Working Toward Mitigating Cognitive Biases in Analogous-Based Cost Estimating Models.
Abstract not provided.
Samberson, Jonell Nicole↗
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Working Toward Mitigating Cognitive Biases in Analogous-Based Cost Estimating Models.
Abstract not provided.
Wright, Jonathan↗
Cost and Performance Estimates for State-of-the-Art and Advanced 1×1 H-Class Natural Gas-Fired Power Plants
As an extension of NETL's Fossil Energy Baseline for Electricity Generating Units Volume 1: Coal and Natural Gas to Electricity (FEB Rev 4a, this study develops cost and performance estimates for analogous NGCC cases using a state-of-the-art 2023 vintage H-Class CT in a 1×1 configuration, where a single combustion turbine and heat recovery steam generator are coupled to a single steam turbine on a common shaft. These 1×1 H-Class cases are used to develop cost and performance estimates of X-Class 1×1 NGCC cases with advanced performance characteristics, analogous to NETL’s cost and performance projections report.
20 FOSSIL-FUELED POWER PLANTS↗
Equivalent Breakeven Installed Cost: A Tradeoff-Informed Measure for Technoeconomic Analysis of Candidate Heliostat Improvements
Technoeconomic analysis (TEA) is commonly used to determine economic viability of power-generating technologies, including concentrating solar power (CSP) and thermal (CST) production plants. Levelized cost of electricity (LCOE) and analogous measures provide an estimate of long-term costs for operating power plants over their designed lifetimes by accounting for revenues and costs in a time-discounted manner. While these measures are effective when assessing a technology’s total lifecycle costs and productivity under various designs, TEA of candidate incremental technology improvements from the lens of LCOE can be limited when required investment and LCOE impacts are small. In this work, we propose a novel metric for TEA of a plant component technology that recasts relative changes in levelized system costs into component-specific capital cost budgets. This measure, which we refer to as the equivalent breakeven installed cost, is the maximum budget for the technology component that leads to improved levelized costs. We illustrate the usefulness of this metric using the example of candidate heliostat improvements for a CSP tower plant. Here, the results suggest that a reduction in mirror washing costs yield a total plant O&M cost of $37/kWe-yr, which is a breakeven proposition if the average reflectance is reduced from 0.90 to 0.85 as a result of the cost savings.
Zolan, Alexander (ORCID:0000000326017604)↗
Equivalent Breakeven Installed Cost: A Tradeoff-Informed Measure for Technoeconomic Analysis of Candidate Heliostat Improvements: Preprint
Technoeconomic analysis (TEA) is commonly used to determine economic viability of power-generating technologies, including concentrating solar power (CSP) and thermal (CST) production plants. Levelized cost of electricity (LCOE) and analogous measures provide an estimate of long-term costs for operating power plants over their designed lifetimes by accounting for revenues and costs in a time-discounted manner. While these measures are effective when assessing a technology's total lifecycle costs and productivity under various designs, TEA of candidate incremental technology improvements from the lens of LCOE can be limited when required investment and LCOE impacts are small. In this work, we propose a novel metric for TEA of a plant component technology that recasts relative changes in levelized system costs into component-specific capital cost budgets. This measure, which we refer to as the equivalent breakeven installed cost, is the maximum budget for the technology component that leads to improved levelized costs. We illustrate the usefulness of this metric using the example of candidate heliostat improvements for a CSP tower plant. Here, the results suggest that a reduction in mirror washing costs yield a total plant O&M cost of $37/kWe-yr, which is a breakeven proposition if the average reflectance is reduced from 0.90 to 0.85 as a result of the cost savings.
