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At least 361 records · Page 20

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Chemistry↗

Ch. 12. A Theoretical Approach to Management of Limited Attentional Resources to Support the m:N Operation in Advanced Air Mobility Ecosystem

Advanced air mobility (AAM) technologies incorporate increasingly autonomous systems that allow fully remote, independent, and intelligent operation of air vehicles to support the transportation of goods and passengers within and across urban and rural areas. With a myriad of automated technologies enabling the AAM ecosystem, the human operator’s role will likely be a passive supervisory monitor of the air vehicles, involving increasingly fewer humans (m) that manage many more autonomous systems (N), or m:N operations. Unfortunately, the human performance literature suggests that human operators will exhibit poor supervision of numerous autonomous agents due to the limits of attentional resources in the operators. In the general human information-processing model, a human operator exercises a limited pool of attentional resources to engage various information-processing stages including detecting, perceiving, comprehending, and predicting objects around them. Yamani and Horrey (2018) expanded the human information-processing model to characterize a tradeoff between information-processing demand and resource relief that automation brings in the context of automated driving. In their model, a driver interacting with an automated driving system is assumed to reallocate resources “freed” by automation to support other information-processing stages required for successful task performance. A future AAM ecosystem enabled by an orchestration of advanced automated systems, however, requires a single operator to interact with more than one air vehicle with varying levels and degrees of automated systems, making the traditional framework of human-automation interaction insufficient. To address this gap, we provide a review of the literature on situation assessment and trust, two constructs identified as critical for a fuller understanding of intimate and intricate interactions between a human operator and multiple air vehicles equipped with increasingly autonomous systems. Then, we propose an expansion of Yamani and Horrey’s (2018) model to motivate systematic research on the human operator’s role, identify factors that influence resource allocation and guide human-centered design of an interface supporting the m:N operation in the AAM environment.

Advanced Air Mobility↗

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↗

Development of the INL Thermal Energy Distribution System (TEDS) in the Modelica Eco-System for Validation and Verification

This report provides an overview on the development of the Thermal Energy Distribution System (TEDS) in the Modelica process model eco-system at Idaho National Laboratory as part of the integrated energy system initiative. Model development has led to the creation of a dynamic process model of the experimental TEDS facility housed within the Engineering Science Laboratory (ESL) at INL. The model was then used during the pre-construction phase of the experimental effort to inform experimental design (insulation requirements, bypass line placement, expected performance of components) and to test innovative control schemes prior to the initial operation. The TEDS model developed in Modelica includes the primary components of the TEDS experimental unit including: a 200kW Chromalox heater, a single-tank packed bed thermal energy storage system filled with 0.125 inch Alumina (Al 2 O 3 ), an ethylene-glycol to Therminol-66 heat exchanger, system piping, six control valves, and all associated temperature, pressure, and mass flow sensors. The model does not include nitrogen fill gas tanks, or associated overfill tanks, as these are not part of standard system control. Two simulation sets were run. The first is a five-hour test that operates as a shakedown test for the facility. This simulation puts the facility through all five potential operating modes and showcases the ability of valving, control sensors, and component controllers to meet the system demands. The second case imposes a typical summer day demand on the system from a region with mixed commercial and residential electrical needs where the generator alone cannot meet peak demand but instead requires the thermal storage unit to act as a peaking unit. Over the course of the fifteen-hour discharge cycle heat losses account for a heat loss of approximately 15°C in outlet temperature. Such heat losses would lead to a decrease in overall system efficiency and may lead to additional design changes for such designs in the future that may include a topping heater. Through commencement of this work, a systems level model of TEDS with associated control systems, sensors, piping diameters, and component capabilities has been created. This model has been utilized in the pre-experimental phase to inform system design, insulation thicknesses, and potential control schemes to operate the system effectively and safely. Once TEDS begins operation, this model will be refined, tuned, and used for validation and verification purposes.

