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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Impact of grassland fire dynamics on particulate emission factors

Understanding particulate emission factors in grassland fires is critical for improving air quality assessments and refining emissions inventories. This study explores the dynamic and heterogeneous nature of fire processes and their influence on particulate emissions. Traditional emission factor inventories often rely on static values, overlooking the spatially and temporally variable dynamics of fire behavior. Through field experiments conducted at Konza Prairie Biological Station measuring emissions with uncrewed aerial vehicles, and computational modeling using HIGRAD/FIRETEC, this research highlights the dependency of emission production on localized fire dynamics, including ignition patterns, fuel properties, and atmospheric conditions. The results demonstrate a need for fire dynamic considerations to accurately capture emissions variability.

09 BIOMASS FUELS↗
DOE OSTI2020

Integration of Large-Scale Renewable Energy in the Bulk Power System: Lessons from International Experiences

Deployment of increasing amounts of renewable energy (RE) presents certain challenges in reliably integrating larger quantities RE into existing bulk power system infrastructures. RE Bulk power typically refers to large-scale power generation at a centralized facility and in the context of RE can include wind farms, utility-scale solar, geothermal, hydro and biomass facilities. Based on international experiences, several actions and related good practices have emerged to support the deployment of RE in the bulk power system. This paper provides a review of emerging international best practices that support the integration of large-scale RE; examples of country-level efforts in implementing these practices; and key considerations for any country pursuing bulk power sector transformation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

A global perspective on bacterial diversity in the terrestrial deep subsurface

While recent efforts to catalogue Earth’s microbial diversity have focused upon surface and marine habitats, 12–20% of Earth’s biomass is suggested to exist in the terrestrial deep subsurface, compared to ~1.8% in the deep subseafloor. Metagenomic studies of the terrestrial deep subsurface have yielded a trove of divergent and functionally important microbiomes from a range of localities. However, a wider perspective of microbial diversity and its relationship to environmental conditions within the terrestrial deep subsurface is still required. Our meta-analysis reveals that terrestrial deep subsurface microbiota are dominated by Betaproteobacteria, Gammaproteobacteria and Firmicutes, probably as a function of the diverse metabolic strategies of these taxa. Evidence was also found for a common small consortium of prevalent Betaproteobacteria and Gammaproteobacteria operational taxonomic units across the localities. This implies a core terrestrial deep subsurface community, irrespective of aquifer lithology, depth and other variables, that may play an important role in colonizing and sustaining microbial habitats in the deep terrestrial subsurface. An in silico contamination-aware approach to analysing this dataset underscores the importance of downstream methods for assuring that robust conclusions can be reached from deep subsurface-derived sequencing data. Understanding the global panorama of microbial diversity and ecological dynamics in the deep terrestrial subsurface provides a first step towards understanding the role of microbes in global subsurface element and nutrient cycling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Optimizing Process Conditions during Catalytic Fast Pyrolysis of Pine with Pt/TiO 2 —Improving the Viability of a Multiple-Fixed-Bed Configuration

Catalytic fast pyrolysis (CFP) has been identified as a promising pathway for the production of renewable fuels and co-products. However, continued technology development is needed to increase process efficiency and reduce process costs. This report builds upon previous research in which a bifunctional metal–acid Pt/TiO 2 catalyst was utilized in a fixed-bed reactor operated with co-fed H 2 to improve product yield and reduce coke generation compared to conventional CFP methods. Here, we report further process optimization, in which we achieved similar CFP oil carbon efficiency (>35%) and CFP oil oxygen content (<20 wt %) to our previous report while reducing catalyst and equipment costs by increasing time-on-stream between regenerations by 40–95% and decreasing required regeneration time by more than a factor of 2. These process improvements were achieved by conducting parameter sweeps to determine optimum conditions for CFP and regeneration with key variables including pyrolysis temperature, catalytic upgrading temperature, hydrogen partial pressure, and regeneration oxygen concentration. Coupled with comprehensive oil analyses, these data provide foundational insight into the deoxygenation and coking chemistries for CFP under realistic process conditions while also advancing the technology through applied engineering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Seedling nitrogen uptake and rhizodeposition between mycorrhizal types

