Multi-Attributional Decision Making in LCA & TEA for CCU: An Introduction to Approaches and a Worked Example

This worked example considers only the elements of the whole process relevant for the integrated assessment in greater detail. This worked example builds on a prior study, covering CO2 to methanol conversion, and as such a more detailed overview of the technology can be found there. A brief overview of the methanol technology is included for familiarization, along with details on the alignment approach taken to ensure that a ‘preference-based’ integration can be completed. The focus of this worked example is the application of multi-attribute decision making (MADM) approaches and their potential use within combined LCA & TEA studies. The practical part of this examples sees the application of one MADM method to a multi-criteria problem with relevancy in CCU that utilizes the outputs of both an LCA & TEA study.This worked example has been released at an intermediate timeframe within the CO2nsistent project, fitting in between the release of version 1.1 and 2.0 of the ‘Techno-economic Assessment & Life Cycle Assessment Guidelines for CO2 Utilization’. This means the subject matter of this worked example (combined assessment, in particular multi-criteria approaches to decision analysis/making) remains to a degree uncovered by the overarching guidelines associated with this project until the release of version 2.0. As such this worked example will include more contextual sections than has been typical in previous examples, in part bridging the gap until a more detailed guidance section on combined assessment can be included in version 2.0. This does not mean that no guidance can be drawn from version 1.1 of the guidelines document in the intermediate timeframe. Version 1.1 contains some guidance on both combined LCA & TEA studies (see section A) and the individual TEA section itself also contains a brief section and guideline rules on multicriteria decision analysis (MCDA) for use within the field. Ultimately this guidance is useful even for application in a combined study, as ultimately the same concept applies with the complication of needing to ensure that both the LCA & TEA study are aligned with suitable precision.Global CO2 InitiativeEIT Climate-KIChttp://deepblue.lib.umich.edu/bitstream/2027.42/167009/3/Multi-attributional Decision-Making in LCA and TEA for CCU and Worked Example.pdfedda1cab-631e-4ed3-880e-28ed1c223cc3SEL

SNG Worked Example for the TEA Guidelines for CO2 Utilization

To meet the high demand for natural gas globally, synthetic natural gas (SNG) can be produced as a substitute for natural gas derived from fossil fuels. Nevertheless, the traditional SNG production process is highly carbon-intensive. In the framework of the Power-to-Gas concept, production of SNG can occur via hydrogenation of CO2, which can be captured from industrial sources. As a result, the reliance of SNG production on fossil fuels can be reduced and, subsequently, associated CO2 emissions can be controlled. The goal of the present study is to assess the technical viability and economic feasibility of producing SNG via CO2 hydrogenation. Additionally, to prepare for integrating the techno-economic analysis (TEA) with a life-cycle assessment (LCA), the challenges and pitfalls of such integration are also discussed. The TEA in this study was carried out mainly from a research & development perspective. The production cost for SNG based on carbon capture and utilization (CCU) is estimated and key cost drivers are identified. The combined indicator of CO2 abatement cost is also estimated as a quantitative indicator for assessing the TEA and LCA results. The methanation plant is assumed to be located next to an iron & steel plant in Germany, from which the CO2 feedstock for producing SNG is by means of MEA-based chemical absorption technology, while the hydrogen (which is produced via electrolysis using surplus electricity) is purchased from a production facility located 250 km away. The output capacity of the methanation plant is 148 MW. Aspen Plus software was used for process modelling and data were taken from the literature. Through discussions, it was found that setting the system boundaries was a central challenge for aligning the TEA and LCA. While LCA tends towards encompassing the full life cycle of products (cradle-to-grave or -gate), it is not necessary to include the upstream and downstream processes to conduct a TEA in the present study. The information on upstream processes is reflected in the characteristics of the input flows entering the product system. Setting identical system boundaries for TEA and LCA would require solving problems of multi-functionality, which can be very challenging for TEA when the market for the products to be analyzed is still uncertain. To align inventories, the relevant environmental parameters (e.g., CO2 emissions) should be documented in addition to the technical and economic parameters. For calculating CO2 abatement cost, system expansion can be used to account for the reduced CO2 emissions, or the CO2 feedstock can be regarded as negative emissions. The results show that the SNG production cost for the analyzed product system is 0.0748 €/MJ and the minimum selling price is 0.271 €/kWh. The production cost is more than 10 times greater than that of the benchmark product (coal-based SNG). The selling price of SNG produced by the proposed system is also significantly higher than that of natural gas in the German market. The CO2 abatement cost, as a combined indicator of TEA & LCA, was calculated as 0.75 €/kgCO2. Sensitivity analysis reveals that the hydrogen purchase price represents the most significant uncertainty for the analyzed system. At a 95% confidence interval, the estimated production cost ranges between 0.065 and 0.173 €/MJSNG. Current legislation of the European Union Emissions Trading Scheme (EU ETS) is found to be inapplicable to the product system investigated. Thus, the analyzed CCU system cannot benefit from the emissions trading scheme. To drive CCU-based SNG forward in the future market, it is essential to reduce the production cost of hydrogen.Global CO2 InitiativeEIT Climate-KIChttp://deepblue.lib.umich.edu/bitstream/2027.42/167382/1/TEA of Synthetic Natural Gas production - worked example.pdfDescription of TEA of Synthetic Natural Gas production - worked example.pdf : Report documentSEL

