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Adaptive control for hybrid manufacturing

The primary objective of the technical collaboration is to validate and implement adaptive feed rate control, tool wear monitoring, and anomaly detection for the finish machining of additive components. Most additively manufactured components need finish machining to achieve surface finish and tolerance requirements. Distortions and surface roughness of additively manufactured components can result in high forces and tool breakage or excessive tool wear during post-additive finish machining, which can result in rework or part scrap. In this collaboration, methods, and models for feed rate control, tool wear monitoring, and anomaly detection via a digital twin will be evaluated and validated.

99 GENERAL AND MISCELLANEOUS↗

Experiments and Project-Based Enhancements for STEM Learning

Motivating K-12 students to pursue careers in science, technology, engineering, and mathematics (STEM) is an effort that requires consistent engagement. Throughout the K-12 student timeline, STEM educators need to continuously pivot their teaching and update their educational materials to motivate the next generation of students. Once students begin their undergraduate education, university professors need to encourage them to consider pursuing graduate studies to ensure that a qualified future workforce can be developed for research and teaching. In this paper, we present our work on developing STEM materials for K-12 student engagement. Our K-12 materials target the grid integration of hydrogen assets that are suitable to engage students in the classroom. Our undergraduate materials target hands-on projects and collaboration with industry to connect classroom learning with real-world applications and needs in renewable energy. Our work leverages available open-source models and tools to create projects for undergraduate students and motivate their interest in pursuing research topics in graduate-level education.

ENERGY PLANNING, POLICY, AND ECONOMY↗

W.A. Parish Post-Combustion CO{sub 2} Capture and Sequestration Project Phase 1 Definition

For a secure and sustainable energy future, the United States (U.S.) must reduce its dependence on imported oil and reduce its emissions of carbon dioxide (CO{sub 2}) and other greenhouse gases (GHGs). To meet these strategic challenges, the U.S. wiU have to create fundamentally new technologies with performance levels far beyond what is now possible. Developing advanced post-combustion clean coal technologies for capturing CO{sub 2} from existing coal-fired power plants can play a major role in the country's transition to a sustainable energy future, especially when coupled with CO{sub 2}-enhanced oil recovery (CO{sub 2}-EOR). Pursuant to these goals, NRG Energy, Inc. (NRG) submitted an application and entered into a cost-shared collaboration with the U.S. Department of Energy (DOE) under Round 3 of the Clean Coal Power Initiative (CCPI) to advance low-emission coal technologies. The objective of the NRG W A Parish Post-Combustion CO{sub 2} Capture and Sequestration Demonstration Project is to establish the technical feasibility and economic viability of post-combustion CO{sub 2} capture using flue gas from an existing pulverized coal-fired boiler integrated with geologic sequestration via an enhanced oil recovery (EOR) process. To achieve these objectives, the project will be executed in three phases. Each phase represents a distinct aspect of the project execution. The project phases are: • Phase I. Project Definition/Front-End Engineering Design (FEED) • Phase ll. Detailed Engineering, Procurement & Construction • Phase III. Demonstration and Monitoring The purpose of Phase I is to develop the project in sufficient detail to facilitate the decision-making process in progressing to the next stage of project delivery. Phase n. This report provides a complete summary of the FEED study effort, including pertinent project background information, the scope of facilities covered, decisions, challenges, and considerations made regarding configuration and performance of the facility, along with the conceptual design and estimate results. The findings of this report should be considered conceptual in nature and are conditioned on the statements contained herein. The cost of preparing this report (including the FEED study described herein) was funded in part by a $167-million grant provided by the U.S. DOE.