24 POWER TRANSMISSION AND DISTRIBUTION↗

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↗

Intelligent alarming

This talk discusses the importance of providing a process operator with concise information about a process fault including a root cause diagnosis of the problem, a suggested best action for correcting the fault, and prioritization of the problem set. A decision tree approach is used to illustrate one type of approach for determining the root cause of a problem. Fault detection in several different types of scenarios is addressed, including pump malfunctions and pipeline leaks. The talk stresses the need for a good data rectification strategy and good process models along with a method for presenting the findings to the process operator in a focused and understandable way. A real time expert system is discussed as an effective tool to help provide operators with this type of information. The use of expert systems in the analysis of actual versus predicted results from neural networks and other types of process models is discussed.

Braden, W. B.↗

Integrating Theory and Practice: Applying the Quality Improvement Paradigm to Product Line Engineering

My assertion is that not only are product lines a relevant research topic, but that the tools used by empirical software engineering researchers can address observed practical problems. Our experience at NASA has been there are often externally proposed solutions available, but that we have had difficulties applying them in our particular context. We have also focused on return on investment issues when evaluating product lines, and while these are important, one can not attain objective data on success or failure until several applications from a product family have been deployed. The use of the Quality Improvement Paradigm (QIP) can address these issues: (1) Planning an adoption path from an organization's current state to a product line approach; (2) Constructing a development process to fit the organization's adoption path; (3) Evaluation of product line development processes as the project is being developed. The QIP consists of the following six steps: (1) Characterize the project and its environment; (2) Set quantifiable goals for successful project performance; (3) Choose the appropriate process models, supporting methods, and tools for the project; (4) Execute the process, analyze interim results, and provide real-time feedback for corrective action; (5) Analyze the results of completed projects and recommend improvements; and (6) Package the lessons learned as updated and refined process models. A figure shows the QIP in detail. The iterative nature of the QIP supports an incremental development approach to product lines, and the project learning and feedback provide the necessary early evaluations.

Stark, Michael↗

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 2.0 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↗

Description of statistical methods and a routine for determining the parameters of a model in processing experimental results

A computer routine is suggested for selecting the optimum parameters of a theoretical model and determining the errors in them due to errors in physical measurements and for evaluating the conformity of theory with the experiment. The paper describes the specification sequence for the input data and the format of the calculation results. Sample printouts are appended.

Usikov, D. A.↗

IDAES-PSE 2.6.0 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 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 2.6.0 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 Intersphinx extension automatically linking Jupyter notebook examples to project documentation New end-to-end diagnostics example demonstrated on a real problem New complementarity formulation for VLE with cubic equations of state, backward compatibility for old formulation New solver interface with presolve (ipopt_v2) in support of upcoming changes to the initialization and APIs methods, with default set to ipopt to maintain backwards compatibility; this will deprecate once all examples have been updated New forecaster and parameterized bidder methods within grid integration library Updated surrogates API and examples to support Keras 3, with backwards compatibility for older formats such as TensorFlow SavedModel (TFSM) Updated costing base dictionary to include the 2023 cost year index value Updated ProcessBlock to include information on the constructing block class Updated Flowsheet Visualizer to allow visualize() method to return value and functions Bug Fixes 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. Fixed typos flagged by June update to crate-ci/typos and removed DMF-related exceptions Minor corrections of units of measurement handling in power plant waste/transport costing expressions, control volume material holdup expressions, and BTX property package parameters Fixed throwing >7500 numpy deprecation warnings by replacing scalar value assignment with element extraction and item iteration calls Testing and Robustness Migrated slow tests (>10s) to integration, impacting test coverage but also yielding a nearly 30% decrease in local test runtime Pinned pint to avoid issues with older supported Python versions Pinned codecov versions to avoid tokenless upload behavior with latest version Bumped extensions to version 3.4.2 to allow pointing to non-standard install location Deprecations and Removals Python 3.8 is no longer supported. The supported Python versions are 3.9 through 3.12 The Data Management Framework (DMF) is no longer supported. Importing idaes.core.dmf will cause a deprecation warning to be displayed until the next release The SOFC Keras surrogates have been removed. The current version of the SOFC surrogate model in the examples repository is a PySMO Kriging model.

