Results for “environmental solutions” — Page 2

NASA NTRS2003

A Comparison between Growth Morphology of "Eutectic" Cells/Dendrites and Single-Phase Cells/Dendrites

Directionally solidified (DS) intermetallic and ceramic-based eutectic alloys with an in-situ composite microstructure containing finely distributed, long aspect ratio, fiber, or plate reinforcements are being seriously examined for several advanced aero-propulsion applications. In designing these alloys, additional solutes need to be added to the base eutectic composition in order to improve heir high-temperature strength, and provide for adequate toughness and resistance to environmental degradation. Solute addition, however, promotes instability at the planar liquid-solid interface resulting in the formation of two-phase eutectic "colonies." Because morphology of eutectic colonies is very similar to the single-phase cells and dendrites, the stability analysis of Mullins and Sekerka has been extended to describe their formation. Onset of their formation shows a good agreement with this approach; however, unlike the single-phase cells and dendrites, there is limited examination of their growth speed dependence of spacing, morphology, and spatial distribution. The purpose of this study is to compare the growth speed dependence of the morphology, spacing, and spatial distribution of eutectic cells and dendrites with that for the single-phase cells and dendrites.

Tewari, S. N.↗

DOE OSTI2025

Summary of Analytical Services for the Hanford Site Radionuclide NESHAP Program

This document is a summary of the point source analytical requirements used to demonstrate compliance for the Department of Energy (DOE) Hanford Site operations with 40 Code of Federal Regulations (CFR) Part 61, “National Emission Standards for Hazardous Air Pollutants,” (NESHAP) Subpart H, “National Emission Standards for Emissions of Radionuclides Other Than Radon From Department of Energy Facilities,” and the Washington Administrative Code (WAC) 246-247, “Radiation Protection – Air Emissions.” This reference collects information from multiple source documents and is not intended to create, supersede, replace or over-ride any existing contractual, DOE, federal or state statutes, regulations, compliance agreements, orders, permits, licenses or other requirements. The requirement source document governs where any difference may exist. The Hanford Mission Integration Solutions (HMIS) Environmental organization has been contracted by DOE to manage and report data collected from the sampling and monitoring of radioactive air emissions point sources, colloquially called stacks. The Environmental organization coordinates the analyses and reporting of samples collected at various facilities across the Hanford Site. These facilities operate approximately 52 stacks that require sampling, monitoring or estimating radioactive air emissions. The stacks are operated by Bechtel National, Inc. (BNI), Central Plateau Cleanup Company (CPCCo), Hanford Tank Waste Operations & Closure (H2C), Hanford Laboratory Management and Integration (HLMI), and Pacific Northwest National Laboratory (PNNL). Stack samples from CPCCo, HLMI and H2C facilities are collected by the operating contractor staff, delivered to HMIS, and then shipped to an offsite contracted laboratory for analyses. The field and laboratory sample data uploaded into the Sample Management and Analytical Results Tracking (SMART) database are used to calculate sample volumes and concentrations. Sample concentrations are evaluated for compliance with federal and state regulations, permits, and license requirements. The SMART database also calculates total curies released for sampled point sources and stacks. Point source effluent concentrations and releases are published annually in publicly available reports. The BNI and PNNL operate several DOE-Hanford Field Office (HFO) stacks subject to the requirements of 40 CFR 61, Subpart H and WAC 246-247. The concentrations, curies released and dose modeling evaluation for these stacks are included in the DOE-HFO annual radionuclide NESHAP report. The sample collection, analyses and emissions estimates for these stacks are outside the scope of HMIS contracted responsibilities and not addressed further in this document.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1973

Applications of remote sensing data to the Alaskan environment

The ERTS program provides a means to overcome the formidable logistic and economic costs of preparing environmental surveys of the vast and relatively unexplored regions of Alaska. There is an excellent potential in satellite remote sensing to benefit Federal, state, local, and private agencies, by providing a new synoptic data base which is necessary for the preparation of the needed surveys and the search for solutions to environmental management problems. One approach in coupling satellite data to Alaskan problems is a major program initiated by the University of Alaska and funded by NASA's Goddard Space Flight Center. This included 12 projects whose aims were to study the feasibility of applying ERTS data to the disciplines of ecology, agriculture, hydrology, wildlife management, oceanography, geology, glaciology, volcanology, and archaeology.