20 FOSSIL-FUELED POWER PLANTS↗

Bridging the PSI Knowledge Gap: A Multi-Scale Approach

Plasma-surface interactions (PSI) pose an immense scientific hurdle in magnetic confinement fusion and our present understanding of PSI in confinement environments is highly inadequate; indeed, a recent Fusion Energy Sciences Advisory Committee report found that 4 out of the 5 top five fusion knowledge gaps were related to PSI. The time is appropriate to develop a concentrated and synergistic science effort that would expand, exploit and integrate the wealth of laboratory ion-beam and plasma research, as well as exciting new computational tools, towards the goal of bridging the PSI knowledge gap. This effort would broadly advance plasma and material sciences, while providing critical knowledge towards progress in fusion PSI. This project involves the development of a Science Center focused on a new approach to PSI science; an approach that both exploits access to state-of-the-art PSI experiments and modeling, as well as confinement devices. The organizing principle is to develop synergistic experimental and modeling tools that treat the truly coupled multi-scale aspect of the PSI issues in confinement devices. This is motivated by the simple observation that while typical lab experiments and models allow independent manipulation of controlling variables, the confinement PSI environment is essentially self-determined with few outside controls. This means that processes that may be treated independently in laboratory experiments, because they involve vastly different physical and time scales, will now affect one another in the confinement environment. Also, lab experiments cannot simultaneously match all exposure conditions found in confinement devices typically forcing a linear extrapolation of lab results. At the same time programmatic limitations prevent confinement experiments alone from answering many key PSI questions. The resolution to this problem is to usefully exploit access to PSI science in lab devices, while retooling our thinking from a linear and de-coupled extrapolation to a multi-scale, coupled approach. The PSI Plasma Center consisted of three equal co-centers; one located at the MIT Plasma Science and Fusion Center, one at UC San Diego Center for Energy Research and one at the UC Berkeley Department of Nuclear Engineering, which moved to the University of Tennessee, Knoxville (UTK) with Professor Brian Wirth in July 2010. The Center had three co-directors: Prof. Dennis Whyte led the MIT co-center, the UCSD co-center was led by Dr. Russell Doerner, and Prof. Brian Wirth led the UCB/UTK center. The directors have extensive experience in PSI and material research, and have been internationally recognized in the magnetic fusion, materials and plasma research fields. The co-centers feature keystone PSI experimental and modeling facilities dedicated to PSI science: the DIONISOS/CLASS facility at MIT, the PISCES facility at UCSD, and the state-of-the-art numerical modeling capabilities at UCB/UTK. A collaborative partner in the center is Sandia National Laboratory at Livermore (SNL/CA), which has extensive capabilities with low energy ion beams and surface diagnostics, as well as supporting plasma facilities, including the Tritium Plasma Experiment, all of which significantly augment the Center. Interpretive, continuum material models are available through SNL/CA, UCSD and MIT. The participating institutions of MIT, UCSD, UCB/UTK, SNL/CA and LLNL brought a formidable array of experimental tools and personnel abilities into the PSI Plasma Center. Our work has focused on modeling activities associated with plasma surface interactions that are involved in effects of He and H plasma bombardment on tungsten surfaces. This involved performing computational material modeling of the surface evolution during plasma bombardment using molecular dynamics modeling. The principal outcomes of the research efforts within the combined experimental – modeling PSI center are to provide a knowledgebase of the mechanisms of surface degradation, and the influence of the surface on plasma conditions.

36 MATERIALS SCIENCE↗

Prototyping and Manufacturing of Magnetic Gearbox Components using Innovations in Castings

In a previous collaboration with Emrgy (CRADA agreement NFE-17-06532), a housing component of Emrgy’s 10 kW gearbox was casted out of aluminum alloys using impression molds prepared using 3D printed techniques. (Henderson, 2018). After comparison with Emrgy’s existing hardware and material testing, the 3D printed impression molds were verified to represent a valid approach to advance technology readiness and commercialize Emrgy’s technology.

13 HYDRO ENERGY↗

Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, in the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.

01 COAL, LIGNITE, AND PEAT↗

Achieving Energy Savings in Municipal Construction in Long Beach, CA

Long Beach Gas and Oil (LBGO), the public gas utility in Long Beach, California, partnered with the U.S. Department of Energy (DOE) to develop and implement solutions to build a new, low-energy modular office building that is at least 50% below requirements set by Energy Standard 90.1-2007 of the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE), the American National Standards Institute (ANSI), and the Illuminating Engineering Society of America (IESNA) as part of DOE’s Commercial Building Partnerships (CBP) program.3 The LBGO building, which demonstrates that modular construction can be very energy efficient, is expected to exceed the ASHRAE baseline by about 45%. The new 15,000-square foot (ft2) LBGO office building has two stories and houses private offices, open-plan cubicle offices, and a conference room and call center on the second floor. The building’s modular nature allowed LBGO to realize the cost benefits of fasttracked construction while saving substantial energy and reducing operational costs. The project was funded by the utility’s ratepayer revenue, which imposed a tight budget limit. The design process was a collaborative effort involving LBGO and its design-build team, Lawrence Berkeley National Laboratory (Berkeley Lab), and subcontractors Stantec (formerly Burt Hill) and LHB Inc. The team proposed efficiency measures based on computer modeling of the building in full compliance with ASHRAE 90.1-2007; in the modeled building, the lighting and cooling systems were the largest energy users, so increasing the efficiency of these systems was a top priority. Promising measures were modeled to estimate their energy performance, and each measure was evaluated for its feasibility within the budget.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Evaluation of the Hanford 200 West Groundwater Treatment System: Fluidized Bed Bioreactor