AS↗

Integrating field observations and process-based modeling to predict watershed water quality under environmental perturbations

Watersheds play a critical role in supplying water resources needed for human use and ecosystem health. Understanding and predicting how, when, and where changes in the quantity and quality of water resources occur under different environmental stresses including extreme events is crucial for sustainable management of water resources under a changing environment. However, few studies have attempted to quantify or identify the factors and process interactions controlling the impact of extreme events across water-shed systems. Only few large-scale studies include coordinated monitoring and modeling efforts, which limits our ability to assess the large-scale impact of extreme events on water supply and quality. Methods are lacking to propagate uncertainty in process understanding through an integrated hydro-biogeochemical model framework and evaluate its importance, thus failing to take full advantage of the information potentially available through transformative advances in characterization technologies from high-resolution mass spectrometry to airborne and satellite-based remote sensing. There are consequent risks to our nations water security and to human and ecosystem health that may become exacerbated with the increasing frequency of extreme events that is projected for the coming decades. This paper reviews the current status of watershed science for both water quantity and quality and identifies critical gaps in our current knowledge and modeling capability in addressing the emergent needs in predicting watershed hydrologic and biogeochemical responses (i.e., water quantity and quality) under natural and anthropogenic perturbations. We highlight the need to (1) understand how environmental perturbations including extreme events like floods and droughts propagate through watershed systems and assess their short- and long-term impacts on watershed biogeochemistry, water quality and their recovery pathways; (2) develop and improve a watershed water quality model that reflects the state of scientific understanding gained from observations; and (3) construct a data-model fusion system for watershed characterization, process identification, and mechanistic model parameterization. A large base of modeling, monitoring and data capabilities have been built by various federal government agencies given the relevance of water to their critical missions. An emerging need is to build an integrated national capability for watershed water availability and quality that can address water-related missions across multiple federal agencies.

13 HYDRO ENERGY↗

A Gaussian process autoregressive model capturing microstructure evolution paths in a Ni–Mo–Nb alloy

Additive manufacturing is increasingly being employed to produce components of complex geometries in structural alloys because of the expected energy savings associated with the near-net-shape capability and the ability to build in novel internal features that are not possible with many conventional manufacturing approaches. However, because of the extreme thermal conditions encountered, the non-equilibrium microstructures produced during powder bed-based additive manufacturing processes must be subjected to custom post-heat treatment processes to recover the target mechanical properties. Phase-field models and simulation techniques have matured to a state where the microstructure evolution paths, and the morphologies of the resulting precipitate phases can be predicted reasonably accurately, considering alloy-specific thermodynamic and kinetic aspects of the nucleation and growth processes. However, phase-field simulations are computationally intensive, which precludes the ability to apply the simulations directly to the length scale of the entire component. Therefore, it is highly desirable to develop low-computational-cost surrogate models that effectively capture the physics at the microstructural length scale, while facilitating the design of optimized processing conditions resulting in location-specific targeted microstructures at the component scale. The work presented here demonstrates the application of the materials knowledge system framework to develop a surrogate model that effectively captures the microstructural path during annealing of a Ni–Mo–Nb alloy containing different Mo and Nb compositions known to segregate during solidification under additive manufacturing conditions. Specifically, the surrogate model built in this work is based on a Gaussian process autoregressive model informed by statistical representation of simulated microstructures using two-point correlations and dimensionality reduction through principal component analysis. In conclusion, this surrogate model is shown to capture the bifurcation of the microstructural path during precipitation, which yields a microstructure dominated by the $\gamma^{\prime\prime}$ phase at high Nb concentrations and the $\delta$ phase at low Nb concentrations.

36 MATERIALS SCIENCE↗

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↗

A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Culex pipiens/restuans Mosquito Density

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration, and validation is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a discrete, semi-stochastic, mechanistic process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens (Diptera, Culicidae) and Culex restuans (Diptera, Culicidae) mosquito populations. This model combines known models for development and survival into a fully connected age-structured model that can reproduce mosquito population dynamics. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like West Nile virus.

