Results for “parameter reduction” — Page 58

DOE OSTI2020

Performance assessment of long-legged tightly-baffled divertor geometries in the ARC reactor concept

Extremely intense power exhaust channels are projected for tokamak-based fusion power reactors; a means to handle them remains to be demonstrated. Advanced divertor configurations have been proposed as potential solutions. Recent modelling of tightly baffled, long-legged divertor geometries for the divertor test tokamak concept, ADX, has shown that these concepts may access passively stable, fully detached regimes over a broad range of parameters. The question remains as to how such divertors may perform in a reactor setting. To explore this, numerical simulations are performed with UEDGE for the long-legged divertor geometry proposed for the ARC pilot plant conceptual design - a device with projected heat flux power width (λq||) of 0.4 mm and power exhaust of 93 MW - first for a simplified Super-X divertor configuration (SXD) and then for the actual X-point target divertor (XPTD) being proposed. It is found that the SXD, combined with 0.5% fixed-fraction neon impurity concentration, can produce passively stable, detached divertor regimes for power exhausts in the range of 80-108 MW - fully accommodating ARC's power exhaust. The XPTD configuration is found to reduce the strike-point temperature by a factor of ~10 compared to the SXD for small separations (~1.4λq||) between main and divertor X-point magnetic flux surfaces. Even greater potential reductions are identified for reducing separations to ~1λq|| or less. The power handling response is found to be insensitive to the level of cross-field convective or diffusive transport assumed in the divertor leg. By raising the separatrix density by a factor of 1.5, stable fully detached divertor solutions are obtained that fully accommodate the ARC exhaust power without impurity seeding. To our knowledge, this is the first time an impurity-free divertor power handling scenario has been obtained in edge modelling for a tokamak fusion power reactor with λq|| of 0.4 mm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Bridging interfacial properties and cell performance: A multiscale model for proton-exchange-membrane fuel cells

Here, to elucidate the impact of local interfaces on mass-transport resistance and overall cell performance of low-loaded proton-exchange-membrane fuel cells (PEMFCs), we present a multiscale modeling framework incorporating a novel modified agglomerate model. The model considers three distinct Pt-electrolyte interfaces: Pt on the carbon surface covered by either ionomer or water film and Pt inside carbon nanopores. Detailed mass-transport voltage-loss breakdowns reveal that coupled agglomerate-interface-scale mass transport dominates the mass-transport loss. The ionomer poisons the exterior-Pt surface through suppressing O 2 adsorption and intrinsic ORR activity, leading to low current-density performance. Conversely, interior-Pt interface enhances the kinetic performance but limits high current-density performance due to its low interfacial permeability. The exterior-Pt/water interface demonstrates superior kinetic performance and mass transport, though its practical implementation requires ensuring proton transport. By coupling the multiscale CL properties with ink parameters, the model identifies an optimal I to C ratio of approximately 0.5, a moderate value where the ionomer content is sufficient to guarantee proton transport without fully covering the Pt surface and forming large agglomeration, thus allowing the utilization of the Pt-water interface and avoiding high mass-transport loss. Overall, the model helps unravel limiting phenomena across different operating regimes and provides routes for optimizing performance.

Cell diagnostic↗

DOE OSTI2025

Constraining cosmological parameters using the pairwise kinematic Sunyaev-Zel’dovich effect with CMB-S4 and future galaxy cluster surveys

