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At least 37 records · Page 2

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Towards this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. In conclusion, two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2).

97 MATHEMATICS AND COMPUTING↗

Front End Engineering Design of Linde-BASF Advanced Post-Combustion CO 2 Capture Technology at a Southern Company Natural Gas-Fired Power Plant (Final Scientific/Technical Report)

This document details the execution of Cooperative Agreement DE-FE0031847, “Front End Engineering Design of Linde-BASF Advanced Post-Combustion Carbon Dioxide (CO 2 ) Capture Technology at a Southern Company Natural Gas-Fired Power Plant” during the period of 10/1/2019 to 6/30/2022. The project was funded by the U.S. Department of Energy’s Office of Fossil Energy and Carbon Management (FECM) and managed by the National Energy Technology Laboratory (NETL). Southern Company Services, Inc. (SCS) was the prime recipient and led the project team. Other members of the project team included Linde, Inc. (Linde), Linde Engineering – Dresden (LED), and BASF. The overall goal of the project was to complete a front-end engineering design (FEED) study for installing the Linde-BASF post-combustion capture (PCC) technology at an existing domestic natural gas-fired combined cycle (NGCC) power plant within Southern Company’s portfolio of assets. The CO 2 capture plant was to be of commercial scale (at least 375 MWe) and include process units for pre-conditioning of the flue gas system, the CO 2 capture plant island, storage vessels, the CO 2 compression train, and any necessary components for integration into the NGCC plant. Mississippi Power’s Plant Daniel Unit #4 was chosen as the host site for the FEED with the target of capturing 90% of CO 2 emissions from the existing combustion turbines. The information produced by the FEED was used to develop a cost estimate of +/- 15% accuracy. Capital costs, excluding financing, are estimated at approximately $\$752$ million dollars (2021). The execution of a project based on this FEED study has an estimated duration of almost five years.

03 NATURAL GAS↗

Alaska Liquid Natural Gas Pipeline Front-End Engineering & Design (Final Technical Report)

The Alaska Gasline Development Corporation (AGDC) is Alaska’s natural gas infrastructure development corporation established in 2013. AGDC’s mission is to maximize the benefit of Alaska’s vast North Slope natural gas resources for Alaskans through the development of infrastructure necessary to move the gas into local and international markets. AGDC was identified for a Congressionally Directed Spending (CDS) project for funding in the Energy and Water Development and Related Agencies Appropriations Act, 2023 under the heading: “Congressionally Directed Energy Efficiency and Renewable Energy Projects.” The CDS included $\$$4,000,000 of direct funding, with required match funds, to move the project forward. Alaska’s North Slope holds America’s largest proven and conventional natural gas supply. The integrated Alaska LNG Project will deliver 3.5 billion cubic feet of natural gas per day from Alaska’s North Slope gas fields to Alaskans as well as to a marine terminal located at tidewater in Cook Inlet. Alaska LNG is an integrated gas infrastructure project with three major components: a gas treatment plant (GTP) located at Prudhoe Bay, an 807-mile (1,287 km) gas pipeline (Mainline Pipeline) to Southcentral Alaska with interconnections for in-state gas use, and a natural gas liquefaction facility (LNG Facility) in Nikiski, Alaska. The integrated Alaska LNG Project has several strategic advantages including proven gas resources, existing upstream infrastructure, an advantageous arctic climate for LNG production, proximity to LNG markets, a track record of reliability from a state that first began exporting LNG to Japan in 1969, and broad support from Alaskans. North Slope natural gas is a conventional resource and can be produced with minimal drilling at a fraction of the carbon dioxide emissions of shale gas from the Lower 48 states. Through the development of the Alaska LNG Project, Alaska can provide energy security to Alaskans and a stable source of LNG to the Asia-Pacific region for generations. The Alaska LNG Project has been progressed through Pre-Front-End Engineering Design (Pre-FEED) and has obtained all major federal and State of Alaska permits and authorizations to construct the project, including the Federal Energy Regulatory Commission (FERC) Order Granting Authorization Under Section 3 of the Natural Gas Act. On September 5, 2024, the U.S. Department of Energy (DOE), National Energy Technology Laboratory (NETL) awarded Project No. DE-FE0032307 to AGDC with the objective to progress the project to Front-End Engineering Design (FEED) entry for the Alaska LNG Project Phase 1 Pipeline. The award Start Date was made effective July 1, 2023, with a Period of Performance through June 30, 2025. On March 27, 2025, AGDC announced the execution of definitive commercial agreements with Glenfarne Alaska LNG, LLC, an affiliate of Glenfarne Group, LLC, (together as “Glenfarne”), to lead the development of the Alaska LNG Project and enter FEED for the Phase 1 Pipeline. Project activities are now funded and directed by this private sector partner who holds a 75% interest in 8 Star Alaska, LLC (8 Star). 8 Star holds the assets of the Alaska LNG Project. As planned, AGDC continues to hold 25% minority interest in 8 Star and will play a governance role moving forward with Alaska LNG. This definitive commercial agreement milestone led to the successful completion of AGDC’s Statement of Project Objectives (SOPO) for FEED entry and led to the completion of DOE Project No. DE-FE0032307. At conclusion of the SOPO, AGDC also reached the award’s maximum federal cost share of $\$$4,000,000. AGDC is, therefore, providing Final Technical Report to close out DOE Project No. DE-FE0032307.