concentrating solar power↗
Reaction mechanism for atmospheric pressure plasma treatment of cysteine in solution
Mechanisms for the cold atmospheric plasma (CAP) treatment of cells in solution are needed for more optimum design of plasma devices for wound healing, cancer treatment, and bacterial inactivation. However, the complexity of organic molecules on cell membranes makes understanding mechanisms that result in modifications (i.e. oxidation) of such compounds difficult. As a surrogate to these systems, a reaction mechanism for the oxidation of cysteine in CAP activated water was developed and implemented in a 0-dimensional (plug-flow) global plasma chemistry model with the capability of addressing plasma-liquid interactions. Reaction rate coefficients for organic reactions in water were estimated based on available data in the literature or by analogy to gas-phase reactions. The mechanism was validated by comparison to experimental mass-spectrometry data for COST-jets sustained in He/O 2 , He/H 2 O and He/N 2 /O 2 mixtures treating cysteine in water. Results from the model were used to determine the consequences of changing COST-jet operating parameters, such as distance from the substrate and inlet gas composition, on cysteine oxidation product formation. Results indicate that operating parameters can be adjusted to select for desired cysteine oxidation products, including nitrosylated products.
60 APPLIED LIFE SCIENCES↗
Cost‐Responsive Optimization of Nickel Nanoparticle Synthesis
Abstract Early‐stage cost evaluation during catalyst development holds the potential to accelerate the commercialization and deployment of advanced catalytic materials for sustainable chemical processes. The modeling and assessment of manufacturing costs as early as the laboratory synthesis scale, for example, focusing on materials costs and synthesis performance metrics, can support the development of an experimental–economic feedback loop that enables rapid insight into cost drivers associated with catalyst synthesis and highlights areas that require focused research and development effort. Ultimately, this feedback loop supports the realization of an economic understanding of the overall synthetic process and highlights opportunities to reduce costs, serving as the foundation for the scale‐up of catalyst manufacturing. Herein, a case study is presented utilizing CatCost, a free and publicly available estimation tool for the evaluation of catalyst manufacturing costs, to perform a cost‐responsive optimization of the synthesis of nickel nanoparticles (Ni NPs). It is demonstrated that reagent substitutions with more cost‐effective analogs, coupled with stoichiometric optimization, afford a 58% reduction in raw materials cost without changing the product yield or properties.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Techno-Economic, Feasibility, and Life Cycle Analysis of Renewable Propane: 2025 Update
To clarify the current and future landscape for renewable propane (RP) production, this work evaluates the value proposition of recovering RP from existing and planned hydroprocessed esters and fatty acids (HEFA) biorefineries and surveys emerging technologies under development or deployment. HEFA biorefineries co-produce a propane-rich fuel gas stream, normally used to meet HEFA process heat requirements, from which propane can be recovered and sold to create an additional revenue stream alongside liquid transportation fuels such as renewable diesel (RD) and sustainable aviation fuel (SAF). This report updates and extends a 2022 analysis of RP recovery from HEFA facilities by escalating capital and operating costs to 2024 prices, incorporating recent policy developments (including the Section 45Z Clean Fuel Production Credit), evaluating RP recovery for both RD- and SAF-focused HEFA facilities at two scales (3,000 and 75,000 barrels per day of feedstock), and quantifying the impact of RP recovery on HEFA liquid-fuel carbon intensity (CI) and associated tax credits using the 45ZCF-GREET model. For a 3,000 BPD RD-focused HEFA facility, approximately 3.5 million gallons per year (MGPY) of RP can be recovered; in this base case, the estimated payback period is 18 months based on the total installed cost of the RP recovery equipment and 36 months based on the total capital investment for the entire RP recovery project. The payback period is slightly shorter for the analogous SAF-focused configuration (approximately 4.3 MGPY RP). Sensitivity analysis shows that CAPEX magnitude, RP recovery plant scale, and CI-driven tax credit valuations are the dominant determinants of project viability. RP recovery may increase the CI of HEFA liquid fuels, which can reduce liquid-fuel tax credits (a key revenue stream for the HEFA biorefinery) and lengthen payback periods. However, RP recovery generally remains economically favorable across a wide range of plausible scenarios and market conditions. The report also summarizes emerging pathways that could expand future RP supply.