Belon, A. E.↗

NASA NTRS1984

White Cliffs: Operating Experience

The fourteen dish white cliffs solar power station area is remote and subject to extreme environmental conditions, solution of the associated problems required careful and thoughtful attention and the application of resources. Notwithstanding the wide range and harshness of conditions, the difficulties caused by remoteness and the lack of a technological base and the need for relatively rapid demonstration of success, the project has had a very positive outcome. Qualitative and quantitative information and lessons are now available to enable considerable simplifications to be made for a new system, reducing both hardware and operation and maintenance costs. Experience and lessons are presented, particularly in relation to: system performance in various environmental conditions; design philosophies for collectors, the array, control systems, engine and plant; operation and maintenance strategies and cost reducing possibilities. Experience so far gives encouragement for the future of such paraboloidal dish systems in appropriate areas.

Kaneff, S.↗

DOE OSTI2020

Morphological–Electrical Property Relation in Cu(In,Ga)(S,Se) 2 Solar Cells: Significance of Crystal Grain Growth and Band Grading by Potassium Treatment

Abstract Solution‐processed Cu(In,Ga)(S,Se) 2 (CIGS) has a great potential for the production of large‐area photovoltaic devices at low cost. However, CIGS solar cells processed from solution exhibit relatively lower performance compared to vacuum‐processed devices because of a lack of proper composition distribution, which is mainly instigated by the limited Se uptake during chalcogenization. In this work, a unique potassium treatment method is utilized to improve the selenium uptake judiciously, enhancing grain sizes and forming a wider bandgap minimum region. Careful engineering of the bandgap grading structure also results in an enlarged space charge region, which is favorable for electron–hole separation and efficient charge carrier collection. Besides, this device processing approach has led to a linearly increasing electron diffusion length and carrier lifetime with increasing the grain size of the CIGS film, which is a critical achievement for enhancing photocurrent yield. Overall, 15% of power conversion efficiency is achieved in solar cells processed from environmentally benign solutions. This approach offers critical insights for precise device design and processing rules for solution‐processed CIGS solar cells.

Kim, Joo‐Hyun↗

DOE OSTI2026

Optimizing enzymes for plastic upcycling using machine learning design and high throughput experiments

Plastic use is ubiquitous in the modern world, and polyethylene terephthalate (PET) is one of the most abundantly produced plastics (and the most highly produced polyester), with ~65 million metric tons manufactured annually. To the consumer, PET is likely most recognizable as the plastic used to make beverage bottles. Like many plastics, traditional mechanical or chemical means of PET deconstruction and upcycling are costly and inefficient. Because of these challenges, recycled plastic is generally of lower quality and is more expensive to produce than virgin plastic derived from petroleum. Ultimately, this results in most plastic ending up as waste. We view plastic waste as an underutilized resource which, with the development of more efficient and high-quality recycling processes, could (1) generate significant economic value while (2) decreasing petroleum usage and greenhouse gas emissions, as well as (3) minimizing its negative environmental and health impacts. Biocatalytic recycling, or biomanufacturing the basic building blocks of new plastic from plastic waste, is a promising approach to plastic reuse that complements existing recycling technologies. Recently, biological enzymes capable of breaking down PET have garnered significant attention as an attractive means of dealing with the plastic problem. These enzymes are currently undergoing pilot studies for implementation in industrial-scale enzyme-based recycling. However, there are significant limitations to current enzymes, including the need to perform costly pre-processing of the plastic waste before the enzymes are able to work. Further optimization of these enzymes is necessary to make these technologies competitive, and ultimately incentivise industry-wide adoption of this biology-based green recycling technology. n this work we demonstrate a means to design and generate performant biological enzymes, capable of efficiently deconstructing plastic waste. Specifically, we applied recent advances in artificial intelligence, machine learning, and statistical analysis to design new versions and discover natural enzymes capable of breaking down PET. We focused on optimizing key properties that are important for industrial-scale enzymatic recycling such as pH and thermotolerance. Normal testing of enzymatic plastic-deconstruction is extremely labor intensive and so through this work we also developed a robotic-assisted experimental pipeline capable of characterizing thousands of candidate enzymes. The results of this iterative, AI-guided, multi-discipline approach have led to increases in enzymatic breakdown of over 150X over starting enzymes. This work supports the rapidly developing and transformative field of biocatalytic solutions to environmental problems beyond the discovery and predictive understanding of enzymes for polymer recycling, and has wide implications for tackling numerous energy problems such as carbon capture and fixation (e.g., engineering carbon monoxide dehydrogenase and the rubisco-pathway), biomining (e.g., design of lanthanide-binding proteins) and biomanufacturing (e.g., lignin-deconstruction enzymes).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Mercury Speciation During Vitrification of LAW