Tree mycorrhizal associations are associated with patterns in N cycling and soil organic matter (SOM) storage, however, we still lack a mechanistic understanding of what tree and fungal traits drive these patterns and how they will respond to global changes in soil N availability. To address this knowledge gap, we investigated how arbuscular mycorrhizal (AM)- and ectomycorrhizal (EcM)-associated seedlings alter rhizodeposition in response to increased inorganic N acquisition. Specifically, we conducted this greenhouse experiment in a sealed labeling chamber with an enriched 13Carbon atmosphere and 15Nitrogen enriched fertilizer over the course of five months from April 2021 - August 2021. To include the variability across tree species, we grew eight species of seedlings belonging to eight families that were either arbuscular (Acer rubrum, Nyssa sylvatica, Thuja occidentalis, and Prunus seritina) or ectomycorrhizal-associated (Quercus rubra, Tilia americana, Pinus strobus, and Betula lenta). We measured rhizodeposition (mg 13C), plant N uptake from fertilizer (mg N), net soil carbon, and the abundance of mycorrhizal fungi (ITS sequencing and qPCR). We also characterized fungal (ITS2) and bacterial (16S) soil communities.The data from this project are ".csv" files that can up downloaded into a folder, and then run in the associated R markdown scripts after changing the source folder location at the top of the script. These data include raw outputs and processed files (using the R markdown files) for seedling growth and biomass, 15N content, soil 13C content, raw reads and processed file versions for fungal and bacterial ASVS, and a final summary file used for modeling. R software is needed to run these data, and the packages needed are listed at the top of the R markdown file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)

Tropical forest dynamics play a crucial role in the global carbon, water, and energy cycles. However, realistically simulating the dynamics of competition and coexistence between different plant functional types (PFTs) in tropical forests remains a significant challenge. This study aims to improve the modeling of PFT coexistence in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a vegetation demography model implemented in the Energy Exascale Earth System Model (E3SM) land model (ELM), ELM-FATES. Specifically, we explore (1) whether plant trait relationships established from field measurements can constrain ELM-FATES simulations and (2) whether machine learning (ML)-based surrogate models can emulate the complex ELM-FATES model and optimize parameter selections to improve PFT coexistence modeling. We conducted three ensembles of ELM-FATES experiments at a tropical forest site near Manaus, Brazil. By comparing the ensemble experiments without (Exp-CTR) and with (Exp-OBS) consideration of observed trait relationships, we found that accounting for these relationships slightly improves the simulations of water, energy, and carbon variables when compared to observations but degrades the simulation of PFT coexistence. Using ML-based surrogate models trained on Exp-CTR, we optimized the trait parameters in ELM-FATES and conducted another ensemble of experiments (Exp-ML) with these optimized parameters. The proportion of PFT coexistence experiments significantly increased from 21 % in Exp-CTR to 73 % in Exp-ML. After filtering the experiments that allow for PFT coexistence to agree with observations (within 15 % tolerance), 33 % of the Exp-ML experiments were retained, which is a significant improvement compared to the 1.4 % in Exp-CTR. Exp-ML also accurately reproduces the annual means and seasonal variations in water, energy, and carbon fluxes and the field inventory of aboveground biomass. This study represents a reproducible method that utilizes machine learning to identify parameter values that improve model fidelity against observations and PFT coexistence in vegetation demography models for diverse ecosystems. Our study also suggests the need for new mechanisms to enhance the robust simulation of coexisting plants in ELM-FATES and has significant implications for modeling the response and feedbacks of ecosystem dynamics to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Theoretical evidence that root penetration ability interacts with soil compaction regimes to affect nitrate capture

