSA performance. To study this relationship, a series of machine learning and artificial intelligence techniques were employed. The primary goal was to extract important relationships between the materials science and industrial PSA performance metrics, with a secondary goal of developing a predictive model which could be used to accelerate the pace of materials discovery. Through the use of machine learning, several metrics were identified which could be used to predict whether a material could meet the minimum target of 95 % purity of captured CO2, and 90 % removal (or recovery) of CO2 from the flue gas stream. Among them was the isotherm parameters for N2, the most abundant species in the flue gas. This finding was significant as to date the focus among MOF chemists studying the PoC-CCS system was placed primarily on the CO2 metrics, with N2 only implicitly considered when calculating the CO2/N2 selectivity. Although several metrics were identified which could predict the purity and recovery targets, none of the conventional metrics tested could be used to estimate the energetic cost of capture or the size of the capture plant, both important considerations in evaluating the cost of capture. The relationship between N2 binding within the pores of the MOF and its ability to meet the purity-recovery targets was explored using GALA. Using a Tanimoto similarity metric and the ratio of single component and competitive loadings, the CO2 and N2 binding environments were studied. It was determined that when the N2 binding environment was significantly altered by the presence of CO2, the material was more likely to meet the purity-recovery targets. Further analysis found that this change in binding environments was correlated to a reduced N2 uptake in the presence of CO2, implying that the competition for binding sites within the pores of the MOF is an important indicator for the material’s ability to meet the purity-recovery target. For the first time, a direct relationship between the behaviour of individual gas molecules to industrial PSA performance can be reported. Although the PSA simulator used throughout this work has proven to be a powerful tool for materials discovery, several shortcomings still exist. The first is the method used by the simulator to predict the loadings at various points within the column. This method relies on single component isotherm data despite the ability of GCMC to simulate multi-component isotherms. An alternative method to using single component isotherms was proposed which relies on multi-component isotherm data and a linear interpolation model. The existing method was compared to the new proposed interpolation method, and it was found that the loadings predicted using the interpolation method were more accurate. The second shortcoming of the PSA simulator is the computational expense associated with the optimizations. Using the PSA simulator, a single material may take up to a week to be fully optimized on a high-performance computing cluster. To increase the pace of materials discovery, a surrogate model was developed using the data accumulated over the course of the work presented in this thesis. Using artificial neural networks, a suite of models was developed which reproduces the outputs of the PSA simulator and is able to optimize a single MOF in a matter of minutes. This suite of models, known as the Fossil Fuel Combustion for Carbon Capture and Storage (FoCAS) was used to perform a screening of over 4,000 materials.

Waste-to-Energy incineration (WtE), coupled with Carbon Capture and Storage (CCS), especially Bioenergy with Carbon Capture and Storage (BECCS), suggest a way to simultaneously retrieve energy from the otherwise disposed waste and reduce CO2 emissions. Independent of one another, WtE, CCS, and BECCS are implemented in a few regions of the world, but their joint diffusion is uncommon in comparison. Regardless of how the future of their combined implementation unfolds, social acceptance is almost certainly expected to play a role in their diffusion. This thesis categorically identified overlaps and contrasts between factors that have been subjects of investigation in public acceptance research on WtE and CCS/BECCS’s social acceptance using Huijts et al’s (2012) energy technology acceptance framework. To allocate peer-reviewed international literature from the two fields for analysis, a systematic literature review was conducted. The results revealed that WtE and CCS/BECCS studies most commonly measured the public’s wide array of emotional experiences regarding the respective technologies and addressed their knowledge and experience with the technologies to the smallest degree. Energy technology projects are bound to attract emotionally charged responses, thus the commonality to address affect makes probing for emotional responses possible. Whereas knowledge can be difficult to measure due to its subjectivity. Factors that were commonly measured in one field but not in the other included public perceptions of fairness, trust, and climate change. The findings provided a comprehensive overview of factors to take into consideration when collecting public opinions on a WtE incineration facility that is coupled with CCS technology.

The Paris Agreement has ambitious targets to limit the global warming below 1.5 °Cin the 21st century. This goal is reflected in the national climate targets, for example, Sweden aims to achieve net zero greenhouse gas emissions by 2045, and thereafter achieve negative emissions. One of the pivotal ways to achieve these goals is by applying the mature bioenergy with carbon capture and storage (BECCS) technology to large-scale industries that emit CO2. Around 6% of the global emissions arise from the pulp and paper industry making them one of the largest localized emitters of biogenic CO2. This makes them suitable for retrofitting BECCS technologies and post-combustion capture (PCC) is one among them. This study presents a techno-economic assessment of an absorption-based PCC unit in SCA Östrand pulp mill. Chemical absorption using MEA and chilled ammonia process (CAP) using NH3 as the solvent are considered in this study. For both the processes, mass and energy balances using Aspen HYSYS were done and validated against published data in literature. Heat integration by applying excess or waste heat from the mill is also considered in this work. CO2 capture from flue gas originating from various emission sources in the mill (recovery boiler, lime kiln and multi-fuel boiler) are considered in different combinations in the analysis. The main key performance indicator (KPI) evaluated in this work is the cost of CO2capture for all the different cases for both the MEA- and chilled NH3-based absorption processes. The minimum cost of CO2 capture for MEA-based absorption process was found to be in the range 37-41 €/tCO2 and for CAP, it was found to be in the range 73-81 €/tCO2. For MEA-based absorption process, the excess low pressure steam from the mill satisfies the steam demand in all the cases, except the one where CO2 is captured from all the three emission sources. For CAP, sufficient excess low pressure steam is present in the mill for all the capture cases due to a lower reboiler duty compared to MEA-based absorption process. An optimal process configuration and capture scenario for the existing design conditions in the mill are derived and justified. A sensitivity analysis was carried out to find the associated bottlenecks from the breakdown of the cost of CO2 capture for each process. The overall BECCS cost is also sensitive to CO2 transport & storage costs. However, it is also clear that incentives for negative emissions will make BECCS an attractive solution for the pulp and paper industry.