59 BASIC BIOLOGICAL SCIENCES↗

Integrated Bosch Process System Models for In-Situ Oxygen and Carbon Production

In-Situ Resource Utilization (ISRU) technology is a vital component to NASA’s mission of a sustainable presence on the Moon and Mars. Local resources can be leveraged to reduce resupply frequency and mass. Elements of the Bosch process, combined with the carbothermal reduction process, can produce oxygen on the lunar surface with minimal consumables. The Bosch process can also produce oxygen on the Martian surface by using the CO 2 -rich environment. Between both systems, adsorption pump, solar thermal energy, carbon formation reactor, and water recovery subsystems are modeled and integrated to create a functional model in MATLAB software. The model is used to simulate performance of the system and reduce mass, power, and volume requirements. This integrated system model provides a tool to scale ISRU technologies for oxygen and carbon production. The MATLAB model is created by developing a system of independent subsystem models that are solved for their quasi-steady state values which can be integrated with respect to time to determine the change in current states. A flexible time stepping method is used to ensure a high level of accuracy during periods of rapid change while still making use of a simple explicit integration method. The flexible time step is calculated for each independent subsystem and the minimum value from those is used as the overall time step. A flexible time step is calculated by dividing a resolution value, or the maximum change per time step, by the variables current rate of change. The maximum value from all points in space is used for subsystem models that contain multiple values. The process is done for every variable that is being monitored in each subsystem and the global minimum is used as that iteration’s timestep. Several assumptions used in the MATLAB model for fluid flow dynamics, such as 1-D gas flow through the sorption pump, are supported by modeling in Ansys Fluent software. The Lunar oxygen production system is outlined in Fig. 1. The carbothermal reduction subsystem uses solar energy to heat a mixture of lunar regolith and carbon powder to produce carbon monoxide. To begin, the carbon monoxide feeds to the modified Bosch subsystem along with hydrogen gas. The reactants then enter the carbon formation reactor where water and carbon powder are produced. Solar thermal energy is used to add energy to the reactor, but waste heat from the carbothermal process is another potential heat source. The water is collected and electrolyzed to produce hydrogen which reenters the Bosch subsystem, and the oxygen is stored for downstream use. The carbon powder is collected and feeds back into the carbothermal subsystem. The Martian oxygen production system uses the full Bosch process and is outlined in Fig 2. A CO 2 adsorption pump thermally cycles to scrub and pressurize CO 2 from the environment. Along with an initial supply of hydrogen, the reactants enter the Reverse Water Gas Shift Reactor (RWGSR) which produces carbon monoxide and water. Carbon monoxide and unreacted hydrogen enter the carbon formation reactor to produce water and carbon powder. The water is collected from both reactors and electrolyzed to reintroduce hydrogen and store oxygen for propellant production or life support. Carbon is removed from the carbon formation reactor and stored. The adsorption pump utilizes rapid cycle temperature swings within a stack of zeolite coated surfaces. The subsystem model solves 1-D quasi-steady conservation laws of the quasi-steady form, shown in Eq. 1, for the gas stream and heat exchange liquid to predict performance parameters such as breakthrough capacity and optimum cycle time. The source term S is used to capture interactions between the fluid flows and the sorbent. A quasi-steady-state scheme is used where no time derivatives appear in the governing equations, except for those in the source terms. This results in an autonomous system, where ∂F/∂x = ƒ(F). The fluxes F are provided at the inlet, and an explicit method is used to solve for the spatial distribution of F. The heat and mass flows to the sorbent are then extracted from the source terms. These flows are numerically integrated to produce a 1-D solution for the system’s state as a function of both time and space. The body of the adsorption pump is separated into two semi-independent models: the heat exchanger fluid flow and gas flow through the zeolite coated surfaces. Both models are solved using the above-described method to find a 1-D solution as a function of space and interact only once a timestep is taken. The interaction point is the sorbent through which all heat transfer between the two models must occur. Sorbent mass adsorption is calculated using the Lagergren model, shown in Eq. 2, where the transfer coefficient, λ D , is found by solving a system of nondimensionalized equations derived by using the heat and mass transfer analogy for transport phenomena. Using Grade 544 Type 13X zeolite as the sorbent material, the equilibrium concentration, θ eq , is calculated using the k-site Langmuir isotherm and fit parameters. Additionally, the enthalpy of adsorption used in the model is computed by interpolation of available data [1]. The subsystem model was validated using the Rapid Cycle Temperature Swing Adsorption (RC-TSA) pump. The solar thermal energy subsystem focuses on a solar concentrator concept with a heat exchanger to heat the reactants before entering the carbon formation reactor. The subsystem model assumes a fixed solar flux and reflector efficiency to calculate the reactant temperature given the incoming temperature, pressure, and exchanger geometry. The receiver is a custom manufactured series of copper blocks with serpentine channels to increase its surface area and the residence time of the reactants to heat up to 550 °C. The subsystem model was validated using a heat exchanger developed at NASA Glenn Research Center (GRC). The solar thermal energy subsystem focuses on a solar concentrator concept with a heat exchanger to heat the reactants before entering the carbon formation reactor. The subsystem model assumes a fixed solar flux and reflector efficiency to calculate the reactant temperature given the incoming temperature, pressure, and exchanger geometry. The receiver is a custom manufactured series of copper blocks with serpentine channels to increase its surface area and the residence time of the reactants to heat up to 550 °C. The subsystem model was validated using a heat exchanger developed at NASA Glenn Research Center (GRC).