Abstract Background and Aims Although root penetration of strong soils has been intensively studied at the scale of individual root axes, interactions between soil physical properties and soil foraging by whole plants are less clear. Here we investigate how variation in the penetration ability of distinct root classes and bulk density profiles common to real-world soils interact to affect soil foraging strategies. Methods We utilize the functional–structural plant model ‘OpenSimRoot’ to simulate the growth of maize (Zea mays) root systems with variable penetration ability of axial and lateral roots in soils with (1) uniform bulk density, (2) plow pans and (3) increasing bulk density with depth. We also modify the availability and leaching of nitrate to uncover reciprocal interactions between these factors and the capture of mobile resources. Key Results Soils with plow pans and bulk density gradients affected overall size, distribution and carbon costs of the root system. Soils with high bulk density at depth impeded rooting depth and reduced leaching of nitrate, thereby improving the coincidence of nitrogen and root length. While increasing penetration ability of either axial or lateral root classes produced root systems of comparable net length, improved penetration of axial roots increased allocation of root length in deeper soil, thereby amplifying N acquisition and shoot biomass. Although enhanced penetration ability of both root classes was associated with greater root system carbon costs, the benefit to plant fitness from improved soil exploration and resource capture offset these. Conclusions While lateral roots comprise the bulk of root length, axial roots function as a scaffold determining the distribution of these laterals. In soils with high soil strength and leaching, root systems with enhanced penetration ability of axial roots have greater distribution of root length at depth, thereby improving capture of mobile resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Chemical properties and single-particle mixing state of soot aerosol in Houston during the TRACER campaign

Abstract. A high-resolution soot particle aerosol mass spectrometer (SP-AMS) was used to selectively measure refractory black carbon (rBC) and its associated coating material using both the ensemble size-resolved mass spectral mode and the event trigger single particle (ETSP) mode in Houston, Texas, in summer 2022. This study was conducted as part of the Department of Energy Atmospheric Radiation Measurement (ARM) program's TRacking Aerosol Convection interactions ExpeRiment (TRACER) field campaign. The study revealed an average (±1σ) rBC concentration of 103 ± 176 ng m−3. Additionally, the coatings on the BC particles were primarily composed of organics (59 %; 219 ± 260 ng m−3) and sulfate (26 %; 94 ± 55 ng m−3). Positive matrix factorization (PMF) analysis of the ensemble mass spectra of BC-containing particles resolved four distinct types of soot aerosol, including an oxidized organic aerosol (OOABC,PMF) factor associated with processed primary organic aerosol, an inorganic sulfate factor (SO4,BC,PMF), an oxidized rBC factor (O-BCPMF), and a mixed mineral dust–biomass burning aerosol factor with significant contribution from potassium (K-BBBC,PMF). Additionally, K-means clustering analysis of the single-particle mass spectra identified eight different clusters, including soot particles enriched in hydrocarbon-like organic aerosol (HOABC,ETSP), sulfate (SO4,BC,ETSP), two types of rBC, OOA (OOABC,ETSP), chloride (ClBC,ETSP), and nitrate (NO3,BC,ETSP). The single-particle measurements demonstrate substantial variation in BC coating thickness with coating-to-rBC mass ratios ranging from 0.1 to 100. The mixing state index (χ), which denotes the degree of homogeneity of the soot aerosol, varied from 4 % to 94 % with a median of 40 %, indicating that the aerosol population lies in between internal and external mixing but has large temporal and source type variability. In addition, a significant fraction of BC-containing particles, a majority enriched with oxidized organics and sulfate, exhibit sufficiently high κ values and diameters conducive to activation as cloud nuclei under atmospherically relevant supersaturation conditions. This finding bears significance in comprehending the aging processes of rBC-containing particles and their activation into cloud droplets. Our analysis highlights the complex nature of soot aerosol and underscores the need to comprehend its variability across different environments for accurate assessment of climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Feedstock-Conversion Interface Consortium Annual Review of Research – FY 2021

Led by the U.S. Department of Energy’s Bioenergy Technologies Office, the Feedstock-Conversion Interface Consortium (FCIC) is a multi-lab collaboration that develops first-principles-based knowledge and tools to understand, quantify, and mitigate the effects of feedstock and process variability across the bioenergy value chain, from the field and forest through downstream conversion. The FCIC recently published its Annual Review of Research Fiscal Year (FY) 2021 report, which presents an overview of the research accomplishments of FCIC members in FY 2021, covering both the low- and high-temperature conversion pathways and all three process areas: Feedstock harvest and storage Preprocessing Conversion. The key research achievements of each task are presented, along with planned FY 2022 work that will build on the FY 2021 accomplishments.

