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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

IDAES↗

DOE OSTI2023

IDAES-PSE 2.3.0 Release

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↗

DOE OSTI2024

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↗

DOE OSTI2020

Accounting for erroneous model structures in biokinetic process models

In engineering practice, model-based design requires not only a good process-based model, but also a good description of stochastic disturbances and measurement errors to learn credible parameter values from observations. However, typical methods use Gaussian error models, which often cannot describe the complex temporal patterns of residuals. Consequently, this results in overconfidence in the identified parameters and, in turn, optimistic reactor designs. Here, we assess the strengths and weaknesses of a method to statistically describe these patterns with autocorrelated error models. This method produces increased widths of the credible prediction intervals following the inclusion of the bias term, in turn leading to more conservative design choices. However, we also show that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design.

42 ENGINEERING↗

DOE OSTI2013

Development of ITM Oxygen Technology for Integration in IGCC and Other Advanced Power Generation DECISION POINT 1 UNDER PHASE 3

Air Products and the DOE have partnered over a number of years in the development of ITM Oxygen technology in support of gasification technology. Throughout this process, studies of application of the technology to IGCC and oxy-coal combustion have shown significant reduction in capital and operating costs compared to similar systems using conventional cryogenic air separation. Phase 3, the current phase of the program, focuses on the design, construction and operation of a 30- to 100-TPD pilot facility, the Intermediate Scale Test Unit (ISTU). Execution of this phase to date has resulted in significant advances in a number of areas including ceramic membrane material development, module design and production, ceramic-to-metal seal design, process control strategies, and engineering development of process cycles. Phase 3 will be complete upon successful operation of the ISTU in a series of tests making oxygen from ceramic membrane modules and producing power from a hot gas expander. Phase 3 work has extended beyond the planned schedule due to a delay in delivery of equipment from vendors. Air Products is currently managing the equipment delay by close involvement with the vendor to redesign the problematic equipment and oversee its fabrication. The result of these unforeseen challenges is that the ISTU project completion date has been delayed. Tight cost controls have been implemented both by DOE program management and APCI to meet budget constraints despite increased costs due to budget delays. Total project costs have increased in several areas. Increased costs in the ISTU project include purchased equipment, instruments, construction, and contractor engineering. Increased costs for other tasks include additional work in support of module production by Ceramatec, Inc, and increased Air Products labor for component testing. Air Products plans to complete testing as outlined in the SOPO and successfully complete all project objectives by the end of FY14.

Anderson, Lori↗

DOE OSTI2020

CsI-Antisolvent Adduct Formation in All-Inorganic Metal Halide Perovskites

The excellent optoelectronic properties shown by hybrid organic/inorganic metal halide perovskites are all predicated on precisely controlling the exact nucleation and crystallization dynamics that occur during film formation. In general, high-performance thin films are obtained by a method commonly called solvent engineering (or antisolvent quench) processing. The solvent engineering method removes excess solvent, but importantly leaves behind solvent that forms chemical adducts with the lead-halide precursor salts. These adduct-based precursor phases control nucleation and the growth of the polycrystalline domains. There has not yet been a comprehensive study comparing the various antisolvents used in different perovskite compositions containing cesium. In addition, there have been no reports of solvent engineering for high efficiency in all-inorganic perovskites such as CsPbI 3 . In this work, inorganic perovskite composition CsPbI 3 is specifically targeted and unique adducts formed between CsI and precursor solvents and antisolvents are found that have not been observed for other A-site cation salts. These CsI adducts control nucleation more so than the PbI 2 -dimethyl sulfoxide (DMSO) adduct and demonstrate how the A-site plays a significant role in crystallization. The use of methyl acetate (MeOAc) in this solvent engineering approach dictates crystallization through the formation of a CsI-MeOAc adduct and results in solar cells with a power conversion efficiency of 14.4%.