02 PETROLEUM↗

Cost details from front-end engineering design of piperazine with the advanced stripper

This Front-End Engineering Design (FEED) was funded by the U.S. Department of Energy (DOE) to estimate the cost to capture and compress 90% of the CO 2 from an existing natural gas combined cycle (NGCC) in Denver City, Texas, USA. The FEED used the PZAS (Piperazine with the Advanced Stripper) 2G amine scrubbing technology developed and modeled by The University of Texas at Austin. This FEED is unique in providing more public cost details than other FEEDs funded by DOE. The primary objective of the FEED was to provide a comprehensive estimate for the total installed cost of the capture plant. The estimated capital cost of NGCC at the Mustang Station is $\$727$ million for a capacity of 460 MW and 1.6 million tonnes CO 2 /yr. This includes a contingency of $\$104.6$ million and a contractor’s profit of $\$60.1$ million. The total direct field cost is $\$384.1$ million. With an optimistic fuel value of $\$3$/MMBtu, the estimated cost of capture varies from $\$85$/t at 4% IRR/85% load to $\$170$/t at 10% IRR/52% load. Air cooling is technically feasible but expensive. The air cooling systems for the water wash and pump-around intercooling account for 23.4% of the direct cost. The gas-fired boilers represent only 4% of the direct costs, but steam extraction would reduce energy cost, free up cooling water, and reduce the direct costs of processing additional flue gas and CO 2 from the boilers. The absorbers represent 9.6% of the direct cost in this FEED with no direct contact cooler and only 7.6 m of packing. Furthermore, the solvent cross exchangers are less expensive than expected (2.5% of direct costs). Doubling the number of these exchangers could reduce the heat duty from 3.0 to 2.5 GJ/t CO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Front-End Engineering Design Study for Retrofit Post-Combustion Carbon Capture on a Natural Gas Combined Cycle Power Plant

The objective of the project is to conduct a Front-End Engineering Design (FEED) study to determine the technical and economic feasibility of installing a retrofit, post-combustion, carbon capture facility on a commercially operating, natural gas-fired, combined cycle (NGCC) power plant. The Electric Power Research Institute (EPRI), California Resources Corporation (CRC), and Fluor Corporation used Fluor's Econamine FG Plus SM (EFG+) conducted the FEED study for capturing CO 2 produced by CRC's 550 MWe Elk Hills Power Plant (EHPP), located in the Elk Hills Oil Field near Tupman, Kern County, California. The EHPP was commissioned in 2003 and is powered by two General Electric 7FA gas turbines, with two heat recovery steam generators (HRSGs) providing steam to a General Electric D11 steam turbine. The target capture amount is 4,000 tonnes CO 2 /day for use in either enhanced oil recovery or dedicated geological saline storage located on CRC property at or nearby EHPP. This CO 2 is captured from a combination of the CO 2 emitted from the flue gas from EHPP and the flue gas generated from a natural gas-fired auxiliary boiler that supplies steam to the EFG+ process.

