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Results for “Kinetic model data”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Numerical Simulation of Biogenic Fluid Catalytic Cracking (BFCC) Regenerators at Different Scales with MFIX-Exa

Catalytic Fast Pyrolysis (CFP) is a process that converts biomass into liquid intermediates suitable for transportation fuels by rapidly heating it in the presence of a catalyst, aiming to produce stable oils with reduced oxygen content. During CFP, the catalyst can become deactivated by the accumulation of coke, a carbon-rich deposit formed from the decomposition of biomass components. Unlike in petroleum refining, regenerating coked catalysts from biomass pyrolysis requires specific approaches due to the different chemical nature of the coke formed. An experimental technique, Temperature Programmed Oxidation (TPO), was used to study the de-coking process by gradually increasing temperature while monitoring the production of CO and CO2, which provides data for kinetic modeling. Utilizing data from TPO experiments, coke combustion kinetic model was developed to describe the rate of coke removal at different temperatures, allowing for simulation of regeneration processes. Then kinetic model is integrated into MFIX-Exa for the simulation of Biogenic Fluid Catalytic Cracker (BFCC) regenerator at different scales, enabling analysis of catalyst flow, temperature distribution, and regeneration efficiency under various operating conditions.

biogenic fluid catalytic cracking↗
DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
DOE OSTI2022

Elucidating the role of reactive nitrogen intermediates in hetero-cyclization during hydrothermal liquefaction of food waste

Hydrothermal liquefaction (HTL) has emerged as a promising strategy for converting abundant, water-rich organic streams into an energy-dense, sustainable, biocrude. Despite major strides in improving oil yields and process intensification, a key underlying challenge persists in relating the composition of the feedstock to the ultimate fate of nitrogen, which is especially important for biocrude quality. To elucidate how nitrogen partitions into the respective gas, aqueous, oil and solid hydrothermal products, food waste was treated under HTL conditions (15 wt% solids, 575 K reaction temperature, and 1 hour reaction time) and elemental, chromatographic and mass spectroscopy analyses were performed on the products to establish dominant product classes and to close mass, carbon, and nitrogen balances. Liquid products (aqueous and biocrude) were found to contain nearly 60% of the nitrogen, with a majority of the biocrude-phase nitrogen in the form of amides. Ab initio thermodynamic and kinetic simulations on model reactants were used to evaluate potential reaction pathways involving reactive nitrogen intermediates. A subsequent kinetic model was evaluated at reaction temperatures ranging 300–1000 K and for a range of feedstock compositions to identify primary reaction pathways. The Maillard reaction is revealed to be the dominant pathway for converting reactive nitrogen intermediates into observed products, especially those in which nitrogen is present as a heterocyclic aromatic. Subsequent product analysis by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) confirms strong agreement among the experimental data, kinetic model and proposed Maillard pathways.

09 BIOMASS FUELS↗
DOE OSTI2022

Data-driven, multi-moment fluid modeling of Landau damping

Deriving governing equations of complex physical systems based on first principles can be quite challenging when there are certain unknown terms and hidden physical mechanisms in the systems. In this work, we apply a deep learning architecture to learn fluid partial differential equations (PDEs) of a plasma system based on the data acquired from a fully kinetic model. Here, the learned multi-moment fluid PDEs are demonstrated to incorporate kinetic effect such as Landau damping. Based on the learned fluid closure, the data-driven, multi-moment fluid modeling can well reproduce all the physical quantities derived from the fully kinetic model. The calculated damping rate of Landau damping is consistent with both the fully kinetic simulation and the linear theory. The data-driven fluid modeling of PDEs for complex physical systems may be applied to improve the fluid closure and reduce the computational cost of multi-scale modeling of global systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Data for KETCHUP: Parameterizing of Large-Scale Kinetic Models Using Multiple Datasets with Different Reference States

Repository for Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP can use K-FIT [2] input files. Example K-FIT input files are located in the K-FIT repository at https://github.com/maranasgroup/K-FIT.