We present a forecast of the pairwise kinematic Sunyaev-Zel’dovich (kSZ) measurement that will be achievable with the future CMB-S4 experiment. CMB-S4 is the next stage for ground-based cosmic microwave background experiments, with a planned wide-area survey that will observe approximately 50% of the sky. We construct a simulated sample of galaxy clusters that have been optically selected in a Legacy Survey of Space and Time–like survey and have spectroscopic redshifts. For this cluster sample, assuming the likelihood is Gaussian, we predict that CMB-S4 will reject the null hypothesis of zero pairwise kSZ signal at 36⁢𝜎. We estimate the effects of systematic uncertainties such as scatter in the mass-richness scaling relation and cluster miscentering. We find that these effects can reduce the signal-to-noise ratio of the CMB-S4 pairwise kSZ measurement by 20%. We explore the constraining power of the measured kSZ signal in combination with measurements of the galaxy clusters’ thermal SZ emission on two extensions to the standard cosmological model. The first extension allows the dark energy equation of state 𝑤 to vary. We find the CMB-S4 pairwise kSZ measurement yields a modest reduction in the uncertainty on 𝑤 by a factor of 1.36 over the Planck’s 2018 uncertainty. The second extension tests general relativity by varying the growth index 𝛾. In conclusion, we find that CMB-S4’s pairwise kSZ measurement will yield a 28⁢𝜎 constraint on 𝛾 and strongly constrain alternative theories of gravity.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Recreating Fuel Cell Catalyst Degradation in Aqueous Environments for Identical-Location Scanning Transmission Electron Microscopy Studies

The recent surge in interest of proton exchange membrane fuel cells (PEMFCs) for heavy-duty vehicles increases the demand on the durability of oxygen reduction reaction electrocatalysts used in the fuel cell cathode. This prioritizes efforts aimed at understanding and subsequently controlling catalyst degradation. Identical-location scanning transmission electron microscopy (IL-STEM) is a powerful method that enables precise characterization of degradation processes in individual catalyst nanoparticles across various stages of cycling. Recreating the degradation processes that occur in PEMFC membrane electrode assemblies (MEAs) within the aqueous cell used for IL-STEM experiments is vital for generating an accurate understanding of these processes. In this work, we investigate the type and degree of catalyst degradation achieved by cycling in an aqueous cell compared to a PEMFC MEA. While significant degradation is observed in IL-STEM experiments performed on a traditional Pt catalyst using the standard accelerated stress test potential window (0.6-0.95 VRHE), degradation of a PtCo catalyst designed for heavy-duty vehicle use is very limited compared to that observed in MEAs. We therefore explore various experimental parameters such as temperature, acid type, acid concentration, ionomer content, and potential window to identify conditions that reproduce the degradation observed in MEAs. We find that by extending the cycling potential window to 0.4-1.0 V RHE in an electrolyte containing Pt ions, the degraded particle size distribution and alloy composition better match that observed in MEAs. In particular, these conditions increase the relative contribution of Ostwald ripening, which appears to play a more significant role in the degradation of larger alloy particles supported on high-surface-area carbons than coalescence. Results from this work highlight the potential for discrepancies between ex situ aqueous experiments and MEA tests. While different catalysts may require a unique modification to the AST protocol, strategies provided in this work enable future in situ and identical-location experiments that will play an important role in the development of robust catalysts for heavy-duty vehicle applications.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2023

Rapid CO2 changes cause oscillations in photosynthesis that implicate PSI acceptor-side limitations

Abstract Oscillations in CO2 assimilation rate and associated fluorescence parameters have been observed alongside the triose phosphate utilization (TPU) limitation of photosynthesis for nearly 50 years. However, the mechanics of these oscillations are poorly understood. Here we utilize the recently developed dynamic assimilation techniques (DATs) for measuring the rate of CO2 assimilation to increase our understanding of what physiological condition is required to cause oscillations. We found that TPU-limiting conditions alone were insufficient, and that plants must enter TPU limitation quickly to cause oscillations. We found that ramps of CO2 caused oscillations proportional in strength to the speed of the ramp, and that ramps induce oscillations with worse outcomes than oscillations induced by step change of CO2 concentration. An initial overshoot is caused by a temporary excess of available phosphate. During the overshoot, the plant outperforms steady-state TPU and ribulose 1,5-bisphosphate regeneration limitations of photosynthesis, but cannot exceed the rubisco limitation. We performed additional optical measurements which support the role of PSI reduction and oscillations in availability of NADP+ and ATP in supporting oscillations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Improved semiclassical model for real-time evaporation of matrix black holes