14 SOLAR ENERGY↗

DOE OSTI2019

Integrating narratives into decision making for complex systems engineering design issues

Abstract Engineering decision making and design requires collaboration between groups from different disciplines, each with different tools, vocabulary, and concerns. Traditional engineering decision‐making tools are generally based on understanding the decision makers’ values, modeling uncertainty with probability, and selecting the alternative that maximizes utility. This rational approach to decision making may not be well understood or used by many stakeholders involved in the engineering design process. Constructing narratives, a basic means of human communication, may aid in engineering communication and comprehension and help with decision making. Narratives represent events by means of a story and usually include characters or agents who cause events and to whom events happen. This paper recommends three methods for how the use of narrative can be applied to the area of engineering decision making. These methods include connecting the decision maker to the analysis, creating narrative simulations for training decision makers, and fostering consensus in problems with multiple stakeholders. An illustrative example of designing a better cookstove for the developing world demonstrates the role that understanding narratives of various stakeholders can play for accomplishing complex systems engineering.

MacKenzie, Cameron A.↗

DOE OSTI2014

Review on characterization of nano-particle emissions and PM morphology from internal combustion engines: Part 2 [Review on morphology and nanostructure characterization of nano-particle emission from internal combustion engines]

This study presents a review of the characterization of physical properties, morphology, and nanostructure of particulate emissions from internal combustion engines. Because of their convenience and readiness of measurement, various on-line commercial instruments have been used to measure the mass, number, and size distribution of nano-particles from different engines. However, these on-line commercial instruments have inherent limitations in detailed analysis of chemical and physical properties, morphology, and nanostructure of engine soot agglomerates, information that is necessary to understand the soot formation process in engine combustion, soot particle behavior in after-treatment systems, and health impacts of the nano-particles. For these reasons, several measurement techniques used in the carbon research field, i.e., highresolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD), and Raman spectroscopy, were used for analysis of engine particulate matter (PM). This review covers a brief introduction of several measurement techniques and previous results from engine nano-particle characterization studies using those techniques.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Cooperative Research and Development Agreement between National Energy Technology Laboratory and Korea Institute of Energy Research [Abstract]

The Korea Institute of Energy Research (KIER) and the National Energy Technology Laboratory (NETL) intend to collaborate under a CRADA to advance the development of oxy-combustion technology, with an emphasis on process modeling, optimization, and techno-economic analysis. This collaboration will result in the development of a full-scale process model of a commercial oxy-fueled circulating fluid bed combustion (oxy-CFBC) plant, informed from pilot-scale data and built using the Institute for the Design of Advanced Energy Systems (IDAES) process systems engineering framework. It will also generate cost comparisons of the optimized oxyCFBC process with an air-CFBC process with and without carbon capture and sequestration.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2023

Enhancing Early Systems R&D Capabilities with Systems —Theoretic Process Analysis

Systems engineering today faces a wide array of challenges, ranging from new operational environments to disruptive technological — necessitating approaches to improve research and development (R&D) efforts. Yet, emphasizing the Aristotelian argument that the “whole is greater than the sum of its parts” seems to offer a conceptual foundation creating new R&D solutions. Invoking systems theoretic concepts of emergence and hierarchy and analytic characteristics of traceability, rigor, and comprehensiveness is potentially beneficial for guiding R&D strategy and development to bridge the gap between theoretical problem spaces and engineering-based solutions. In response, this article describes systems–theoretic process analysis (STPA) as an example of one such approach to aid in early-systems R&D discussions. STPA—a ‘top-down’ process that abstracts real complex system operations into hierarchical control structures, functional control loops, and control actions—uses control loop logic to analyze how control actions (designed for desired system behaviors) may become violated and drive the complex system toward states of higher risk. By analyzing how needed controls are not provided (or out of sequence or stopped too soon) and unneeded controls are provided (or engaged too long), STPA can help early-system R&D discussions by exploring how requirements and desired actions interact to either mitigate or potentially increase states of risk that can lead to unacceptable losses. This article will demonstrate STPA's benefit for early-system R&D strategy and development discussion by describing such diverse use cases as cyber security, nuclear fuel transportation, and US electric grid performance. Together, the traceability, rigor, and comprehensiveness of STPA serve as useful tools for improving R&D strategy and development discussions. In conclusion, leveraging STPA as well as related systems engineering techniques can be helpful in early R&D planning and strategy development to better triangulate deeper theoretical meaning or evaluate empirical results to better inform systems engineering solutions.