Techno-Economic Assessment & Life Cycle Assessment Guidelines for CO2 Utilization (Version 1.1)

Climate change is one of the greatest challenges of our time. Under the auspices of the UN Framework Convention on Climate Change and through the Paris Agreement, there is a commitment to keep global temperature rise this century to well below two degrees Celsius compared with pre-industrial levels. This will require a variety of strategies, including increased renewable power generation, broad-scale electrification, greater energy efficiency, and carbon-negative technologies. With increasing support worldwide, innovations in carbon capture and utilization (CCU) technologies are now widely acknowledged to contribute to achieving climate mitigation targets while creating economic opportunities. To assess the environmental impacts and commercial competitiveness of these innovations, Life Cycle Assessment (LCA) and Techno-Economic Assessment (TEA) are needed. Against this background, guidelines (Version 1.0) on LCA and TEA were published in 2018 as a valuable toolkit for evaluating CCU technology development. Ever since, an open community of practitioners, commissioners, and users of such assessments has been involved in gathering feedback on the initial document. That feedback has informed the improvements incorporated in this updated Version 1.1 of the Guidelines. The revisions take into account recent publications in this evolving field of research; correct minor inconsistencies and errors; and provide better alignment of TEA with LCA. Compared to Version 1.0, some sections have been restructured to be more reader-friendly, and the specific guideline recommendations are renamed ‘provisions.’ Based on the feedback, these provisions have been revised and expanded to be more instructive.Global CO2 Initiative at the University of MichiganEIT Climate-KIChttp://deepblue.lib.umich.edu/bitstream/2027.42/162573/5/TEA&LCA Guidelines for CO2 Utilization v1.1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162573/7/ESI reference scenario data_Corrected.xlsxSEL

Stoichiometry-Based Estimation of Climate Impacts of Emerging Chemical Processes: Method Benchmarking and Recommendations

Current chemical process development aims to improve sustainability. Decision-making thus needs to assess potential environmental benefits. However, reliable life cycle inventory data is often unavailable at early design stages. Without process information, life cycle assessment practitioners usually estimate inventories solely based on the reaction stoichiometry and proxies for energy and utility demands. However, the quality of these proxies has not been tested on a comprehensive data set. In this study, we compare and benchmark stoichiometry-based estimation methods that employ proxies for the yield and utility demands from the literature to a new benchmark database of 474 processes. This benchmark data set is based on industrially validated processes from the Process Economics Program (PEP) yearbook. Most estimation methods are shown to underestimate the global warming impact. We found that the yield range assumed by Geisler et al. (2004) closely reflects the actual raw material demands, while the average process energy demands, calculated by Kim and Overcash (2003), perform best as a proxy for energy demands. Thus, we propose to combine both proxies to improve predictions of the inventory data and the overall global warming impact.ISSN:2168-048

Stoichiometry-Based Estimation of Climate Impacts of Emerging Chemical Processes: Method Benchmarking and Recommendations

Current chemical process development aims to improve sustainability. Decision-making thus needs to assess potential environmental benefits. However, reliable life cycle inventory data is often unavailable at early design stages. Without process information, life cycle assessment practitioners usually estimate inventories solely based on the reaction stoichiometry and proxies for energy and utility demands. However, the quality of these proxies has not been tested on a comprehensive data set. In this study, we compare and benchmark stoichiometry-based estimation methods that employ proxies for the yield and utility demands from the literature to a new benchmark database of 474 processes. This benchmark data set is based on industrially validated processes from the Process Economics Program (PEP) yearbook. Most estimation methods are shown to underestimate the global warming impact. We found that the yield range assumed by Geisler et al. (2004) closely reflects the actual raw material demands, while the average process energy demands, calculated by Kim and Overcash (2003), perform best as a proxy for energy demands. Thus, we propose to combine both proxies to improve predictions of the inventory data and the overall global warming impact

Stoichiometry-Based Estimation of Climate Impacts of Emerging Chemical Processes: Method Benchmarking and Recommendations