09 BIOMASS FUELS↗
Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic
We introduce a quantum information theory-inspired method to improve the characterization of many-body Hamiltonians on near-term quantum devices. We design a new class of similarity transformations that, when applied as a preprocessing step, can substantially simplify a Hamiltonian for subsequent analysis on quantum hardware. By design, these transformations can be identified and applied efficiently using purely classical resources. In practice, these transformations allow us to shorten requisite physical circuit-depths, overcoming constraints imposed by imperfect near-term hardware. Importantly, the quality of our transformations is t u n a b l e : we define a 'ladder' of transformations that yields increasingly simple Hamiltonians at the cost of more classical computation. Using quantum chemistry as a benchmark application, we demonstrate that our protocol leads to significant performance improvements for zero and finite temperature free energy calculations on both digital and analog quantum hardware. Specifically, our energy estimates not only outperform traditional Hartree-Fock solutions, but this performance gap also consistently widens as we tune up the quality of our transformations. In short, our quantum information-based approach opens promising new pathways to realizing useful and feasible quantum chemistry algorithms on near-term hardware.
Physics↗
Introduction: Neuromorphic Materials
The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.
Circuits↗
Quantitative Risk Analysis of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants using IRADIC Technology
This report documents the activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2021 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) Risk Assessment project. In FY-2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades/designs. An integrated risk assessment technology for the DI&C systems (IRADIC technology) was proposed for this strategy, which aims to (1) provide a best-estimate risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the High Safety Significant Safety-related (HSSSR) DI&C systems, (2) develop an advanced risk assessment technology to support transition from analog to DI&C technologies for nuclear industry, (3) assure the long-term safety and reliability of vital HSSSR DI&C systems, (4) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals and deal with the expensive licensing justifications from regulatory insights, the IRADIC technology is instructive for nuclear vendors and utilities to effectively lower the costs associated with digital compliance and speed industry advances by: (1) defining an integrated risk-informed analysis process for DI&C upgrade, including hazard analysis, reliability analysis, and consequence analysis, (2) applying systematic and risk-informed tools to address common cause failures (CCFs) and quantify responding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the individual level, system level, and plant level, (4) providing insights and suggestions on designs to manage the risks; thus, to support the development, licensing, and deployment of advanced DI&C technologies on nuclear power plant (NPPs). In this report, an approach for performing software CCF analysis, given limited data, is developed and demonstrated using a case study of a highly redundant digital reactor trip system. Consequence analysis is also performed based on different accident scenarios. Results indicate that plant modernization including the improvement of HSSSR DI&C systems will make great benefits to plant safety by providing more safety margins to accident management. In addition, a novel approach is proposed in this report for the quantification of software hazards when sufficient operational and testing data available. The method incorporates software development quality as well as strong analysis techniques to identify and link software defects to potential failure modes. The approach includes both semantic and test-based analysis to detect failures that can exist in different stages of the software development life cycle. This method is applied to an advanced human system interface relevant to reactor trip safety developed from the APR 1400 design.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Seeing through noise in power laws
Despite widespread claims of power laws across the natural and social sciences, evidence in data is often equivocal. Modern data and statistical methods reject even classic power laws such as Pareto’s law of wealth and the Gutenberg–Richter law for earthquake magnitudes. We show that the maximum-likelihood estimators and Kolmogorov–Smirnov (K-S) statistics in widespread use are unexpectedly sensitive to ubiquitous errors in data such as measurement noise, quantization noise, heaping and censorship of small values. This sensitivity causes spurious rejection of power laws and biases parameter estimates even in arbitrarily large samples, which explains inconsistencies between theory and data. We show that logarithmic binning by powers of λ > 1 attenuates these errors in a manner analogous to noise averaging in normal statistics and that λ thereby tunes a trade-off between accuracy and precision in estimation. Binning also removes potentially misleading within-scale information while preserving information about the shape of a distribution over powers of λ, and we show that some amount of binning can improve sensitivity and specificity of K-S tests without any cost, while more extreme binning tunes a trade-off between sensitivity and specificity. We therefore advocate logarithmic binning as a simple essential step in power-law inference.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Seeing through noise in power laws