42 ENGINEERING↗

DOE OSTI2021

A generalized machine learning workflow to visualize mechanical discontinuity

Accurate detection and mapping of mechanical discontinuity in materials has widespread industrial and research applications. Herein, we developed a generalized machine-learning framework for visualizing single mechanical discontinuity embedded in material of any composition, velocity, density, porosity, and size with limited data. The proposed visualization of discontinuity requires accurate estimations of the length, location, and orientation of the embedded discontinuity by processing multipoint wave-transmission measurements. k-Wave simulator is used to create a large dataset of elastic waveforms recorded during multi-point wave-transmission measurements through materials containing single mechanical discontinuity. k-Wave simulator considers the wave attenuation, dispersion, and mode conversion in wave motion. Discrete wavelet transform (DWT) and statistical feature extraction are essential for data preprocessing prior to the data-driven model development. DWT also minimizes the effect of noise. Using hyper-parameter tuning and cross validation, gradient boosting regression can visualize the mechanical discontinuity with an accuracy of 0.85, in terms of coefficient of determination. A double-layered neural network-based regression has better performance with an accuracy of 0.95. Use of convolutional neural network converts the predictive task from a waveform processing to an image processing problem. Convolutional neural network achieved a generalization performance of 0.91. The proposed generalized workflow requires robust simulation of wave propagation, signal processing, feature engineering, and model evaluation. Sensors closest to the source and those located opposite the source are the most significant for the desired visualization. Notably, the sensors closest to the source capture the non-linear associations, whereas the sensor on the border opposite to the source capture the linear associations between the measured waveforms and the properties of the mechanical discontinuity.

42 ENGINEERING↗

DOE OSTI2015

Recovery Act. Demonstration of a Pilot Integrated Biorefinery for the Efficient, Direct Conversion of Biomass to Diesel Fuel

The Renewable Energy Institute International, in collaboration with Greyrock Energy and Red Lion Bio-Energy (RLB) has successfully demonstrated operation of a 25 ton per day (tpd) nameplate capacity, pilot, pre-commercial-scale integrated biorefinery (IBR) plant for the direct production of premium, “drop-in”, synthetic fuels from agriculture and forest waste feedstocks using next-generation thermochemical and catalytic conversion technologies. The IBR plant was built and tested at the Energy Center, which is located in the University of Toledo Medical Campus in Toledo, Ohio.

03 NATURAL GAS↗

DOE OSTI2026

Dynamics and observational signatures of core-collapse supernovae with central engines: hydrodynamics simulations with Monte Carlo post-processing

A long-lived central engine embedded in expanding supernova ejecta can alter the dynamics and observational signatures of the event, producing an unusually luminous, energetic, and/or rapidly evolving transient. We use 2D hydrodynamics simulations to study the effect of a central energy source, varying the amount, rate, and isotropy of the energy deposition. We post-process the results with a time-dependent Monte Carlo radiation transport code to extract observational signatures. The engine excavates a bubble at the centre of the ejecta, which becomes Rayleigh–Taylor unstable. Sufficiently powerful engines are able to break through the edge of the bubble and accelerate, shred, and compositionally mix the entire ejecta. The breakout of the engine-driven wind occurs at distinct rupture points, and the outflowing high-velocity gas may eventually give rise to radio emission. The dynamical impact of the engine leads to faster rising optical light curves, with photon escape facilitated by the faster expansion of the ejecta and the opening of low-density channels. For models with strong engines, the spectra are initially hot and featureless, but later evolve to resemble those of broad-line Ic supernovae. Under certain conditions, line emission from ionized, low-velocity material near the centre of the ejecta may be able to escape and produce narrow emission similar to that seen in interacting supernovae. We discuss how variability in the engine energy reservoir and injection rate could give rise to a heterogeneous set of events spanning multiple observational classes, including the fast blue optical transients, broad-line Ic supernovae, and superluminous supernovae.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Systematic Literature Review: How is Model-Based Systems Engineering Justified?