09 BIOMASS FUELS↗
DOE OSTI2020

A phytophotonic approach to enhanced photosynthesis

Photosynthesis is the dominant biotic carbon sink on earth and hence presents an opportunity for enhanced sequestration of CO 2 . If the average net carbon fixation efficiency of terrestrial plants could be increased by 3.3%, all anthropogenic CO 2 accumulating in the atmosphere could instead be reduced and incorporated into terrestrial biomass. Plants make inefficient use of the overly abundant sunlight available to them, a result of having evolved to be competitive and survive highly dynamic environmental conditions rather than maximize photosynthetic productivity. We explore herein a phytophotonic approach to enhanced photosynthesis, whereby sunlight is redistributed by means of luminescent or persistent luminescent (PersL) materials. Phytophotonics has potential at varied scales, ranging from photobioreactors to greenhouses all the way to crops in the field, the latter having the potential to impact planetary CO 2 levels. The approach is three-fold: a spectral redistribution to relieve high-light-stress at the top surface of leaves and increasingly drive photosynthesis deeper in leaves and canopies; a minute-scale temporal redistribution to bridge periods of intermittent shade and reduce shock associated with variable light conditions; and a multiple-hour temporal redistribution to shift a fraction of high-intensity midday lighting to evening hours. Based on simulations of photoluminescent materials and light quality experiments with a model algal system, it is shown that while lengthening daylight hours will require significant improvements in PersL materials, the other two approaches show more immediate promise. We demonstrate a means of concentrating PersL light from SrAl 2 O 4 :Eu,Dy, approaching levels needed to effectively bridge periods of natural shade, and outline the scientific questions and technical hurdles remaining to realize the benefits of the proposed spectral shift.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Intraspecific variability in root traits and edaphic conditions influence soil microbiomes across 12 switchgrass cultivars

Microbial communities help plants access nutrients and tolerate stress. Some microbiomes are specific to plant genotypes and, therefore, may contribute to intraspecific differences in plant growth and be a promising target for plant breeding. Switchgrass (Panicum virgatum L.) is a potential bioenergy crop with broad variation in yields and environmental responses; recent studies suggest that associations with distinct microbiomes may contribute to variation in cultivar yields. We used a common garden experiment to investigate variation in 12 mature switchgrass cultivar soil microbiomes and, further, to examine how root traits and soil conditions influence microbiome structure. Here, we found that average root diameter varied up to 33% among cultivars and that they associated with distinct soil microbiomes. Cultivar had a larger effect on the soil bacterial than fungal community, but both were strongly influenced by soil properties. Root traits had a weaker effect on microbiome structure, but root length contributed to variation in the fungal community. Unlike the soil communities, the root bacterial communities did not group by cultivar, based on a subset of samples. Microbial biomass carbon and nitrogen and the abundance of several dominant bacterial phyla varied between ecotypes, but overall the differences in soil microbiomes were greater among cultivars than between ecotypes. Our findings show that there is not one soil microbiome that applies to all switchgrass cultivars, or even to each ecotype. These subtle but significant differences in root traits, microbial biomass, and the abundance of certain soil bacteria could explain differences in cultivar yields and environmental responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The Effect of Rapid Development on Soil CO2 Efflux in a Cellulosic Biofuel Stand

As awareness of climate change increases, the need for carbon neutral fuel sources is growing. Lignocellulosic biofuel derived from pine trees has been suggested as one potential energy source; however, it requires more research before its efficacy for climate change mitigation can be determined. Due to the large share of forest carbon held in soils and the extensive area of pine plantations in the southeast U.S., a better understanding of plantation soil carbon dynamics is critical for biofuel carbon accounting. This study evaluated the effects of canopy development and productivity on soil CO2 efflux, a proxy for soil respiration (Rs), in an intensively managed loblolly pine (Pinus taeda) stand over a period from May 2015 to December 2019. We found that leaf area index (LAI) and gross ecosystem production (GEP), as well as meteorological variables, had significant effects on Rs, but that both overall Rs and soil carbon pools did not increase over the course of the study. We thus hypothesize that GEP and LAI had intra-annual effects on Rs, and that the lack of change in Rs is the result of an increase in autotrophic respiration (Ra) that offset a decrease in decomposition of the previous stand’s organic matter.