Despite widespread claims of power laws across the natural and social sciences, evidence in data is often equivocal. Modern data and statistical methods reject even classic power laws such as Pareto’s law of wealth and the Gutenberg–Richter law for earthquake magnitudes. We show that the maximum-likelihood estimators and Kolmogorov–Smirnov (K-S) statistics in widespread use are unexpectedly sensitive to ubiquitous errors in data such as measurement noise, quantization noise, heaping and censorship of small values. This sensitivity causes spurious rejection of power laws and biases parameter estimates even in arbitrarily large samples, which explains inconsistencies between theory and data. We show that logarithmic binning by powers of λ > 1 attenuates these errors in a manner analogous to noise averaging in normal statistics and that λ thereby tunes a trade-off between accuracy and precision in estimation. Binning also removes potentially misleading within-scale information while preserving information about the shape of a distribution over powers of λ, and we show that some amount of binning can improve sensitivity and specificity of K-S tests without any cost, while more extreme binning tunes a trade-off between sensitivity and specificity. We therefore advocate logarithmic binning as a simple essential step in power-law inference.
97 MATHEMATICS AND COMPUTING↗
Analysis of Carbon Capture Retrofits for Cement Plants
The objective of this study is to provide an estimate of the cost to capture CO 2 in retrofit applications at cement plants. The cement plant configurations considered in this study include natural gas and solid fuel (coal and coke) cases, and both wet- and dry kiln-fed plant designs, however the base cement plants in this study were not evaluated other than characterization of their kiln off-gas stream and high-level quantification of heat integration potential at the existing plant. In each case, the base cement plant produces 1.5 M tonnes per year of finished cement, assuming 91.4 percent clinker content. This analysis includes a 10 percent retrofit cost increase for process integration and low grade heat recovery, when compared to the analogous non-heat integration case. Heat integration is considered as a potential offset to capture system heating demands (i.e., as a percentage reduction). Recovery and reuse of excess heat from the base cement plant can provide economic benefits—primarily by reducing the need to purchase supplemental natural gas for CO 2 solvent regeneration—but any process improvement must be great enough to overcome the cost increases (i.e., capital and O&M) necessary to realize those benefits. With heat integration potential of 10 percent and 30 percent, that benefit wasn’t significant enough to offset the increase in capital and operating costs, and a cost of capture increase was observed based on the assumptions in this report. Benefits of heat integration potential may be realized when natural gas prices are higher, as demonstrated in the sensitivity to natural gas price where crossover points exist between heat integration cases and their respective non-heat integration cases. Four of the base cases were further evaluated to explore the cost implications of deeper levels of gas pre-treatment to remove oxides of nitrogen (NOx) and oxides of sulfur (SOx) from the kiln off-gas stream prior to CO 2 capture, purification, and compression. The results of these additional case analyses showed a 7.4–18.8 percent increase in cost of capture over the respective base case (i.e., analogous cases without SOx/NOx removal).
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Variational Optical Phase Learning on a Continuous-Variable Quantum Compiler
Quantum process learning is a fundamental primitive that draws inspiration from machine learning with the goal of better studying the dynamics of quantum systems. One approach to quantum process learning is quantum compilation, whereby an analog quantum operation is digitized by compiling it into a series of basic gates. While there has been significant focus on quantum compiling for discrete-variable systems, the continuous-variable (CV) framework has received comparatively less attention. We present an experimental implementation of a CV quantum compiler that uses two-mode squeezed light to learn a Gaussian unitary operation. We demonstrate the compiler by learning a parameterized linear phase unitary through the use of target and control phase unitaries to demonstrate a factor of 5.4 increase in the precision of the phase estimation and a 3.6-fold acceleration in the time-to-solution metric when leveraging quantum resources. We further show how our approach can be extended to higher-dimensional compilation tasks. Our results are enabled by the tunable control of our cost landscape via variable squeezing, thus providing a critical framework to simultaneously increase precision and reduce time-to-solution.