Metabolomics↗
DOE OSTI2021

Nitromethane Decomposition via Automated Reaction Discovery and an Ab Initio Corrected Kinetic Model

In the explore the systematic construction of kinetic models from in silico reaction data for the decomposition of nitromethane. Our models are constructed in a computationally affordable manner by using reactions discovered through accelerated molecular dynamics simulations using the ReaxFF reactive force field. The reaction paths are then optimized to determine reaction rate parameters. We introduce a reaction barrier correction scheme that combines accurate thermochemical data from density functional theory with ReaxFF minimal energy paths. We validate our models across different thermodynamic regimes, showing predictions of gas phase CO and NO concentrations and high-pressure induction times that are similar to experimental data. The kinetic models are analyzed to find fundamental decomposition reactions in different thermodynamic regimes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Microwave-Assisted Plastic Upcycling: Dynamic Data Reconciliation, Parameter Estimation, and Kinetic Modeling

Microwave (MW)-assisted catalytic pyrolysis offers a promising pathway for efficient plastic upcycling. This work develops an integrated modeling framework combining dynamic data reconciliation, a temperature-dependent rate model, and a yield model to represent the time-varying production rate of components in MW-assisted LDPE pyrolysis conducted in a batch reactor. An Arrhenius-type rate model with a temperature-dependent reaction order is developed. A biexponential correlation is proposed for the yield of gaseous products that enables to capture the evolving product formation behavior during conversion. In the yield correlation, one term is used to represent the initial increase in yield, reflecting the rapid formation of intermediate or primary products at the early stages of the reaction when a larger fraction of the reactant remains available. As conversion progresses, the influence of this term gradually diminishes. The other term accounts for the subsequent decrease in the predicted yield, representing secondary reactions such as further cracking or coke formation that reduce the concentration of certain products at higher conversion. The model is found to accurately represent reconciled experimental flow rate profiles from an in-house MW-assisted catalytic batch reactor for major products, including ethylene, ethane, 1-butene, and benzene, across 250−350 °C. Ethylene remains the dominant product but decreases from about 41.95% at 250 °C to 30.14% at 350 °C, while heavier products increase significantly, with 1-butene rising to nearly 8.37% and benzene reaching 2.17% at intermediate temperatures. The model shows that the ethylene production rate can be maximized at around 270 °C. The models developed in this work can be utilized for process optimization, reactor design and scale-up of microwave-assisted plastic conversion technologies, and economic analysis.

Damahe, Harish [West Virginia Univ., Morgantown, W↗
DOE OSTI2021

Measurements of Intermediate Species in Fuel-Rich Oxidation of Ethylene, Toluene, and n -Decane

Polycyclic aromatic hydrocarbons (PAHs) are important precursors to the formation of soot and are also pollutant emissions from combustion devices, including internal combustion engines. To understand the formation of PAHs, the low-molecular-weight hydrocarbon chemistry leading to their formation needs to be understood. Toward this goal, fuel-rich oxidation of three different hydrocarbons, i.e., ethylene, toluene, and n-decane, has been investigated in an atmospheric pressure flow reactor at varying temperatures (1000–1350 K), equivalence ratios (φ = 3.0–12.0), and residence times of 0.25–1.5 s. The major C 1 to C 7 intermediates, such as methane, acetylene, ethylene, allene, propyne, propylene, vinylacetylene, 1,3-butadiene, cyclopentadiene, benzene, and toluene, were quantified using a gas chromatograph equipped with a flame ionization detector. The experimental tendency of intermediate species formation, e.g., the dependence of temperature, equivalence ratio, and residence time, was similar in ethylene oxidation and n-decane oxidation. The concentrations of intermediate species up to C 4 were higher in ethylene/n-decane oxidation than toluene oxidation, while a nearly equal or larger amount of C5–C7 species was produced in toluene oxidation. The experimental data were compared with modeling results using a detailed chemical kinetic mechanism. The calculated data using the kinetic model were in agreement with the experimental results. Additionally, a comprehensive kinetic analysis on the reaction pathways of each species was conducted to assess the differences in the oxidation chemistry with the change in the structure of hydrocarbons. In particular, in-depth analysis of benzene formation was performed, elucidating that two benzene formation pathways were important in ethylene and n-decane oxidation: (1) H elimination of the 2,4-cyclohexadienyl radical produced from the isomerization of the 2,4-cyclopentadienylmethyl radical, and (2) dehydrogenation of cyclohexadiene produced from reactions of vinyl radical with 1,3-butadiene. In toluene oxidation, it was found that benzene was primarily produced from toluene through the replacement of the methyl group with hydrogen.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Simulating Catalysis with Realistic Pellet Geometries Using Mesoflow: A Case Study of Catalytic Propane Dehydrogenation