n this work, laboratory and engineering-scale tests were conducted that mimic the reactive environment of the Hanford Tank Waste Treatment and Immobilization Plant (WTP) low activity waste (LAW) melter and off-gas system in order to assess the speciation of mercury at select points in the processing system. The experimental protocols, test equipment, feed materials used, and rationale for testing are detailed in the Test Plan for this work. Particular attention was paid to the amount and speciation of mercury in submerged bed scrubber (SBS) solutions, which are intended to be recycled back to the melter feed at the WTP. Results are presented from detailed mercury analysis of these solutions using Environmental Protection Agency (EPA) Method 1630 and modifications to this method for dimethyl mercury and methyl mercury as well as EPA Method 1631 and modifications to this method for total, dissolved, elemental, suspended, and ionic mercury. Testing was conducted in two separate sets of experiments: (1) One set of tests (crucible-scale furnace tests) that involved heating small batches of mercury-spiked melter feed in crucibles to determine the amount of mercury retained in the glass and the species of mercury in the exhaust gases; (2) A second set of tests (DM10 melter tests) was planned that involved creating LAW melter plenum gas compositions using the DM10 melter system, injecting mercury into the off-gas stream, and passing that stream though a reactor that simulates various plenum gas conditions. Operational issues led to the need to use one of the VSL DM100 melters in place of the planned DM10 melter for these tests. The speciation of mercury after exposure to those conditions was monitored. In both sets of tests, the exhaust gases were run through a scrubber that was intended to mimic the LAW SBS in order to determine how much of the mercury exiting the melter would be retained in the primary off-gas system fluids and in what form. After passing through the SBS, the exhaust stream was analyzed to determine particulate, ionic, elemental, and total mercury passing downstream of the SBS. In tests employing gases derived from the DM100 melter that were spiked with elemental mercury, the processing system provided gas temperatures and residence times that are representative of the WTP LAW vitrification system in order to assess the effect of those conditions on mercury speciation. The Decontamination Factor (DF) across the system in the tests with mercury-spiked DM100 melter exhaust was determined using the analytical data from EPA Method 30B (Fluegas Adsorption Mercury Speciation (FAMS TM )) exhaust samples and the amount of mercury detected in the SBS solutions. Mercury species used in melter feed crucible scale tests were divalent (chloride and iodide), monovalent (chloride and fluoride), and elemental mercury. Individual tests included only a single form of mercury in the feed. The results for total mercury mass balances in the crucible tests are also presented.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Environmental photosensitizers can exhibit enhanced actinic absorption in microhydrated clusters compared to solution

Brown carbon chromophores at environmental air-water interfaces often act as photosensitizers that absorb sunlight and subsequently transfer energy to nearby molecules, initiating a wide variety of chemical reactions. Despite their importance to understanding daytime chemistry at these airwater interfaces, little is known about the role of the solvation environment on the photophysical properties of these photosensitizers. In this work, we present a joint experimental-theoretical study of the vibrational and photophysical properties of microhydrated protonated and deprotonated 4-benzoylbenzoic acid (4-BBA), a key model system for environmental photosensitizers. We find that for protonated 4-BBAH + ∙(H 2 O) 0-1 , representing photosensitizers in very acidic conditions, a single bright state dominates the UV-Vis spectrum between 280 and 400 nm. Comparing the experimental UV-Vis spectra and quantum chemistry-predicted spectra of 4-BBA + ∙(H 2 O) 0-2 , we find that the degree of microhydration has little effect on the UV-Vis spectra or the orbitals of the dominant feature. For deprotonated 4-BBA ‒ , representing photosensitizers in basic conditions, quantum chemistry calculations predict that the UV-Vis spectra are ~3x weaker in intensity than the brightest 4-BBAH + ∙(H 2 O) 0-1 features and were not observed experimentally. Quantum chemistry calculations predict one dominant UV-Vis feature is present in the spectra of 4-BBA ‒ ∙(H 2 O) 0-2 , which exhibit minor shifts with degree of microhydration. While 4-BBA in bulk solution over a range of pH values has relatively weak absorption within the solar actinic region, we show that microhydrated 4-BBA has bright transitions within the actinic region. This indicates that the complex structure of environmental air-water interfaces can shift the absorption maximum of photosensitizers into the actinic region for enhanced absorption of sunlight and subsequent enhancement of photosensitizer-driven reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Behavior of iodate substituted ettringite during aqueous leaching

Not Available

Avalos, Nancy M. (ORCID:0000000180734642)↗

DOE OSTI2023

Operational and scaling-up barriers of SOEC and mitigation strategies to boost H 2 production- a comprehensive review