In this paper, we study real-time classical matrix mechanics of a simplified 2 × 2 matrix model inspired by the black hole evaporation problem. This is a step towards making a quantitative model of real-time evaporation of a black hole, which is realized as a bound state of D0-branes in string theory. The model we study is the reduction of Yang–Mills in 2 + 1 dimension to 0 + 1 dimensions, which has been corrected with an additional potential that can be interpreted as a zero-point energy for fermions. Our goal is to understand the lifetime of such a classical bound state object in the classical regime. To do so, we pay particular attention to when D-particles separate to check that the “off-diagonal modes” of the matrices become adiabatic and use that information to improve on existing models of evaporation. It turns out that the naive expectation value of the lifetime with the fermionic correction is infinite. This is a logarithmic divergence that arises from very large excursions in the separation between the branes near the threshold for classical evaporation. The adiabatic behavior lets us get some analytic control of the dynamics in this regime to get this estimate. This divergence is cutoff in the quantum theory due to quantization of the adiabatic parameter, resulting in a long lifetime of the bound state, with a parametric dependence of order log(1/ℏ).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Cost function for low-dimensional manifold topology assessment

Abstract In reduced-order modeling, complex systems that exhibit high state-space dimensionality are described and evolved using a small number of parameters. These parameters can be obtained in a data-driven way, where a high-dimensional dataset is projected onto a lower-dimensional basis. A complex system is then restricted to states on a low-dimensional manifold where it can be efficiently modeled. While this approach brings computational benefits, obtaining a good quality of the manifold topology becomes a crucial aspect when models, such as nonlinear regression, are built on top of the manifold. Here, we present a quantitative metric for characterizing manifold topologies. Our metric pays attention to non-uniqueness and spatial gradients in physical quantities of interest, and can be applied to manifolds of arbitrary dimensionality. Using the metric as a cost function in optimization algorithms, we show that optimized low-dimensional projections can be found. We delineate a few applications of the cost function to datasets representing argon plasma, reacting flows and atmospheric pollutant dispersion. We demonstrate how the cost function can assess various dimensionality reduction and manifold learning techniques as well as data preprocessing strategies in their capacity to yield quality low-dimensional projections. We show that improved manifold topologies can facilitate building nonlinear regression models.

42 ENGINEERING↗

DOE OSTI2021

Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery

The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model on electrostatic interaction, the nonpolar interaction contribution of solvent and the conformational entropy of solute in solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of conformational entropy of solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal/mol for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined.

25 ENERGY STORAGE↗

DOE OSTI2020

Mirostructure Characterization of Friction Consolidated Copper-Nickel using a Machine Learning Approach: Developing Process to Microstructure Associations

Friction consolidation (FC) is a solid phase processing approach where discrete material forms such as powders, chips, nuggets, etc. are densified via shear deformation. The precursors are placed in a billet container and brought in contact with a rotating tool that applying the desirable amount of normal force. Under the combined action of the rotation and normal pressure, the discrete precursor is consolidated through porosity reduction and shear deformation. FC is increasingly being studied as an attractive approach to manufacturing fully dense parts from powder forms owing to its ability to mix, alloy and consolidate difficult-to-process precursors in minimal number of process steps. Material consolidation and deformation in shear consolidation processes have been studied extensively previously for different material combinations previously. However, despite the extensive research in this area, understanding of the mechanistic processes in pore consolidation, deformation-induced mixing and material solubility during FC is still evolving. Material development using solid phase processing approaches such as FC is often performed based on research experience/education, which can be biased. Conventional analysis and simulation tools in this area tend to be successful only when material thermodynamic pathways and microstructural evolution sequences resulting from processing are clearly defined or known. They are not as effective for emerging advanced manufacturing technologies where material evolution pathways are not well established. The ability to predict optimal process parameters based on material chemistry and bulk properties is essential to accelerate materials design and processing, as are an understanding of the relevant structure-processing-property relationships. These structure-processing-property-performance relationships are at the core of materials science research. Microstructure characterization provides the link to these four core areas, often through visualizing material microstructure using imaging techniques. However, linking microstructure image data (i.e., micrographs) to variables of interest (e.g., processing parameters, material chemistry) in a reproducible, generalizable, and quantitative manner is a significant challenge. Typically, quantitatively linking image data to processing history relies on significant domain knowledge and manual or subject matter expert (SME)-heuristic based image analysis. Such an approach to image analysis has the potential to be biased, inefficient, and difficult to replicate.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Machine learning-guided design of direct methanol fuel cells with a platinum group metal-free cathode