A fluidized bed reactor (FBR) in the 200W water treatment facility at Hanford is removing nitrate from groundwater as part of the overall pump-treat-reinject process. Control of the FBR bed solids has proven challenging, impacting equipment, increasing operations and maintenance (O&M), and limiting the throughput of the facility. In response to the operational challenges, the Department of Energy Richland Office (DOE-RL) commissioned a technical assistance team to facilitate a system engineering evaluation and provide focused support recommendations to the Hanford Team. The DOE Environmental Management (EM) technical assistance process is structured to identify and triage technologies and strategies that address the target problem(s). The process encourages brainstorming and dialog and allows rapid identification and prioritization of possible options. Recognizing that continuous operation of a large-scale FBR is complex, requiring careful attention to system monitoring data and changing conditions, the technical assistance process focused on explicit identification of the available control parameters (“knobs”), how these parameters interact and impact the FBR system, and how these can be adjusted under different scenarios to achieve operational goals. The technical assistance triage process was performed in collaboration with the Hanford team.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NNSA/CEA Workflow Workshop Report 2021

Researchers from three NNSA labs plus CEA recently met for a half-day workshop on scientific and engineering workflows in March of 2021. Tools, projects and use cases from each institution were described. A wide range of unique capabilities and requirements were represented. The workshop highlights the fact that the CEA/NNSA workflows space mirrors the broader open science workflows community, with multiple technologies under development in a number of science domains and under a number of funding streams, with differing capabilities and focuses. Despite the number of tools, presentations and discussions have shown that these tools cover specific mission spaces at the four labs, each with distinctive capabilities that do not completely overlap with each other. We believe there is a strong interest in the short term for sharing lessons learned and collaborating on benchmarks and site evaluation of projects. This report, prepared by the NNSA/CEA Workflows Working Group, briefly summarizes the presentations in the areas of domain specific workflows, end user environments, data management, job and resource management, and infrastructure, and then identifies six broad areas for potential collaboration. A key finding is that users could benefit from greater interoperability, compatibility, and composability of the workflow technologies under development and that point-to-point collaboration opportunities should be identified to explore these aspects.

97 MATHEMATICS AND COMPUTING↗

Fiber Based Optical Amplifier for High Energy Laser Pulses Final Report CRADA No. TC02100.0

This was a collaborative effort between Lawrence Livermore National Security, LLC (formerly The Regents of the University of California)/Lawrence Livermore National Laboratory (LLNL), and The Boeing Company to develop an optical fiber-based laser amplifier capable of producing and sustaining very high-energy, nanosecond-scale optical pulses. The overall technical objective of this CRADA was to research, design, and develop an optical fiber-based amplifier that would meet specific metrics.

42 ENGINEERING↗

Process Capability Study on the Volume Manufacturing of a Complex Rotating Automotive Component in Laser Powder Bed Fusion Additive Manufacturing

Oak Ridge National Lab (ORNL) and Ford Motor Company used an AddUp FormUp 350 to print a turbo wheel geometry to show feasibility of laser powder bed fusion (L-PBF) additive manufacturing (AM) in producing cost-effective components for automotive industries. L-PBF AM has potential to disrupt traditional manufacturing techniques by introducing novel materials and geometries that cannot otherwise be produced. Fundamental challenges associated with L-PBF include limitations on printing overhang geometries and resulting surface finish. In this collaboration, ORNL and Ford partnered with AddUp to show the viability of using L-PBF for an AM design turbine wheel while reducing as-fabricated surface roughness. A cost model associated with mass production was produced to analyze benefits of pursing LPBF AM compared to investment casting.