03 NATURAL GAS↗

Dataset for "Large Language Models as molecular design engines"

This dataset contains data and results associated with the paper "Large Language Models as molecular design engines" The paper investigates the use of large language models, specifically Claude 3 Opus, for generating and analyzing chemical structures based on various prompts from A-H (as mentioned in the manuscript), and guided design related to electron-withdrawing groups (EWG), electron-donating groups (EDG).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Carbon Capture Plant Front-End Engineering Design Study for Cement Manufacturing

The objective of this project was to conduct a Front-End Engineering Design (FEED) study for capturing carbon dioxide (CO₂) from cement plant flue gas using non-aqueous solvents (NAS) with a 95% capture efficiency. The study aimed to develop AACE (Association for the Advancement of Cost Engineering) Class 3 cost estimates for a commercial-scale CO₂ capture system with a capacity of 2.4 million tonnes of CO₂ per year (t-CO₂/yr). The results will provide Cemex and the U.S. Department of Energy with reliable data to support investment decisions, evaluate economic feasibility, and guide future project development and implementation.

20 FOSSIL-FUELED POWER PLANTS↗

Metabolite damage and repair in metabolic engineering design

The necessarily sharp focus of metabolic engineering and metabolic synthetic biology on pathways and their fluxes has tended to divert attention from the damaging enzymatic and chemical side-reactions that pathway metabolites can undergo. Although historically overlooked and underappreciated, such metabolite damage reactions are now known to occur throughout metabolism and to generate (formerly enigmatic) peaks detected in metabolomics datasets. It is also now known that metabolite damage is often countered by dedicated repair enzymes that undo or prevent it. Metabolite damage and repair are highly relevant to engineered pathway design: metabolite damage reactions can reduce flux rates and product yields, and repair enzymes can provide robust, host-independent solutions. Herein, after introducing the core principles of metabolite damage and repair, we use case histories to document how damage and repair processes affect efficient operation of engineered pathways - particularly those that are heterologous, non-natural, or cell-free. We then review how metabolite damage reactions can be predicted, how repair reactions can be prospected, and how metabolite damage and repair can be built into genome-scale metabolic models. Lastly, we propose a versatile 'plug and play' set of well-characterized metabolite repair enzymes to solve metabolite damage problems known or likely to occur in metabolic engineering and synthetic biology projects.

Enzyme promiscuity↗

Front-End Engineering Design (FEED) Study for a Carbon Capture Plant Retrofit to a Natural Gas-Fired Gas Turbine Combined Cycle Power Plant

A comprehensive front-end engineering design (FEED) study has been undertaken for a post-combustion capture (PCC) unit located at Panda’s Sherman natural gas–combined cycle (NGCC) power plant in Sherman, Texas. This is described in a full and unredacted FEED study report with all supporting documents, numbering over 150, also publicly available.

20 FOSSIL-FUELED POWER PLANTS↗

Scaleup and Site-Specific Engineering Design for Global Thermostat Direct Air Capture Technology

The overall goal of the project is completion of an initial design of a commercial-scale, Carbon Capture, Utilization, and Storage Direct Air Capture (CCUS-DAC) plant design at three different sites that captures a net of at least 100,000 tonnes per year (TPY) carbon dioxide (CO 2 ) from the atmosphere and considers compression and conditioning of the captured CO 2 for purpose of pipeline transportation to different geological formation sites for deep well injection and underground storage. In addition to the leading system consisting of a scaled-up Global Thermostat DAC unit, overall plant design includes a combined Heat and Power (CHP) unit integrated with a conventional liquid amine-based carbon capture system (90% capture) and compression facilities. These are considered balance of plant (BOP) systems and are required to provide low carbon-intensity steam and power to the DAC process. A second approach involving provision of DAC units modified to directly capture emissions from the CHP unit was initially assessed as an alternative and it was determined to be less mature than considered approach and not included in the scaled-up plant design efforts. Three geographically diverse continental United States locations were selected to better understand the effect of local/regional ambient conditions on scaled-up DAC system performance and project costs: Bucks, Alabama (hot wet climate), Odessa, Texas (dry hot climate), and Goose Creek, Illinois (mid continental climate). The team focused on initial engineering design activities, including the development of project design criteria, initiation of site-specific studies and investigations, completion of DAC system process and equipment design, and definition of balance of plant (BOP) engineering. The purpose of the activities were to develop Technoeconomic Analysis (TEA), Life-Cycle Analysis (LCA), and Environmental, Health, and Safety (EH&S) analysis, and Business Case Analysis (BCA) to validate that the project engineering and scale-up plans of the DAC systems, at each of the three distinct case studies considered, are technically, economically, and environmentally feasible for commercial-scale operation.