Direct methanol fuel cells (DMFCs) offer a promising solution for clean electricity generation, particularly in small electronics and remote auxiliary power units. However, optimizing their efficiency and performance is challenging due to the complex interactions between various factors. Here, we present a novel approach that integrates experiments with machine learning to model and predict the performance of these fuel cells using atomically dispersed platinum group metal (PGM)-free catalysts at the cathode. Further, our machine learning models, trained on diverse input parameters, allow for the comprehensive optimization of DMFC performance prior to fabrication and testing. Through extensive experimental validation, we demonstrate that this data-driven approach accurately predicts key performance metrics, such as maximum power output and polarization curves. By combining our models with interpretable game-theory methods, we provide deep insights into the factors governing fuel cell performance, ultimately paving the way for the design of scalable and efficient DMFC technologies.

25 ENERGY STORAGE↗

DOE OSTI2021

Statistical Multiobjective Optimization of Thiospinel CoNi 2 S 4 Nanocrystal Synthesis via Design of Experiments

Thiospinels, such as CoNi 2 S 4 , are showing promise for numerous applications, including as catalysts for the hydrogen evolution reaction, hydrodesulfurization, and oxygen evolution and reduction reactions; however, CoNi 2 S 4 has not been synthesized as small, colloidal nanocrystals with high surface-area-to-volume ratios. Traditional optimization methods to control nanocrystal attributes such as size typically rely upon one variable at a time (OVAT) methods that are not only time and labor intensive but also lack the ability to identify higher-order interactions between experimental variables that affect target outcomes. Herein, we demonstrate that a statistical design of experiments (DoE) approach can optimize the synthesis of CoNi 2 S 4 nanocrystals, allowing for control over the responses of nanocrystal size, size distribution, and isolated yield. After implementing a 2 5–2 fractional factorial design, the statistical screening of five different experimental variables identified temperature, Co:Ni precursor ratio, Co:thiol ratio, and their higher-order interactions as the most critical factors in influencing the aforementioned responses. Second-order design with a Doehlert matrix yielded polynomial functions used to predict the reaction parameters needed to individually optimize all three responses. A multiobjective optimization, allowing for the simultaneous optimization of size, size distribution, and isolated yield, predicted the synthetic conditions needed to achieve a minimum nanocrystal size of 6.1 nm, a minimum polydispersity (σ/$\bar{d}$) of 10%, and a maximum isolated yield of 99%, with a desirability of 96%. The resulting model was experimentally verified by performing reactions under the specified conditions. Furthermore, our work illustrates the advantage of multivariate experimental design as a powerful tool for accelerating control and optimization in nanocrystal syntheses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Changing temperature profiles and the risk of dengue outbreaks

