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At least 1,315 records · Page 73

IDAES-PSE 1.13.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.

IDAES↗

IDAES-PSE 2.3.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.. New Features and Models • New diagnostics toolboxes and examples o Tutorial for using the DiagnosticsToolbox o Methods to identify potential evaluation errors in models o SVDToolbox for performing singular value decomposition analysis on models to identify possible degeneracies and poor scaling o DegeneracyHunter for identifying irreducible degenerate sets in models • Model for solid-liquid separation which takes two inputs (solid and liquid streams) and produces three outlets (solids, liquid entrained with solids and pure liquid) • Example of temperature swing adsorption in models_extra Deprecation Warnings • With the update to Pyomo 6.7.0, the plate heat exchanger model has begun failing tests on some CI platforms. If this is not addressed by the February release, this model may be removed due to failing tests. See #1294. Offers to help identify the cause and fix this issue are welcome. Improvements and bug fixes • Fixed oversight which precluded using Mixer and Separator models when inherent reactions were present in property package • Added reporting methods to the MSContactor model • Minor corrections in some examples where values were being fixed outside of declared variable bounds

DiagnosticsToolbox,IDAES,PSE,Process Systems Engin↗

IDAES-PSE 2.4.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.. Deprecations • Convergence Analysis tool (idaes/core/util/convergence): deprecated in favor of new Parameter Sweep tools. To be removed in v3.0.0. New Beta Capabilities • Parameter Sweep Tool (idaes.core.util.parameter_sweep) o A new API for defining and performing parameter sweep studies on IDAES models has been developed • Diagnostics Tools (idaes.core.util.model_diagnostics) o New methods for identifying duplicate variables and constraints have been added to the diagnostics toolbox o New tools for detecting ill conditioning in Jacobians have been developed and are available in the model_diagnostics module. These provide alternatives to the existing DegeneracyHunter toolbox, and will eventually be merged with this capability, but initial working versions have been provided as beta capabilities for interested users o IpoptConvergenceAnalysis (replaces deprecated Convergence Analysis tool):  A new tool for performing convergence analysis studies that leverages the new Parameter Sweep tools has been developed. This tool allows users to define the input parameters to their model and sampling methods for these (leveraging Pysmo's sampling tools) and to then solve their model across the sampled domains and return a summary of the solver performance (IPOPT only) Improved Models • Thickener model (idaes.models.unit_models.solid_liquid.thickener) o Improved model to include predictive correlations for unit sizing based on settling velocity measurements (steady-state only) • Modular Property Packages o Added general support for calculating critical properties of mixtures using defined Equation of State modules. New API defined for Equation of State modules in order to define the necessary constraints for calculating critical properties (most EoS modules DO NOT support calculation of critical properties (yet)) o Added new methods to Cubic Equation of State module to support calculation of critical properties

DiagnosticsToolbox↗

IDAES-PSE 2.5.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.5.0 Release Highlights Upcoming Changes IDAES will be switching to the new Pyomo solver interface in the next release. Whilst this will hopefully be a smooth transition for most users, there are a few important changes to be aware of. The new solver interface uses a different version of the IPOPT writer (“ipopt_v2”) and thus any custom configuration options you might have set for IPOPT will not carry over and will need to be reset. By default, the new Pyomo linear presolver will be activated with ipopt_v2. Whilst are working to identify any bugs in the presolver, it is possible that some edge cases will remain. IDAES will begin deploying a new set of scaling tools and APIs over the next few releases that make use of the new solver writers. The old scaling tools and APIs will remain for backward compatibility but will begin to be deprecated. New Models, Tools and Features New diagnostics check for near-parallel variables and constraints. New diagnostics tools for identifying causes of infeasibility in models. New example for creating a custom model of a liquid-liquid extractor unit operation. Bug Fixes Fixed bug in Gibbs reactor that caused it to appear to have additional spurious degrees of freedom. Fixed bug in the Modular Property Framework that would cause errors when trying to use phase-based material balances with phase equilibria. Fixed bug in Modular Properties Framework that caused errors when initializing models with non-vapor-liquid phase equilibria. Testing and Robustness Deployed the IDAES Diagnostics Toolbox to confirm that there are no structural or numerical issues in the core model libraries. Additional robustness tests for core model, and some associated improvements in the converge tester class. Fixed a number of issues that were causing unexpected warnings to be emitted during testing. Deprecations and Removals Removed examples for RIPE tool which has not been supported for a number of releases.