97 MATHEMATICS AND COMPUTING↗
Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants
Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. Here this paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework's capabilities is documented here in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Helium metastable density determination in the COST reference source by absolutely calibrated optical emission spectroscopy
Helium metastable densities in the COST Reference Microplasma Jet are estimated for a variety of He/N2 admixtures and dissipated powers by applying a collisional-radiative model to absolutely calibrated optical emission spectroscopy measurements. This is accomplished by delineating the excitation mechanisms that result in the N2(C–B) and N2+(B–X) emission bands, the latter of which is strongly coupled to the presence of helium metastables. A number of other plasma parameters are established and discussed for each operating condition including the electron energy distribution function, reduced electric field, rate constants, and electron density. With these parameters, the reaction rates for the primary ionization pathways are also calculated, emphasizing the importance of helium metastables for discharge sustainment. Good agreement with the existing literature is found for most plasma parameters and for helium metastable densities, in particular. A clear [N2]−1 relationship between the nitrogen concentration and density of helium metastables is demonstrated, as has been identified in previous studies in analogous atmospheric pressure plasma jets. This validates the efficacy of this optical technique for determining helium metastable densities and establishes it as a viable, and in many cases, more accessible alternative to other means of quantifying helium metastables in low-temperature plasmas.
Physics↗
Wastewater Recycling Using a Hygroscopic Cooling System
This project by the Energy & Environmental Research Center (EERC), Baltimore Aircoil Company (BAC), and Great River Energy (GRE) evaluated the concept of recycling wastewater at a coal-fired power plant using a hygroscopic cooling system, which is an evaporative cooling technology analogous to conventional cooling towers, except that sparingly soluble dissolved solids are precipitated and removed as waste solids instead of purging them with a liquid blowdown stream. This technology can maximize the use of plant makeup water by obtaining useful evaporative cooling from wastewater while minimizing the volume of wastes needing disposal. Experimental activities were conducted in two phases, a laboratory-based evaluation of the properties of wastewater from the host site, GRE’s Coal Creek Station near Underwood, North Dakota, and a field test of a small pilot hygroscopic cooling system at the host site power plant. Findings from the laboratory study informed the design of the pilot system and the system’s field test performance served as the basis for a techno-economic analysis (TEA) of the hygroscopic recycling concept. At the preferred operating conditions identified during the TEA, the wet-bulb approach temperature of the tower was 7.3°C (13°F) and the volume of blowdown produced by the plant was reduced to 5.4% of its incoming volume. Waste solids produced during field testing were classified as nonhazardous waste based on the measured hazardous element content and evaluation of their leaching potential. However, to qualify as a solid for landfill disposal i.e., as determined by the U.S. Environmental Protection Agency’s paint filter test, it appears that a dewatering step beyond hydrocyclone separation is needed. The baseline levelized cost of wastewater disposal (LCWD) for hygroscopic wastewater recycling was estimated to be $\$ $3.69–$\$ $3.72 per m3 of plant blowdown. Capital cost was estimated to contribute over 54% to the LCWD, and parameters that impact capital cost such as the heat exchange coil material of construction and the tower’s wet-bulb approach temperature were identified as having the greatest impact on overall LCWD. A LCWD estimate prepared for the same application but using thermomechanical brine evaporation was almost 40% higher than that calculated for hygroscopic cooling, despite recovering distilled-quality water for reuse, while the LCWD for disposal-only, deep well injection was estimated to be 30% lower compared to hygroscopic wastewater recycling.
01 COAL, LIGNITE, AND PEAT↗