We present a case study of catalytic propane dehydrogenation with our open-source multiphysics solver, Mesoflow. The solver was developed to simulate reactive flow coupled to heterogeneous catalytic reactions and deactivation in the context of complex, mesoscale geometry. The method leverages cartesian block-structured adaptive mesh refinement to capture realistic catalyst microstructural features acquired directly from X-ray computed tomography data. A kinetic model for propane dehydrogenation and catalyst deactivation was developed based on temporal analysis of products (TAP) reactor experiments. The TAP reactor experiments allow for precise characterization of intrinsic kinetic reaction steps which are implemented into Mesoflow simulations to model the spatial and temporal evolution of reactants, products, and catalyst active sites. The short-term and long-term deactivation behavior is studied by using XCT data collected from fresh and aged catalyst pellets, which exhibit different microstructural features. This study employs time-splitting algorithms to connect disparate reaction and flow timescales, enabling the simulations to achieve realistic deactivation timescales on the order of minutes while the flow time-scales for small particles (100 microns) are several milliseconds. We also introduce a flexible automated python script that writes the necessary files to construct a Mesoflow simulation from user-created chemical mechanisms. We will also introduce a few new features that are added to Mesoflow such as higher order schemes, implicit chemistry integrators and the ability to run on AMD and NVIDIA graphics-processing-units.

AMReX↗
DOE OSTI2025

Rate expressions and kinetic parameters for metal ferrites in relation to applications of fossil fuel conversion to hydrogen: Part 1 of 2

Here, the goal of the present work was to provide the necessary reaction emulation information to enable detailed process simulation of a chemical looping H 2 production system from fossil fuels using CaFe 2 O 4 . This specifically pertained to the necessary kinetic data, reaction model development, and model rate parameters required for reaction emulation in both reducing and oxidizing environments. A logical methodology was defined, which included discretization of the reaction network, establishing a core model for reaction emulation that could be adapted based on the system phenomena, and development of a rate parameter regression tool designed around the core model. An extensive array of data sets was acquired by which parametric regressions were performed. The work presented and tabulated a comprehensive set of rate parameters for the reduction and oxidation reactions of CaFe 2 O 4 and descendent phases of Ca 2 Fe 2 O 5 , FeO, Fe 3 O 4 , Fe, and CaO to emulate reaction behavior in a looping-based process environment. This included direct reduction using CH 4 , H 2 , and CO, and direct oxidation reactions with steam, CO 2 and O 2 . Dynamic equilibrium was quantified for reactions that could utilize H 2 O and CO 2 as soft oxidants to re-saturate lattice oxygen in the depleted structure/phases. The kinetics associated with the oxidative mechanisms with the soft oxidants were quantified and compared to those of the reducing counterparts. The analysis provided critical insight to emulate reactions for a process that seeks to use natural gas (NG) or other fossil fuels as a direct reductant for the end goal of H 2 production.

calcium ferrite oxygen carriers↗
DOE OSTI2026

Kinetic and modeling studies of the mechanism of the dehydrogenation of Mg(BH 4 ) 2 to Mg(B 3 H 8 ) 2

Since its discovery over 15 years ago, the reversible dehydrogenation of Mg(BH 4 ) 2 to Mg(B 3 H 8 ) 2 has remained one of the more intriguing hydrogen-cycling systems. While the mechanism of this reaction has been the subject of a good deal of speculation and computational studies, prior to this work it had not been probed through kinetic studies. Previous reports of the dehydrogenation of Mg(BH 4 ) 2 to Mg(B 3 H 8 ) 2 have not included kinetic studies. The present studies have shown that the dehydrogenation of Mg(BH 4 ) 2 to Mg(B 3 H 8 ) 2 is suppressed by hydrogen pressure indicating that the rate-limiting step in this process involves hydrogen elimination. Computational modeling of kinetic data obtained from monitoring the hydrogen elimination from Mg(BH 4 ) 2 to Mg(B 3 H 8 ) 2 under static vacuum over a range of temperatures supports that the dehydrogenation occurs through a reversible three-step process in which the elimination of hydrogen from the [B 3 H 10 ] − intermediate is rate limiting. A mechanism involving the low energy transfer of neighboring BH3 groups is proposed to account for the formation of [B 3 H 8 ] − at relatively low temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Selectivity of enzymes involved in the formation of opposite enantiomeric series of p -menthane monoterpenoids in peppermint and Japanese catnip