36 MATERIALS SCIENCE↗

MAUS: the MICE analysis user software

The Muon Ionization Cooling Experiment (MICE) collaboration has developed the MICE Analysis User Software (MAUS) to simulate and analyze experimental data. It serves as the primary codebase for the experiment, providing for offline batch simulation and reconstruction as well as online data quality checks. The software provides both traditional particle-physics functionalities such as track reconstruction and particle identification, and accelerator physics functions, such as calculating transfer matrices and emittances. The code design is object orientated, but has a top-level structure based on the Map-Reduce model. This allows for parallelization to support live data reconstruction during data-taking operations. MAUS allows users to develop in either Python or C++ and provides APIs for both. Various software engineering practices from industry are also used to ensure correct and maintainable code, including style, unit and integration tests, continuous integration and load testing, code reviews, and distributed version control. The software framework and the simulation and reconstruction capabilities are described.

47 OTHER INSTRUMENTATION↗

US Hybrid Northeast Demonstration and Deployment of FCRxNV200 (DE-EE0007276 Final Report)

In this project, US Hybrid and other strategic project partners, collaborated to build a fuel cell range extended plug-in hybrid utility vehicle and demonstrate it in real-world operation. The base vehicle platform to be modified to the hybrid platform was the Nissan e-NV200, which is an all-electric version of the NV200 commercial cargo van. This is a Class 1 vehicle with a GVWR of approx. 4,900 lbs., wheelbase of 115 inches, and cargo capacity of approx. 122ft. The 80kW/254N-m electric powertrain is the same as Nissan’s LEAF, but with a revised final drive ratio (9.301 vs. 8.194) to accommodate higher payloads with lower top speed. US Hybrid utilized a purpose built, freeze capable, integrated fuel cell engine FCe™10 to hybridize and range-extend the existing e-NV200 vehicle with OEM drivetrain. The eNV200 has the same size battery as the LEAF with a different rear axle ratio. Fuel cell stack size and balance of plant (BOP) configuration were designed for compact under the hood vehicle packaging. Engine design took into consideration the need for high efficiency (>50% at all loads & up to 62% at partial power), optimal heat recovery for cabin heating, and low maintenance. The technology of the isolated DC-DC converter operating at >100kHz enables extremely high efficiency (ZVS and SIC power devices) and robust performance while reducing system complexity, weight, and volume. This enables an extended vehicle range of up to 250 miles and an operational life that should provide lower total cost of ownership (TCO) to the end user.

33 ADVANCED PROPULSION SYSTEMS↗

Report 1: International Cooperation to Development of Strategy and R&D Collaboration for Substitution of Rare Earth Resources and Report 2: Development of Magnetocaloric Alloys without Critical Elements

A Memorandum of Understanding was signed between the Ames Laboratory and Korea Institute of Industrial Technology (KITECH) in April of 2011 to establish a close cooperative relationship of research on Rare Metals. The Ames Laboratory through its operating contractor, Iowa State University, and the KITECH, as one of the goals of a joint Memorandum of Understanding, made agreement on a Cooperative Research and Development Agreement (CRADA) for a personnel exchange in April of 2012. Ames Laboratory and KITECH would be jointly performing collaboration work in this cooperation as the Parties. The individual backgrounds and capabilities of the Parties are ideally suited to the successful execution of this work. The Ames Laboratory has a history spanning more than 60 years of pioneering work in rare-earth (RE) research. With its team of internationally recognized rare-earth experts, the Ames Laboratory is uniquely positioned to provide the knowledge, expertise and training necessary to help ensure a global leadership position for the United States in rare-earth research, development and applications. The KITECH was founded to promote technological capability of small and medium-sized enterprises in Korea and has a mission of supporting research on materials science and engineering in Korea. In 2009, Korea Institute for Rare Metals (KIRAM) at KITECH was established. KIRAM makes selection and funding decisions for technology projects for strategic rare metals, and provides oversight of R&D programs. The KIRAM is focusing on the commercialization and recycling of particular rare metals. In addition KIRAM will be coordinating government, local universities, and small to medium enterprises to facilitate R&D support for core technologies. KIRAM will be carrying them through to commercialization, and establishing new industry.