In situ Resource Utilization↗

BETO 2021 Peer Review - Algal Biofuels Techno-Economic Analysis 1.3.5.200

The objective of NREL's Algal Biofuel Techno-Economic Analysis (TEA) project is to provide process modeling and analysis to support Algae Program activities, utilizing TEA models to relate key process parameters with overall economics for cultivation, processing, and conversion of algal biomass to fuels and coproducts. By quantifying economic implications of key process metrics, TEA models highlight the technical requirements to achieve future program cost goals as well as enabling a means to track progress towards these goals. This project provides high impact and relevance though generation of critical cost data tied to funded research, with our analyses subsequently exercised by BETO to guide program plans, FOA priorities, and other directives. This includes costs for both algal biomass production and downstream conversion, most notably to support BETO's fuel cost targets below $2.5/GGE by 2030. To mitigate a key risk/challenge in constraining our work to academic analyses rooted only in future projections, our work also seeks to provide near-term value to today's algae industry through frequent industry engagement, while maintaining close ties with other BETO collaborators. This project has made numerous accomplishments since the 2019 peer review, including a continued focus on opportunities for value-added products, with related analyses for new pathway opportunities to achieve BETO cost goals and notable State-of-Technology (SOT) improvements over prior cost benchmarks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Large Divergence in Tropical Hydrological Projections Caused by Model Spread in Vegetation Responses to Elevated CO 2

Increasing atmospheric CO 2 and associated global warming are expected to alter the global hydrological cycle, thereby posing widespread threats to freshwater availability. However, future hydrological projections differ greatly between models, particularly over the tropical regions. The large difference between model projections directly limits policy planning efforts, and the responsible modeling processes remain unclear. Here, we identify the primary processes accounting for model differences in tropical hydrological changes using multiple CO 2 sensitivity experiments in the Coupled Model Intercomparison Project. We show that differences in projected changes to tropical evapotranspiration, precipitation, and surface water availability mainly arise from model representations of vegetation cover and stomatal conductance responses to elevated CO 2 and associated changes in atmospheric moisture and circulation. Atmospheric responses to sea surface warming contribute additionally to the divergence in hydrological projections. Given the importance of vegetation responses to elevated CO 2 and associated atmosphere feedbacks, our results underscore the need to improve representations of the vegetation physiological response to rising CO 2 and its coupling to the atmosphere, to provide reliable tropical hydrological projections.

54 ENVIRONMENTAL SCIENCES↗