As temperatures change worldwide, the pattern and competency of disease vectors will change, altering the global distribution of both the burden of infectious disease and the risk of the emergence of those diseases into new regions. To evaluate the risk of potential summer dengue outbreaks triggered by infected travelers under various climate scenarios, we develop an SEIR-type model, run numerical simulations, and conduct sensitivity analyses under a range of temperature profiles. Our model extends existing theoretical frameworks for studying dengue dynamics by introducing temperature dependence of two key parameters: the mosquito extrinsic incubation period and the lifespan of mosquitoes, which empirical data suggests are both highly temperature dependent. We find that changing temperature significantly alters dengue risk in an inverted U-shape, with temperatures in the range 27-31°C producing the highest risk. As temperatures increase beyond 31°C, the determinants of dengue risk begin to shift from mosquito biting rate and carrying capacity to the duration of the human infectious period, suggesting that changing temperatures not only alter dengue risk but also the potential efficacy of control measures. To illustrate the role of spatial and temporal temperature heterogeneity, we select five US cities where the primary dengue vector, the mosquito Aedes aegypti , has been observed, and which have had dengue cases in the past: Los Angeles, Houston, Miami, Brownsville, and Phoenix. Our analysis suggests that an increase of 3°C leads to an approximate doubling of the risk of dengue in Los Angeles and Houston, but a reduction of risk in Miami, Brownsville, and Phoenix due to extreme heat.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Feedstock Carbon Intensity Calculator (FD-CIC): Users’ Manual and Technical Documentation

The carbon intensities (CIs) of biofuels are determined with the life cycle analysis (LCA) technique, which accounts for the energy/material uses and emissions during the complete supply chain of biofuel including feedstock production and fuel conversion stages. Regulatory agencies such as California Air Resources Board (CARB) adopts LCA to calculate biofuel CIs. The Low Carbon Fuel Standard (LCFS) program developed by CARB allows individual biofuel conversion facilities to submit their own biofuel CIs with their facility input data and incentivizes the reduction in the CI specific to that particular facility compared to a reference fuel’s CI (Liu et al 2020). Such an incentive program has driven innovations in biorefineries to reduce their greenhouse gas (GHG) emissions by linking their revenue directly to its CI score through LCFS credit trading. Besides the biofuel conversion stage, different farming practices for feedstock growth can result in significant CI variations for feedstocks, thus for biofuels. To provide evidence-based research findings, the U.S. Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E) has supported the Systems Assessment Center of the Energy Systems and Infrastructure Analysis Division at Argonne National Laboratory to examine CI variations of different farming practices to grow agricultural crops for biofuel production. Meanwhile, the ARPA-E has launched the Systems for Monitoring and Analytics for Renewable Transportation Fuels from Agricultural Resources and Management (SMARTFARM) program to develop technologies and data platforms that enable an accurate measurement of key farming parameters that can help robust accounting of the GHG benefits of sustainable, low-carbon agronomic practices at farm level.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Nonlocal, diamagnetic electromagnetic effects in magnetically insulated transmission lines

We identify the time-dependent physics responsible for the critical reduction of current losses in magnetically insulated transmission lines (MITLs) due to uninsulated space charge-limited currents of electrons emitted by field stress. A drive current of sufficiently short pulse length introduces a strong enough time dependence that steady-state results alone become inadequate for the complete understanding of current losses. The time-dependent physics can be described as a nonlocal, diamagnetic electromagnetic response of space charge limited currents. As the pulse length is increased or equivalently, the MITL length reduced, these time-dependent effects diminish and current losses converge to those predicted by the well-known Child–Langmuir law in the external (vacuum) fields. We present a simple one-dimensional (1D) model that encapsulates the essence of this physics. We find excellent agreement with 2D particle-in-cell simulations for two MITL geometries, Cartesian parallel plate and azimuthally symmetric straight coaxial. Based on the 1D model, we explore various scaling dependencies of MITL losses with relevant parameters, e.g., peak current, pulse length, geometrical dimensions, etc. We propose an improved physics model of magnetic insulation in the form of a Hull curve, which could also help improve predictions of current losses by common circuit element codes, such as BERTHA. Finally, we describe how to calculate the temperature rise due to electron impact within the 1D model.