20 FOSSIL-FUELED POWER PLANTS↗

Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization

In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computational cost associated with engine design optimization. However, such methods still suffer from drawbacks. One main disadvantage is that the default machine learning (ML) hyperparameters are often severely suboptimal for a given problem. This has often been addressed by manually trying out different hyperparameter settings, but this solution is ineffective in case of a high-dimensional hyperparameter space. Besides this problem, the amount of data needed for training is also not known a priori. In response to these issues that need to be addressed, the present work describes and validates an automated active learning approach, AutoML-GA, for surrogate-based optimization of internal combustion engines. In this approach, a Bayesian optimization technique is used to find the best machine learning hyperparameters based on an initial dataset obtained from a small number of CFD simulations. Subsequently, a genetic algorithm is employed to locate the design optimum on the ML surrogate surface. In the vicinity of the design optimum, the solution is refined by repeatedly running CFD simulations at the projected optima and adding the newly obtained data to the training dataset. It is demonstrated that AutoML-GA leads to a better optimum with a lower number of CFD simulations, compared to the use of default hyperparameters. The proposed framework offers the advantage of being a more hands-off approach that can be readily utilized by researchers and engineers in industry who do not have extensive machine learning expertise.

Owoyele, Opeoluwa↗

Engineering Design of a Polaris Membrane CO 2 Capture System at a Cement Plant

Sargent & Lundy (S&L) and Membrane Technology and Research, Inc. (MTR) in cooperation with CEMEX, completed an initial engineering design study to evaluate the retrofit of MTR’s membrane-based post-combustion carbon dioxide (CO 2 ) capture technology to the CEMEX Balcones cement plant (Balcones) Kiln 2. The CO 2 capture plant is designed to treat the entire flue gas flow from Kiln 2; it is estimated that this system will capture 2,039 tonnes/day per day (TPD) of CO 2 , approximately 75% of current CO 2 emissions from the kiln. The captured CO 2 is compressed to 2,213 psia (152.58 bara) before flowing to the capture plant boundary tie-point. It was assumed for this study that the CO 2 would be pumped off-site and transferred by pipeline to either a CO 2 storage complex for geological sequestration or elsewhere for Enhanced Oil Recovery (EOR).

42 ENGINEERING↗

Deer Park Energy Center Natural Gas Combined Cycle Carbon Capture System Front-End Engineering Design Study (Final Scientific/Technical Report – Unlimited)

Calpine Texas CCUS Holdings, LLC (Calpine), Shell Catalysts & Technologies (SCT), Technip Energies (TEN), and Sargent & Lundy, LLC (S&L) have conducted a Front-End Engineering Design (FEED) Study to integrate a new carbon capture system at Calpine’s Deer Park Energy Center (DKEC). The DKEC facility is a nominal 1200 MW gas-fired co-generation power plant situated at the Shell Chemical Manufacturing (Shell) Facility located in Deer Park, Texas. Steam is produced for use by the adjacent Shell Chemical Manufacturing Facility as well as by the onsite steam turbine for electrical generation. The power output from DKEC is provided to the Electric Reliability Council of Texas (ERCOT) operated power grid. DKEC provides power to industrial and commercial customers via this interconnection.