Solid oxide electrolysis cells (SOECs) proffer a flexible and environmentally friendly solution to reduce CO 2 emissions and/or produce H 2 fuel for various applications. Here, the integration of this technology with renewable energy systems makes them even more promising in realizing a near-perpetual green energy infrastructure in the near future. SOEC technology has achieved stupendous improvement in recent years, and some of the limiting factors to the commercialization of this technology have been explored. It is, therefore, necessary to have a succinct outlook of the different factors that contribute to the recent developments in SOEC technology. The current work presents a comprehensive review of the state-of-the-art SOEC component materials and their degradation mechanisms, SOEC modeling essentials and efficiencies, and the scalability of SOECs. Although SOEC is considered a highly promising technology and sufficient multifaceted research has been done, yet not been widely commercialized. Despite the improvement to mitigate degradation of the SOEC materials, a large share of the degradation concerns sprang from charge transfer reaction at the triple-phase boundaries of the H 2 -electrode. Part of which can be improved by microkinetic modeling of these materials supported by their characterization to give insight into their development. Some other directions for the future in advancing this technology toward the commercialization stage are thoroughly addressed.

08 HYDROGEN↗

DOE OSTI2020

Insights into the Biogeochemical Cycling of Cobalt: Precipitation and Transformation of Cobalt Sulfide Nanoparticles under Low-Temperature Aqueous Conditions

Cobalt sulfide precipitates, key phases in the natural biogeochemical cycling of cobalt and in relevant remediation and resource recovery processes, remain poorly defined under low-temperature aqueous conditions. Here, we systematically studied the Co-(Fe) sulfides precipitated and aged in environmentally relevant solutions, defined by different combinations of pH, initial cobalt-to-iron ratios ([Co] aq /[Fe] aq ), with/without S 0 , and the presence/absence of sulfate-reducing bacteria. The initial abiogenic precipitates without Fe aq were composed exclusively of amorphous Co-sulfide hydrates (CoS· x H 2 O), whose estimated log K* was three orders of magnitude higher than that previously reported for crystalline Co-sulfides. The addition of S 0 , in combination with acidic pH and elevated temperature (60°C), resulted in the recrystallization of the amorphous precipitates into nanocrystalline jaipurite (hexagonal CoS) within one month (which otherwise remained stable for up to two months). In the presence of Fe(II) aq , the abiogenic precipitates were composed of more crystalline Co-sulfide hydrates and/or Co-rich mackinawite, the exact phase being dependent on the specific [Co] aq /[Fe(II)] aq . The biogenic precipitates displayed higher crystallinity for Co sulfides (up to the formation of nanocrystalline cobalt pentlandite, Co 9 S 8 ) and lower crystallinity for Co-rich mackinawite, suggestive of mineral-specific bacterial interaction. The revealed precipitation and transformation pathways of Co-(Fe-)sulfides in this study allows for predicting the prevalent Co-bearing phases in various natural and engineered environments.

58 GEOSCIENCES↗

DOE OSTI2022

Stable and Efficient Single-Atom Zn Catalyst for CO2 Reduction to CH4

Explore the source record for details and available documents.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

IDAES-PSE 2.2.0 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 in the 2.2 release include: DiagnosticsToolbox DiagnosticsToolbox (idaes.core.util.model_diagnostics. DiagnosticsToolbox is a new self-guiding toolbox for assisting with diagnosing modeling issues. The toolbox contains a wide range of tools for identifying common modeling issues and conveying these to the user, and more features will be added in the coming months. To try the new toolbox, use the following steps: from idaes.core.util import DiagnosticsToolbox Build an instance of your model Ensure your models has zero degrees of freedom (as best you can) dt = DiagnosticsToolbox(model) dt.report_structural_issues() Follow the prompts Deprecations DegeneracyHunter is being deprecated in favor of the new DiagnosticsToolbox. The features and capabilities of DegeneracyHunter will be moved over to the DiagnositcsToolbox in the coming months. Removal of usage of Pyomo's expr.current due to deprecation upstream. Removed support for radial basis functions in ALAMOpy as these did not work in the current implementation. Those wishing to use radial basis functions should use Pysmo instead. New Capabilities MSContactor model for unit operations involving mass transfer between 2 or more streams, such as membrane, solvent extraction and solids leaching operations. Other Improvements Improved formulation for solubility products with additional parameters for better scaling of terms Better backward compatibility, verification and robustness testing of core models and APIs Added support for Separator models without energy balances