Computer simulation↗

DOE OSTI2025

Probabilistic flux limiters

The stable numerical integration of shocks in compressible flow simulations relies on the reduction or elimination of Gibbs phenomena (unstable, spurious oscillations). A popular method to virtually eliminate Gibbs oscillations caused by numerical discretization in under-resolved simulations is to use a flux limiter. A wide range of flux limiters have been studied in the literature, with recent interest in their optimization via machine learning methods trained on high-resolution datasets. The common use of flux limiters in numerical codes as plug-and-play blackbox components makes them key targets for design improvement. Even for deterministic dynamical models, numerical uncertainty is introduced via coarse-graining required by insufficient computational power to solve all scales of motion. Conventional flux limiters are deterministic and lack the capacity to address uncertainties, both aleatoric (inherent randomness) and epistemic (modeling uncertainty due to limited knowledge), which arise in coarse-grained numerical simulations. Here, we introduce a conceptually distinct type of flux limiter that is designed to handle the effects of randomness in the model and uncertainty in model parameters. Unlike traditional single-function flux limiters, these new probabilistic flux limiters incorporate multiple flux limiting functions, each applied with a learned probability drawn from high-resolution data to mitigate the effects of uncertainty in numerical simulations. This approach departs from traditional single-function limiters by explicitly modeling and incorporating uncertainty into the shock capturing process. Using the example of Burgers' equation as a testbed, we show that a machine learned, probabilistic flux limiter may be used in a shock capturing code to more accurately capture shock profiles. In particular, we show that our probabilistic flux limiter outperforms standard limiters and can be successively improved upon (up to a point) by expanding the set of probabilistically chosen flux limiting functions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Towards a Circular Economy for PET Bottles in the US

The United States generates the most plastic waste of any country. Along with that GHG emissions from the global plastic economy are expected to increase to 15% of the global carbon budget by 2050. It is imperative that plastic recycling is made a reality to reduce both plastic pollution in the environment and GHG emissions. A portfolio of end-of-life strategies must be implemented to minimize environmental impacts and retain valuable plastic material, but it is challenging to compare options that generate products with different utility and lifetime. Plastic use reduction, reuse and recycling are thus increasingly important, but making informed policy and research decisions within this space can be challenging given the diverse range of available solutions. The novel analysis framework, Plastic Parallel Pathways Platform (4P) has been equipped with consequential life cycle assessment, techno-economic analysis, and a plastic circularity indicator to estimate the greenhouse gas (GHG) emissions, circularity, and cost of polyethylene terephthalate (PET) down-cycling to lower-quality resin, closed-loop recycling to food-grade PET bottles, up-cycling to fiber-reinforced plastic (FRP), and conversion to non-plastic products (electricity, oil) on a United States economy-wide basis. Integrating system dynamics into this robust plastics model that already incorporates techno-economics, circularity, and environmental impacts will enable identification of key bottlenecks between manufacturers, waste sorters, and reclaimers that currently prevent rapid decarbonization of the plastics economy. System dynamics (SD) explore the evolution of activities and technologies based on changed macro parameters such as plastic demand and supply, market shifts, and cross-sectoral interactions. This project particularly aims to explore the interplay between, waste collection, plastic waste sorting, recycling, and manufacturing, as well as the effect of plastic bale quality and plastic reuse initiatives on the surrounding process stages. This functionality will facilitate combinatory analysis in which a portfolio of end-of-life pathways are assessed simultaneously, with the exact makeup of that portfolio affected by parameters such as technology scales, resource constraints, and waste mitigation efforts. Integrating SD with the 4P framework enables analyzing the effect of increased revenue and reinvestment into improving process efficiencies, sorting and collection quantities. Through that, market effects of increased recycled resin availability can be studied for the plastics systems model for the US. The results will help identify technical or economic bottlenecks that currently limit efforts to decarbonize the U.S. plastics economy.