Peppermint (Mentha x piperita L.) and Japanese catnip (Schizonepeta tenuifolia (Benth.) Briq.) accumulate p-menthane monoterpenoids with identical functionalization patterns but opposite stereochemistry. In the present study, we investigate the enantioselectivity of multiple enzymes involved in monoterpenoid biosynthesis in these species. Based on kinetic assays, mint limonene synthase, limonene 3-hydroxylase, isopiperitenol dehydrogenase, isopiperitenone reductase, and menthone reductase exhibited significant enantioselectivity toward intermediates of the pathway that proceeds through (-)-4S-limonene. Limonene synthase, isopiperitenol dehydrogenase and isopiperitenone reductase of Japanese catnip preferred intermediates of the pathway that involves (+)-4R-limonene, whereas limonene 3-hydroxylase was not enantioselective, and the activities of pulegone reductase and menthone reductase were too low to acquire meaningful kinetic data. Molecular modeling studies with docked ligands generally supported the experimental data obtained with peppermint enzymes, indicating that the preferred enantiomer was aligned well with the requisite cofactor and amino acid residues implicated in catalysis. A striking example for enantioselectivity was peppermint (-)-menthone reductase, which binds (-)-menthone with exquisite affinity but was predicted to bind (+)-menthone in a non-productive orientation that positions its carbonyl functional group at considerable distance to the NADPH cofactor. Here, the work presented here lays the groundwork for structure-function studies aimed at unraveling how enantioselectivity evolved in closely related species of the Lamiaceae and beyond.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A broad-host-range event detector: expanding and quantifying performance between Escherichia coli and Pseudomonas species

Modern microbial biodesign relies on the principle that well-characterized genetic parts can be reused and reconfigured for different functions. However, this paradigm has only been successful in a limited set of hosts, mostly comprised from common lab strains of Escherichia coli . It is clear that new applications such as chemical sensing and event logging in complex environments will benefit from new host chassis. This study quantitatively compared how the same chemical event logger performed across four strains and three different microbial species. An integrase-based sensor and memory device was operated by two representative soil Pseudomonads— Pseudomonas fluorescens SBW25 and Pseudomonas putida DSM 291. Quantitative comparisons were made between these two non-traditional hosts and two benchmark E. coli chassis including the probiotic Nissle 1917 and common cloning strain DH5α. The performance of sensor and memory components changed according to each host, such that a clear chassis effect was observed and quantified. These results were obtained via fluorescence from reporter proteins that were transcriptionally fused to the integrase and downstream recombinant region and via data-driven kinetic models. The Pseudomonads proved to be acceptable chassis for the operation of this event logger, which outperformed the common E. coli DH5α in many ways. This study advances an emerging frontier in synthetic biology that aims to build broad-host-range devices and understand the context by which different species can execute programmable genetic operations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Resolving domain positions of cellobiose dehydrogenase by small angle X–ray scattering

The interdomain electron transfer (IET) between the catalytic flavodehydrogenase domain and the electron-transferring cytochrome domain of cellobiose dehydrogenase (CDH) plays an essential role in biocatalysis, biosensors and biofuel cells, as well as in its natural function as an auxiliary enzyme of lytic polysaccharide monooxygenase. We investigated the mobility of the cytochrome and dehydrogenase domains of CDH, which is hypothesised to limit IET in solution by small angle X-ray scattering (SAXS). CDH from Myriococcum thermophilum (syn. Crassicarpon hotsonii, syn. Thermothelomyces myriococcoides) was probed by SAXS to study the CDH mobility at different pH and in the presence of divalent cations. By comparison of the experimental SAXS data, using pair-distance distribution functions and Kratky plots, we show an increase in CDH mobility at higher pH, indicating alterations of domain mobility. To further visualise CDH movement in solution, we performed SAXS-based multistate modelling. Glycan structures present on CDH partially masked the resulting SAXS shapes, we diminished these effects by deglycosylation and studied the effect of glycoforms by modelling. The modelling shows that with increasing pH, the cytochrome domain adopts a more flexible state with significant separation from the dehydrogenase domain. On the contrary, the presence of calcium ions decreases the mobility of the cytochrome domain. Experimental SAXS data, multistate modelling and previously reported kinetic data show how pH and divalent ions impact the closed state necessary for the IET governed by the movement of the CDH cytochrome domain.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A purely kinetic description of the evaporation of water droplets

The process of water evaporation, although deeply studied, does not enjoy a kinetic description that captures known physics and can be integrated with other detailed processes such as drying of catalytic membranes embedded in vapor-fed devices and chemical reactions in aerosol whose volumes are changing dynamically. In this work, we present a simple, three-step kinetic model for water evaporation that is based on theory and validated by using well-established thermodynamic models of droplet size as a function of time, temperature, and relative humidity as well as data from time-resolved measurements of evaporating droplet size. Additionally, the kinetic mechanism for evaporation is a combination of two limiting processes occurring in the highly dynamic liquid-vapor interfacial region: direct first order desorption of a single water molecule and desorption resulting from a local fluctuation, described using third order kinetics. The model reproduces data over a range of relative humidities and temperatures only if the interface that separates bulk water from gas phase water has a finite width, consistent with previous experimental and theoretical studies. The influence of droplet cooling during rapid evaporation on the kinetics is discussed; discrepancies between the various models point to the need for additional experimental data to identify their origin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