36 MATERIALS SCIENCE↗

OC6 Phase III: Validation of Wind Turbine Aerodynamic Loading During Surge/Pitch Motion

The objective of Phase III of the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) project was to validate the accuracy of aerodynamic load predictions by offshore wind modeling tools for a floating offshore wind turbine as it experiences large surge-translational and pitch-rotational motion, as would occur during normal operation. The test data considered were generated at Politecnico di Milano – a wind tunnel with a robotic excitation system to emulate wave loading on the wind turbine. Testing was performed using a scaled version of the DTU 10-MW reference model, and motion was prescribed as harmonic oscillations in the surge and pitch directions, independently. A variety of models were examined in the project, performing a three-way validation between engineering-level tools, higher-fidelity tools, and measurement data from two wind tunnel experimental campaigns. The Load Cases (LC) considered in this testing are as follows: LC 1.X - Steady Wind LC 2.X - Unsteady Wind - Surge Motion LC 3.X - Unsteady Wind - Pitch Motion Details on the results of the OC6 Phase III campaign can be found in the following reference: Bergua R., et al. OC6 project Phase III: validation of the aerodynamic loading on a wind turbine rotor undergoing large motion caused by a floating support structure, Wind Energ. Sci., 8, 465–485, https://doi.org/10.5194/wes-8-465-2023, 2023.

17 WIND ENERGY↗

Development of pyro-processing technology at CRIEPI for carving out the future of nuclear fuel cycle

Pyro-processing has been attracting increasing attention as a promising candidate as an advanced nuclear fuel cycle technology. It provides economic advantage as well as reduction in proliferation risk and burden of long live radioactive waste, especially when it is combined with advanced fuels such as metallic or nitride fuel which gives excellent burning efficiency of minor actinides (MA). CRIEPI has been developing pyro-processing technology since late eighties with both domestic and international collaborations. In the early stage, electrochemical and thermodynamic properties in LiCl-KCl eutectic melt, and fundamental feasibility of core technology like electrorefining were chiefly investigated. Currently, stress in the process chemistry development is also placed on supporting technologies, such as treatment of anode residue and high temperature distillation for cathode product from electrorefining, and so on. Waste treatment process development, such as studies on adsorption behavior of various FP elements into zeolite and conditions for the fabrication of glass-bonded sodalite waste form, are steadily improved as well. In parallel, dedicated pyro-processing equipment such as zeolite column for treatment of spent electro-refiner salt is currently in progress. Recently, an integrated engineering-scale fuel cycle tests were performed funded by Japanese government (MEXT) as an important step before proceeding to large scale hot demonstration of pyro-processing. Oxide fuels can be readily introduced into the pyro-processing by reducing them to metals by adoption of electrochemical reduction technique. Making use of this advantage, the pyro-processing is currently under preliminary evaluation for its applicability to the treatment of the corium, mainly consisting of (U,Zr)O{sub 2}, formed in different composition during the accident of the Fukushima Daiichi nuclear power plant. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Integrating CO2 Electrolysis with Gas Fermentation to Produce Valuable Fuels and Chemicals

Many industrial activities squander CO2, decreasing process yield. We envision a future where this waste carbon is instead captured, upgraded, and valorized directly at the point of emission. Within the CO2 Reduction and Upgrading Consortium (a collaboration of seven US national laboratories and industrial partners), we are pursuing this goal by developing and de-risking new technologies for low temperature CO2 electrolysis, coupled with biological upgrading of intermediates into more valuable compounds. One such process involves electrocatalytic reduction of CO2 to generate carbon monoxide (CO). As both a carbon and energy source, CO represents an attractive feedstock for microbial upgrading by certain syngas-fermenting species, such as the autotrophic bacterium Clostridium autoethanogenum. Our team has developed new genetic tools and optimized cultivation techniques to enhance C. autoethanogenum as a platform host for the biological conversion of syngas into value-added products. For example, we have created novel CRISPR-based genetic engineering techniques to build new, genome-reduced, platform strains of C. autoethanogenum with improved growth rates. Further, we ve introduced heterologous biochemical pathways into C. autoethanogenum to enable the production of high-value compounds from syngas, such as the isoprenoid precursor mevalonic acid. With the tools of electrochemistry and synthetic biology, there is virtually no limit to the spectrum of products that could be sustainably manufactured from CO2.

09 BIOMASS FUELS↗