circular economy↗

DOE OSTI2024

Towards a Circular Economy for PET Bottles in the U.S. - 4P Model

The United States generates the most plastic waste of any country. Along with that GHG emissions from the global plastic economy are expected to increase to 15% of the global carbon budget by 2050. It is imperative that plastic recycling is made a reality to reduce both plastic pollution in the environment and GHG emissions. A portfolio of end-of-life strategies must be implemented to minimize environmental impacts and retain valuable plastic material, but it is challenging to compare options that generate products with different utility and lifetime. Plastic use reduction, reuse and recycling are thus increasingly important, but making informed policy and research decisions within this space can be challenging given the diverse range of available solutions. The novel analysis framework, Plastic Parallel Pathways Platform (4P) has been equipped with consequential life cycle assessment, techno-economic analysis, and a plastic circularity indicator to estimate the greenhouse gas (GHG) emissions, circularity, and cost of polyethylene terephthalate (PET) down-cycling to lower-quality resin, closed-loop recycling to food-grade PET bottles, up-cycling to fiber-reinforced plastic (FRP), and conversion to non-plastic products (electricity, oil) on a United States economy-wide basis. Integrating system dynamics into this robust plastics model that already incorporates techno-economics, circularity, and environmental impacts will enable identification of key bottlenecks between manufacturers, waste sorters, and reclaimers that currently prevent rapid decarbonization of the plastics economy. System dynamics (SD) explore the evolution of activities and technologies based on changed macro parameters such as plastic demand and supply, market shifts, and cross-sectoral interactions. This project particularly aims to explore the interplay between, waste collection, plastic waste sorting, recycling, and manufacturing, as well as the effect of plastic bale quality and plastic reuse initiatives on the surrounding process stages. This functionality will facilitate combinatory analysis in which a portfolio of end-of-life pathways are assessed simultaneously, with the exact makeup of that portfolio affected by parameters such as technology scales, resource constraints, and waste mitigation efforts. Integrating SD with the 4P framework enables analyzing the effect of increased revenue and reinvestment into improving process efficiencies, sorting and collection quantities. Through that, market effects of increased recycled resin availability can be studied for the plastics systems model for the US. The results will help identify technical or economic bottlenecks that currently limit efforts to decarbonize the U.S. plastics economy.

carbon↗

DOE OSTI2024

Towards a Circular Economy for PET Bottles in the U.S. - 4P Framework

The United States generates the most plastic waste of any country. Along with that GHG emissions from the global plastic economy are expected to increase to 15% of the global carbon budget by 2050. It is imperative that plastic recycling is made a reality to reduce both plastic pollution in the environment and GHG emissions. A portfolio of end-of-life strategies must be implemented to minimize environmental impacts and retain valuable plastic material, but it is challenging to compare options that generate products with different utility and lifetime. Plastic use reduction, reuse and recycling are thus increasingly important, but making informed policy and research decisions within this space can be challenging given the diverse range of available solutions. The novel analysis framework, Plastic Parallel Pathways Platform (4P) has been equipped with consequential life cycle assessment, techno-economic analysis, and a plastic circularity indicator to estimate the greenhouse gas (GHG) emissions, circularity, and cost of polyethylene terephthalate (PET) down-cycling to lower-quality resin, closed-loop recycling to food-grade PET bottles, up-cycling to fiber-reinforced plastic (FRP), and conversion to non-plastic products (electricity, oil) on a United States economy-wide basis. Integrating system dynamics into this robust plastics model that already incorporates techno-economics, circularity, and environmental impacts will enable identification of key bottlenecks between manufacturers, waste sorters, and reclaimers that currently prevent rapid decarbonization of the plastics economy. System dynamics (SD) explore the evolution of activities and technologies based on changed macro parameters such as plastic demand and supply, market shifts, and cross-sectoral interactions. This project particularly aims to explore the interplay between, waste collection, plastic waste sorting, recycling, and manufacturing, as well as the effect of plastic bale quality and plastic reuse initiatives on the surrounding process stages. This functionality will facilitate combinatory analysis in which a portfolio of end-of-life pathways are assessed simultaneously, with the exact makeup of that portfolio affected by parameters such as technology scales, resource constraints, and waste mitigation efforts. Integrating SD with the 4P framework enables analyzing the effect of increased revenue and reinvestment into improving process efficiencies, sorting and collection quantities. Through that, market effects of increased recycled resin availability can be studied for the plastics systems model for the US. The results will help identify technical or economic bottlenecks that currently limit efforts to decarbonize the U.S. plastics economy.

circular economy↗