Current chemical process development aims to improve sustainability. Decision-making thus needs to assess potential environmental benefits. However, reliable life cycle inventory data is often unavailable at early design stages. Without process information, life cycle assessment practitioners usually estimate inventories solely based on the reaction stoichiometry and proxies for energy and utility demands. However, the quality of these proxies has not been tested on a comprehensive data set. In this study, we compare and benchmark stoichiometry-based estimation methods that employ proxies for the yield and utility demands from the literature to a new benchmark database of 474 processes. This benchmark data set is based on industrially validated processes from the Process Economics Program (PEP) yearbook. Most estimation methods are shown to underestimate the global warming impact. We found that the yield range assumed by Geisler et al. (2004) closely reflects the actual raw material demands, while the average process energy demands, calculated by Kim and Overcash (2003), perform best as a proxy for energy demands. Thus, we propose to combine both proxies to improve predictions of the inventory data and the overall global warming impact

Stoichiometry-Based Estimation of Climate Impacts of Emerging Chemical Processes: Method Benchmarking and Recommendations

Current chemical process development aims to improve sustainability. Decision-making thus needs to assess potential environmental benefits. However, reliable life cycle inventory data is often unavailable at early design stages. Without process information, life cycle assessment practitioners usually estimate inventories solely based on the reaction stoichiometry and proxies for energy and utility demands. However, the quality of these proxies has not been tested on a comprehensive data set. In this study, we compare and benchmark stoichiometry-based estimation methods that employ proxies for the yield and utility demands from the literature to a new benchmark database of 474 processes. This benchmark data set is based on industrially validated processes from the Process Economics Program (PEP) yearbook. Most estimation methods are shown to underestimate the global warming impact. We found that the yield range assumed by Geisler et al. (2004) closely reflects the actual raw material demands, while the average process energy demands, calculated by Kim and Overcash (2003), perform best as a proxy for energy demands. Thus, we propose to combine both proxies to improve predictions of the inventory data and the overall global warming impact

Life-Cycle and Techno-Economic Assessment of Early-Stage Carbon Capture and Utilization Technologies - A Discussion of Current Challenges and Best Practices

The mitigation of climate change requires research, development, and deployment of new technologies that are not only economically viable but also environmentally benign. Systematic and continuous technology assessment from early technology maturity onwards allows assessment practitioners to identify economic and environmental characteristics. With this information, decision-makers can focus time and resources on the most promising technologies. A broad toolset for technology assessment exists—stretching from the well-established life cycle assessment (LCA) methodology to more loosely defined techno-economic analysis (TEA) methods and the increasingly popular principles of technology maturity assessment such as the concept of technology readiness levels (TRL). However, current technology assessment practice faces various challenges at early stages, resulting in a potential mismatch of study results and stakeholders' needs and an escalation of assessment effort. In this practice review, we outline current challenges in the interplay of LCA, TEA, and TRL and present best practices for assessing early-stage climate change mitigation technologies in the field of carbon capture and utilization (CCU). The findings help practitioners systematically identify the TRL of a technology and adapt technology assessment methodologies accordingly. We highlight the methodological challenges for practitioners when adapting the goal and scope, identifying benchmark technologies, creating a comprehensive inventory, comparing early stage to commercial stage, ensuring clarity of recommendations for decision-making under high uncertainty, and streamlining conventional LCA and TEA assessment approaches and provide actionable recommendations. Overall, this work contributes to identifying promising technologies faster and more systematically, accelerating the development of new technologies for climate change mitigation and beyond.ISSN:2624-955

Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways

Comparisons of emerging carbon capture and utilization (CCU) technologies with equivalent incumbent technologies are necessary to support technology developers and to help policy-makers design appropriate long-term incentives to mitigate climate change through the deployment of CCU. In particular, early-stage CCU technologies must prove their economic viability and environmental reduction potential compared to already-deployed technologies. These comparisons can be misleading, as emerging technologies typically experience a drastic increase in performance and decrease in cost and greenhouse gas emissions as they develop from research to mass-market deployment due to various forms of learning. These changes complicate the interpretation of early techno-economic assessments (TEAs) and life cycle assessments (LCAs) of emerging CCU technologies. The effects of learning over time or cumulative production themselves can be quantitatively described using technology learning curves (TLCs). While learning curve approaches have been developed for various technologies, a harmonized methodology for using TLCs in TEA and LCA for CCU in particular is required. To address this, we describe a methodology that incorporates TLCs into TEA and LCA to forecast the environmental and economic performance of emerging CCU technologies. This methodology is based on both an evaluation of the state of the art of learning curve assessment and a literature review of TLC approaches developed in various manufacturing and energy generation sectors. Additionally, we demonstrate how to implement this methodology using a case study on a CO2 mineralization pathway. Finally, commentary is provided on how researchers, technology developers, and LCA and TEA practitioners can advance the use of TLCs to allow for consistent, high-resolution modeling of technological learning for CCU going forward and enable holistic assessments and fairer comparisons with other climate technologies.ISSN:2624-955