DiagnosticsToolbox↗

DOE OSTI2023

IDAES-PSE 2.1.0 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 2.1.0 Release Highlights New IDAES Examples Repository Starting with this release, the IDAES examples are developed in the new IDAES/examples repository. Along with many content and usability improvements, the most significant changes are: To install the examples, after installing IDAES, run pip install idaes-examples The idaes get-examples command, previously used for this, has been removed The HTML version is now available at https://idaes-examples.readthedocs.io The previous URL, https://idaes.github.io/examples-pse, will not be updated and may be removed at some point in the future For more details, refer to the resources available at IDAES/examples. Removal of Non-Functional Apps A review of the code in the idaes/apps and idaes/models_extra folders was undertaken, and a number of tools were identified as being outdated or non-functional and no longer supported by their development teams. Due to this, the following tools have been removed: idaes/apps/alamopy_depr (note that the new ALAMOpy interface remains avaialble in idaes/core/surrogates) idaes/apps/helmet idaes/apps/ripe idaes/apps/roundingRegression idaes/models_extra/carbon_capture Pyomo 6.6 This version of IDAES is the first requiring Pyomo 6.6. This version of Pyomo contains multiple internal improvements and refactorings. While for the majority of cases this should have positive or no impact on solvability of IDAES models, we are aware of a small number of models that have been affected as a result of these changes. For more information, refer to the Pyomo 6.6.1 release notes. Other highlights Model Initialization A prototype API for a new approach to initializing IDAES models is now available which makes available some new techniques for initializing models. This is documented in the Initializing Models Reference Guide Modular Properties Framework Support for some transport properties Helmholtz Equation of State properties Better error checking for case where unit models are set to include phase equilibrium but the property package is set to support only a single phase Multi-Stream Contactor model: a new base model for systems involving contacting of two or more streams with mass transfer. This model is intended to be used as the foundation for models such as membrane separators, solvent extraction and other similar processes. This is documented in the Multi-Stream Contactor Reference Guide idaes/models_extra/power_generation report() methods for unit models using Helmholtz equation of state General Code Maintenance Streamlining of dependencies and creation of new optional dependency groupings to support non-core tools General linting of codebase to ensure compliance with most pylint checks Spell checking of all code and doc strings Removal of backward compatibility code for Python 2

IDAES↗

DOE OSTI2023

IDAES-PSE 2.0 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 2.0.0 Release Highlights Removal of deprecated features from IDAES v1 Update to Pyomo v6.5 – this required a number of updates to support the new NL solver writer and to address some changes in Pyomo Creation of new testing suite for backward compatibility, model robustness and verification More general implementation of the Helmholtz EoS. This brings some new features like standard property diagrams, choice of mass or mole basis, and new state variable options Standardizing names in Heat Exchanger models (breaking change from v2.0.0a2): Control Volumes named hot_side and cold_side Ports names hot_side_inlet, hot_side_outlet, cold_side_inlet and cold_side_outlet Config Blocks names hot_side_config and cold_side_config Config arguments for user provided names for each side: hot_side_name and cold_side_name. Updating Keras surrogate tool to use v1.1 of OMLT New prototype API for model initialization (idaes.core.initialization) The new API uses "Model Initializer" objects instead of class methods, allowing for the definition of multiple initialization routines for a single model A number of common, model agnostic initialization routines have also been defined, including initialization from data, block-decomposition and a general hierarchical approach equivalent to the existing method for common unit models New metadata for thermophysical properties – valid_range This can be used to record the range of values over which a property value can be trusted, such as the range of experimental data used to regress parameters A number of new utility functions have been added to check for properties with values outside the valid range and to set bounds based on this metadata Updated construction of balance expressions in Control Volumes to remove unneeded terms In the past, unneeded terms were added as a constant 0 term, however they will now be dropped entirely from the expression This was necessary due to more strict unit checking in the new Pyomo solver writer which no longer ignores 0 terms Updates to metadata for thermophysical properties to better define known properties and units of measurement This results in more strict enforcement of standard naming for thermophysical and reaction properties Users can still define custom properties, but these must be done explicitly using the define_custom_properties() method instead of being implicitly created by add_property() Updated convergence tester utility tool to support definition of benchmark files (JSON format) and comparison of performance to benchmarks Set default iteration limit for IPOPT in IDAES config to 200 iterations Update scaling of example models to work with new Pyomo NL solver writer Improve testing of extensions and examples infrastructure to avoid need for downloading files Updated distillation column to centralize common functionality and remove a number of Pyomo warnings

IDAES↗

DOE OSTI2022

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↗

DOE OSTI2023

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↗

DOE OSTI2024

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↗