AS↗

Multi-objective Bayesian alloy design using multi-task Gaussian processes

In design applications, correlations among material properties (such as the tendency for stronger materials to be less ductile) are often neglected. This approach is echoed in multi-objective optimization techniques which treat each performance characteristic as an independent objective, aiming to optimize scalar functions and find optimal Pareto fronts. However, this overlooks the statistical relationships between performance characteristics inherent in a material system. To address this, we propose the use of Bayesian optimization, a highly efficient black-box optimization algorithm known for constructing Gaussian processes (GPs) – uncorrelated surrogates - to model objective functions. Rather than evaluating multiple GPs for each objective function separately, we argue for a shift towards jointly modeling these objective functions, considering their statistical correlations. This integrated approach utilizes naturally occurring relationships among material properties, providing additional information to enhance the performance of the design framework. This requires the replacement of multiple independent GPs with a single multi-task GP, employing a correlation matrix to construct a multi-task kernel function, wherein each task corresponds to a single objective function. Here, we anticipate this refined methodology will better leverage material correlations, improving design optimization results.

36 MATERIALS SCIENCE↗

Isotherm modeling and techno-economic analysis of a TSA moving bed process using a tetraamine-appended MOF for NGCC applications

Tetraamine-appended metal-organic frameworks (MOF) are a new family of amine-functionalized MOF materials that show potential for CO 2 capture from flue gas conditions relevant to natural gas combined cycle (NGCC) power plants. This work presents isotherm modeling of the tetraamine-appended MOF Mg 2 (dobpdc)(3-4-3)(dobpdc 4- =4,4'-dioxidobiphenyl-3,3'dicarboxylate;3-4-3=N,N'bis(3-aminopropyl)-1,4-diaminobutane), process modeling, scale up, and a techno-economic optimization of a moving bed Temperature Swing Adsorption (TSA) process for carbon capture using this sorbent. This MOF exhibits a unique two-step CO 2 adsorption profile in three different pressure ranges. Thus, arctangent-based logistic functions and, quadratic and Langmuir models were employed to represent such isotherm behavior. The results of the isotherm model show good fitting vs the experimental data with an RMSE of 0.41. Here, to model the carbon capture process, the isotherm model was embedded into a moving bed contactor model, and this was used to simulate a TSA CO 2 capture cycle and evaluate cost-optimal designs considering flue gas from a -650 MWe NGCC power plant. The capital cost model consists of CAPEX correlations for reactors, compressors, ducting, etc., while the operating costs include steam, water, chemicals, and electricity (following NETL's quality guidelines for energy systems studies). A techno-economic optimization of the capture system was performed by using NETL's Framework for the Optimization and Quantification of Uncertainty and Surrogates tool (FOQUS). Results suggest that a moving bed carbon capture system with tetraamine-appended MOF can be competitive compared to conventional MEA solvent-based capture processes for NGCC plants, when a heat recovery efficiency of at least 40% is achieved, and MOF materials can be produced at a cost below -$9/kg.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Optimization of Delignified Wood Rods for Interfacial Solar Evaporation