03 NATURAL GAS↗

Front-End Engineering Design (FEED) Study for a Carbon Capture Plant Retrofit to a Natural Gas-Fired Gas Turbine Combined Cycle Power Plant (2x2x1 Duct-Fired 758-MWe Facility with F Class Turbines)

A comprehensive front-end engineering design (FEED) study has been undertaken by Bechtel National Inc. (Bechtel) for locating a post-combustion capture and compression (PCC) unit at Panda’s Sherman natural gas–combined cycle (NGCC) power plant in Sherman, Texas. This is described in the unredacted FEED Study report (Attachment 1) with all supporting documents, numbering over 150. The Study Report is publicly available. Sizing of the PCC plant is based on treating an amount of flue gas equivalent to that produced when generating 420 MW, which is approximately 68% of the total flue gas emitted by the NGCC power plant operating at guarantee condition with duct burners off. A reduced power plant capacity factor was used for sizing the PCC plant because the gas turbines at the site often operate at reduced load due to the high penetration of renewable power in the ERCOT region. The cost of carbon capture is primarily driven by capital cost (and therefore is highly sensitive to capacity factor). Sizing the capture unit so that when used it is nearly always operating at full capacity is critical to the economic viability of the proposed investment.

03 NATURAL GAS↗

Enlightened Education: Solar Engineering Design to Energize School Facilities

Here, this paper explores the potential for universities, colleges, and K-12 schools to implement solar electric infrastructure projects on their campuses that not only provide financial savings but also provide learning environments and instructional opportunities for students. A recent case study at Madison College is presented for a 1.85 MW photovoltaic system that is the largest solar rooftop installation in the State of Wisconsin. The system was designed with several unique features to facilitate public access, provide students with hands-on interaction, and compare and contrast several different types of solar equipment. Special engineering design considerations should be made when installing solar on schools, and recommended practices from the Madison College experience are detailed. Madison College completed a Solar Roadmap in order to prioritize and sequence investment in solar across the multiple buildings and campus locations operated by the college. The featured installation was the first project within that plan. A ten-step guide on how to create a solar roadmap is shared, so that other schools can learn from Madison College’s experience and replicate the process for their own institutions.

14 SOLAR ENERGY↗

Engineering Design with Digital Thread

Digital Thread is a data-driven architecture that links together information generated from across the product lifecycle. Though Digital Thread is gaining traction as a digital communication framework to streamline design, manufacturing, and operation processes in order to more efficiently design, build, and maintain engineering products, a principled mathematical formulation describing the manner in which Digital Thread can be used for critical design decisions remains absent. The contribution of this paper is to present such a formulation from the context of a data-driven design and decision problem under uncertainty. This formulation accounts for the fact that the design process is highly iterative and not all information is available at once. Output design decisions are made not only on what data to collect but also on the costs and benefits involved in experimentation and sensor instrumentation to collect that data. The mathematical formulation is illustrated through an example design of a structural fiber-steered composite component. In this example, the methodology highlights how different sequencing of small-scale experimentation with manufacturing and deployment lead to different designs and different associated costs.

42 ENGINEERING↗

Engineering Design of D1/D3 Dipole Magnets for the Electron-Ion Collider (EIC)

Here, the Electron-Ion Collider (EIC) at Brookhaven National Laboratory (BNL) is designed to deliver a peak luminosity of 10 34 cm −2 s −1 . An electron storage ring (ESR) will be installed in the existing Relativistic Heavy Ion Collider (RHIC) tunnel and will store polarized electron beams from 5 to 18 GeV. The ESR will have 750 dipole magnets with varying field requirements, including 576 super-bend triplets consisting of two magnets D1/D3 and D2. This paper presents on the engineering design of the D1/D3 dipole magnets within the arcs, with special focus on the coil design that allows the magnet to be configured for variable ampere-turns per energy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Front End Engineering Design Report

Energy storage is becoming an increasingly important component on modern power grids to ensure sufficient balancing capability for intermittent renewable power generations is available to maintain system reliability. Although energy storage has traditionally been provided by pumped storage hydroelectric systems, it is not possible to install these at every location and they can be expensive to construct. Lithium Ion batteries are increasingly being used to carry out energy storage grid services, however these can be expensive when specified over a few hours of capacity. An alternative way to store substantial energy is to use thermal energy storage (TES). This has been deployed on concentrated solar units using molten nitrate salts as a storage media. Again, the cost of the material can be prohibitive, thus lower cost alternative materials are being actively sought, such as sand, rocks and concrete. This project investigates the potential for Concrete Thermal Energy Storage (CTES) when used to directly store thermal energy from a thermal plant source. This report represents the initial Front-End Engineering Design (FEED) deliverables that are submitted for the Hazard and Operability (HAZOP) safety review for process validation.

25 ENERGY STORAGE↗