The genesis for this systematic literature review was to search for industry case studies that could inform a decision of whether or not to support the change process, investment, training, and tools needed to implement an MBSE approach across the engineering enterprise. The question asked was, how the change from a document - based systems engineering approach (DBSE) to a model-based systems engineering approach (MBSE) is justified? The methodology employed for this systematic literature review was to conduct a document search of electronically published case studies by authors from the defense, space, and complex systems product engineering industries. The 67 case studies without metrics mainly attributed success to completeness, consistency, and communication of requirements. The 21 case studies with metrics on cost and schedule primarily attributed success to the ability of an MBSE approach to improve defect prevention strategies. The primary conclusion is that there is a significant advantage to project performance by applying an MBSE approach. An MBSE approach made the engineering processes on a complex system development effort more efficient by improving requirements completeness, consistency, and communication. These were seen in engineering processes involved in requirements management, concept exploration, design reuse, test and qualification, Verification and Validation, and margins analyses. An MBSE approach was most effective at improving defect prevention strategies. The approach was found to enhance the capability to find defects early in the system development life cycle (SDLC), when they could be fixed with less impact and prevented rework in later phases, thus mitigating risks to cost, schedule, and mission. However, if a program only employed an MBSE approach for requirements management, advantages from finding defects early could not be leveraged in later phases, where the savings in cost and schedule from rework prevention is realized. Significant performance success was achieved when the systems engineer (SE) held a leadership role over engineering process es. A number of the case studies addressed a general lack of skilled MBSE engineers as a major hindrance to implementing an MBSE approach successfully.

42 ENGINEERING↗

DOE OSTI2024

Opportunities for Process Intensification with Membranes to Promote Circular Economy Development for Critical Minerals

Critical minerals are essential to the future of clean energy, especially energy storage, electric vehicles, and advanced electronics. In this paper, we argue that process systems engineering (PSE) paradigms provide essential frameworks for enhancing the sustainability and efficiency of critical mineral processing pathways. As a concrete example, we review challenges and opportu-nities across material-to-infrastructure scales for process intensification (PI) with membranes. Within critical mineral processing, there is a need to reduce environmental impact, especially con-cerning chemical reagent usage. Feed concentrations and product demand variability require flex-ible, intensified processes. Further, unique feedstocks require unique processes (i.e., no one-size-fits-all recycling or refining system exists). Membrane materials span a vast design space that allows significant optimization. Therefore, there is a need to rapidly identify the best opportunities for membrane implementation, thus informing materials optimization with process and infrastructure scale performance targets. Finally, scale-up must be accelerated and de-risked across the materials-to-process levels to fully realize the opportunity presented by membranes, thereby fostering the development of a circular economy for critical minerals. Tackling these challenges requires integrating efforts across diverse disciplines. We advocate for a holistic molecular-to-systems perspective for fully realizing PI with membranes to address sustainability challenges in critical mineral processing. The opportunities for PI with membranes are excellent applications for emerging research in machine learning, data science, automation, and optimization.

Dougher, Molly↗

DOE OSTI2015

Are We Preparing the Next Generation? K-12 Teacher Knowledge and Engagement in Teaching Core STEM Practices

Several of the recent reform efforts in K-12 STEM education (e.g. Next Generation Science Standards [NGSS and Common Core State Standards-Mathematics [CCSS-M]) have included significant emphasis on the practices of STEM. We argue that K-12 teachers’ ability to effectively engage their students in these core STEM practices is fundamental to the success of potential and current engineering students and their subsequent careers as engineers. Practices such as identifying problems, modeling using mathematics, and arguing from evidence are fundamental processes in engineering. Helping students develop their capacity to engage in these practices early in their education will increase the likelihood of the students applying the practices and developing skills aligned with the work of engineers. We contend that engaging in the practices associated with engineering may increase K-12 student interest and the successful pursuit of engineering as a career because they will find relevance in what is being taught and gain knowledge of the applications of STEM content which will help them develop talents aligned with the work of engineers.

99 GENERAL AND MISCELLANEOUS↗