Interfacial solar evaporation using three-dimensional evaporator materials has shown promise to achieve high evaporation rates exceeding the photothermal limit for water treatment and resource recovery processes. However, challenges remain in optimizing the material geometry to balance the vertical capillary uptake of water and the rate of evaporation to achieve both stable and high evaporation rates. Delignified wood can serve as a highly porous and hydrophilic substrate material to achieve high evaporation rates. In this work, we designed a suspended biochar-coated delignified wood rod evaporator and investigated the influence of its geometric parameters on its evaporation performance. The height of the evaporator exposed to air for evaporation was observed to be an important parameter in governing the evaporation rate, as shorter evaporators limited the available surface area for evaporation, but taller evaporators could not achieve sufficient rates of capillary uptake to maintain stable evaporation rates over 24 h under 1-sun illumination (1 kW m –2 ). In conclusion, the exposed height of the evaporator was optimized to achieve an average hourly evaporation rate of 3.05 ± 0.23 kg m –2 h –1 , which could be maintained in saline solutions containing up to 5 wt % NaCl.

Coating materials↗

SMART-COM – Scalable Multi-Agent Adaptive Resolution Tools for Collaborative Outage Management

The purpose of this grant was to conduct scientific research and prototype applications to support NPP outage staff in their adaptive decision-making in efficient scheduling and resource allocation while preventing violation of safety technical specifications. The project contributed to scientific knowledge and engineering methods in (1) user interface design, (2) scheduling optimization and risk estimation, and (2) natural language processing that would benefit the nuclear power plants in minimizing schedule overruns and even unexpected shutdowns. The research team conducted site visits at a test reactor facility and an operating nuclear power plant to gather necessary information and inputs for research and development of a software application to support NPP staff in executing their outages. The final software application consisted of three modules. First, the natural language processing module supports interactive processing of technical documentation to build a database for outage staff to query non-permissible actions on system components. This module can alleviate outage staff from reviewing extensive documentation and minimize violation of technical specifications, especially in time-sensitive situations. Second, the schedule optimization module schedules outage activities and compute risk indices that outperform existing software and current practice. This module can reduce completion time of an outage that typically have too many activities for human to optimize based on current practice that does not apply the latest operations research. Finally, the visualization module presents progress and risk information of the overall outage and individual activities, as well as enabling access to the natural language processing and schedule optimization modules. This module can provide outage staff with situation awareness that are necessary to make risk-informed decisions in response to unexpected events during the execution of an outage.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance. We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model. We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

09 BIOMASS FUELS↗

Rapid Optimization of Total Variation with Applications in Imaging, Additive Manufacturing, and Qualification

Total Variation optimization penalizes the gradient of a control variable or state. While this work focuses on image processing in particular, it has also found applications in inverse problems and topology optimization. In image processing, the goal is to maintain faithfulness to the original image while denoising and/or deblurring. Additionally, bilevel optimization over the spatially varying regularization weights can illuminate interfaces such as damage regions and other anomalies. We will address two fundamental challenges with TV-optimization: (i) the typical slow convergence of existing TV-optimization methods, and (ii) the selection of spatially varying TV parameters to promote interface detection. Additionally, we will apply such techniques to image data collected in additive manufacturing. In said context, stochasticity in build events induces flaws in the manufactured piece, compromising the integrity of said part. There is a critical need for in-situ monitoring to spot anomalies once they form, and in this setting we apply our total variation and hyperparameter solvers. We will develop a customized algorithm based on for extreme-scale TV-optimization that achieves super-linear or quadratic-convergence, a critical property for real-time, image-by-image analysis. A worst-case outcome is a preprocessing step that enhances image quality in-situ, specifically for out-of-focus and noisy images.

36 MATERIALS SCIENCE↗

Low temperature, ambient pressure electrochemical ammonia synthesis in alkaline media — Mechanistic studies and catalyst design. Final Report

Ammonia synthesis via the Haber-Bosch process is a pillar of modern agriculture, which converts the abundant but inert dinitrogen in the atmosphere to nitrogen-based fertilizers. Despite more than a century of optimization, the Haber-Bosch process remains energy intensive and reliant on fossil fuels and produces large amounts of CO 2 . Distributed and modular ammonia synthesis via the electrochemical nitrogen reduction reaction (ENRR) at or close to ambient conditions, powered by renewable electricity is an attractive alternative because it allows as needed production of ammonia, and in turn fertilizers. However, selective ENRR catalysts remain lacking. In this project, we systematically investigated two categories of catalytic materials, i.e., precious metals and transition metal nitrides, in the ENRR. Precious metals exhibit low activity and selectivity in the ENRR, which is in agreement with recent computational studies. Transition metal nitrides, especially VN, has been demonstrated as active and selective ENRR catalysts. We developed the first quantitative method to determine the amount of ammonia produced in the ENRR on N-containing containing catalysts in the membrane electrode assembly configuration. An initial ammonia production rate of 6.6 × 10 -10 mol s -1 mg -1 and a Faradaic efficiency of 6.0% at -0.1 V were reached on VN. After the initial deactivation, VN was shown to be stable in the ENRR for 116 hours with a steady state ammonia production rate of 1.1 × 10 -10 mol s -1 cm -2 . A vanadium oxynitride species was identified as the active phase in the ENRR via a combination of operando and ex situ characterization techniques. In addition, we established that the ENRR proceeded on transition metal nitrides via the Mars van Krevelen mechanism and determined the initial and steady state active site densities by developing a quantitative isotopic exchange method.

42 ENGINEERING↗

gpCAM v8

gpCAM is a Python software for large-scale Gaussian-Process driven uncertainty quantification, Bayesian Optimization, and Autonomous Experimentation. It is designed with maximum flexibility and customizability while offering record-breaking computing capabilities. In 2023 gpCAM broke the world record for Gaussian processes on large datasets set in 2019. Through clever programming and math, Gaussian Process building blocks, such as prior mean, kernel, and noise functions maintain their mathematical flexibility while supporting acceleration and scaling. This flexibility means that the approximation and uncertainty quantification can be domain-aware and extended over exotic input and output spaces.

Noack, Marcus↗

Co-sputter deposition of Nb₃Sn layer into SRF cavity using Nb-Sn composite target

Nb₃Sn, with its superior superconducting critical temperature (Tc ~18.3 K) and superheating field (Hsh ~400 mT), is considered a promising material for superconducting radiofrequency (SRF) cavities, offering enhanced cryogenic performance compared to bulk niobium cavities. A Nb₃Sn coating technique has been developed for Nb SRF cavities using co-sputtering of Nb-Sn composite target in a DC cylindrical magnetron sputtering system. The composite target configuration and discharge conditions for co-sputtering were optimized to deposit Nb-Sn films on flat Nb substrates, followed by annealing to form Nb₃Sn. Multiple strategies have been explored to improve the surface homogeneity of the Nb₃Sn coating, including optimizing a two-step annealing process, annealing in Sn vapor, and a light Sn recoating process. A 1.5 µm Nb-Sn co-sputtered film was deposited on the interior of a 2.6 GHz Nb SRF cavity and annealed at 600 °C for 6 h, followed by 950 °C for 1 h. Cryogenic RF testing of the annealed cavity demonstrated a Tc of 17.8 K, confirming the formation of Nb₃Sn. Then, the annealed cavity underwent a light recoating treatment and attained a quality factor (Q0) of 8.5E+08 at 2.0 K.

Accelerator Physics↗

Optimizing Sub-Sampled STEM Imaging for Beam Sensitive Materials and Dynamic Processes

The goal of this research is to quantify how sub-sampling and inpainting (broadly classified as methods in Artificial Intelligence) can be used to obtain the highest resolution images under any experimental conditions, and to use this to reproducibly control dynamic in-situ processes in gases and liquids on the nanoscale. As atomic diffusion under non-extreme conditions is typically in the sub-ms time domain, the ability to use this sub-sampling approach to reduce the effect of the electron beam and to speed-up the imaging process represents the optimum combination of spatial and temporal resolution achievable in a conventional microscope. This LDRD funding demonstrated the feasibility of imaging dynamics in complex energy systems using this methodology by establishing the optimum experimental sampling conditions (optimizing the number of pixels, speed, contrast and resolution) for each experiment. For in-situ liquid cell experiments this analysis shows that simply by distributing the same dose and dose rate maximally in space and time, the kinetics for radiolysis products can be modified significantly. The final stage of this work, i.e. the experimental demonstration of its use for nucleation and growth, was severely curtailed by issues with COVID19. However, all the experiments are designed and currently underway, with the goal to complete the final demonstration and publication as soon as safety concerns for laboratory access are met.

36 MATERIALS SCIENCE↗

Flow-Assisted Selective Mineral Extraction from Seawater

The sustainable production of critical materials from natural sources requires a paradigm shift away from currently used resource-intensive processes. In this paper, we report a single-step, laminar co-flow method (LCM) that leverages non-equilibrium conditions to selectively extract pure Mg(OH) 2 from natural seawater. Conventional seawater-based Mg extraction involves adding individual or a combination of precipitants to obtain Mg(OH) 2 , but the co-existence of Ca 2+ unavoidably results in CaCO 3 impurities requiring additional purification steps. Here we show that the non-equilibrium conditions in LCM achieved using a microfluidics device and by simply co-injecting a NaOH solution with seawater can result in improved selectivity for Mg(OH) 2 unlike in conventional bulk mixing method. The resulting precipitates are characterized for composition and the process yield and purity are optimized through systematic variations of the reaction time and the concentration of NaOH. This is the first demonstration of LCM for selective separation, and as a one-step process that does not rely on novel sorbents, membranes, or external stimuli, it is easy to scale-up. LCM has the potential to be broadly relevant to selective separations from complex feed streams and diverse chemistries—enabling more sustainable materials extraction and processing.

54 ENVIRONMENTAL SCIENCES↗

Technology Case Study: Economic, Sustainability, and Deployment Considerations for Sustainable Aviation Fuels Produced via Lignocellulosic Sugar Catalysis

This report presents a technology case study reflecting one exemplary representative pathway for the conversion of lignocellulosic sugars to sustainable aviation fuels (SAF) via aqueous phase reforming (APR) catalysis, considered within a broader integrated biorefinery framework based on biochemical processing operations. While far from the only option for converting sugars to SAF, this pathway was selected as a case study here based on its relatively high technology maturity and simplistic processing approach (avoiding complex separations or other equipment scalability challenges), coupled with the potential for high fuel yields and favorable costs/carbon intensities with opportunities for further near-term optimization. The report considers key process integration and engineering design considerations for a modeled hypothetical, nth-plant commercial biorefinery, reflecting a number of processing options and parameters envisioned to be achievable as future goals. Resultant outputs from Aspen Plus process simulations are evaluated through techno-economic and life cycle analyses (TEA and LCA), including implications for marginal cost of CO2 abatement and inclusion of currently-applicable policy incentives. Moving beyond base case configurations, a number of alternative scenarios are also evaluated for their ability to further improve economics, greenhouse gas (GHG) emissions, and marginal cost of abatement, highlighting a path to achieve deep decarbonization goals of more than 70% GHG reduction for SAF (with the potential to reach net-negative carbon intensities in some cases) under reasonable fuel production costs. The report also highlights future opportunities and gaps for further research on this technology pathway.

09 BIOMASS FUELS↗

All-optical phase conjugation using diffractive wavefront processing

Abstract Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with applications ranging from imaging to beam focusing. Here, we present a diffractive wavefront processor to approximate all-optical phase conjugation. Leveraging deep learning, a set of diffractive layers was optimized to all-optically process an arbitrary phase-aberrated input field, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated this wavefront processor by 3D-fabricating diffractive layers and performing OPC on phase distortions never seen during training. Employing terahertz radiation, our diffractive processor successfully performed OPC through a shallow volume that axially spans tens of wavelengths. We also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and multi-wavelength nature, this diffractive wavefront processor can be used for various applications, e.g., turbidity suppression and aberration correction across different spectral bands.

42 ENGINEERING↗

Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. Here, to demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation.

01 COAL, LIGNITE, AND PEAT↗