09 BIOMASS FUELS↗
DOE OSTI2024

Understanding and modeling current and future coastal wetland methane dynamics (Final Report)

The coastal terrestrial-aquatic interface (TAI) is a highly dynamic component of the Earth system that plays a critical role in biogeochemical cycling. Due to its dynamic nature, the processes that regulate decomposition and methane (CH 4 ) emissions are of greater significance at the TAI than in upland systems. Despite this, we have limited mechanistic understanding of how climate stressors interact to regulate the electron acceptors and donors that determine decomposition pathways within TAIs, including the generation of hot spots and hot moments. Accurately modeling these processes is critical for incorporating the coastal TAI into Earth systems models, such as DOE’s E3SM. With previous DOE support, we adapted an aerobic terrestrial representation of decomposition using PFLOTRAN, a reactive flow and transport model, and added anerobic decomposition pathways, salinity, and oxygen (O 2 ) that fluctuates independently of water table level. However, because this model (PFLOTRAN TAI ) is based on decomposition rates and organic matter carbon to nitrogen ratios from terrestrial systems, its performance in TAI systems is currently limited by the lack of empirical data to properly parameterize variables. In addition, while PFLOTRAN TAI can simulate movement of O 2 into sediments, it is not currently capable of tracking the movement of CH 4 gas through plant tissues due to both current model structure and lack of available data. Our overall objective of the project was the increase our mechanistic understanding of CH 4 dynamics in response to environmental change, such that we can improve the representation of these dynamics in PFLOTRAN. We installed automated flux chambers in a new field-scale active soil warming experiment and set up marsh organs (mesocosms) to test effects of warming, flooding, and salinity. This resulted in a new dataset consisting of chamber-level CH 4 flux measurements across multiple sites, ecological conditions, and timeframes, as well as corresponding measurements on porewater chemistry, soil carbon quality, plant biomass, and redox reaction rates. Using these data, we improved PFLOTRAN TAI to more accurately model CH 4 dynamics and successfully tested our hypotheses. This grant contributed to the professional development of 3 postdocs, 4 undergraduate interns, 4 teacher interns, 17 technicians, and 15 participatory scientists. The automated chamber technology designed for this grant has also been shared with multiple new projects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Compressibility and permeability of particulated non-recyclable municipal solid waste

Biofuels from non-recyclable municipal solid waste (NMSW) stand at the forefront of energy sustainability. However, their widespread adoption is hampered by persistent material handling issues stemming from the variability in NMSW material properties. An enhanced understanding of particulated NMSW properties, particularly compressibility and permeability, is essential to address the feedstock handling challenges and optimize the handling equipment. This study measures the compressibility and gas permeability of five streams of NMSW materials (i.e., rigid plastics, cardboard, thin film, paper, and foam) and their mixtures under different stress conditions. The results highlight the significant variability in compressibility and gas permeability among different streams, as well as the impacts of particle sizes. A semi-empirical model capable of predicting the gas permeability of NMSW mixtures is established and validated. Here, the results also highlight that reduced NMSW particle size helps promote consistency in NMSW feedstock’s physical and mechanical properties, which is favored for handling equipment design in waste-to-energy recovery facilities.

09 BIOMASS FUELS↗
DOE OSTI2026

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗
DOE OSTI2022

Density fluctuations, homeostasis, and reproduction effects in bacteria

Abstract Single-cells grow by increasing their biomass and size. Here, we report that while mass and size accumulation rates of single Escherichia coli cells are exponential, their density and, thus, the levels of macromolecular crowding fluctuate during growth. As such, the average rates of mass and size accumulation of a single cell are generally not the same, but rather cells differentiate into increasing one rate with respect to the other. This differentiation yields a density homeostasis mechanism that we support mathematically. Further, we observe that density fluctuations can affect the reproduction rates of single cells, suggesting a link between the levels of macromolecular crowding with metabolism and overall population fitness. We detail our experimental approach and the “invisible” microfluidic arrays that enabled increased precision and throughput. Infections and natural communities start from a few cells, thus, emphasizing the significance of density-fluctuations when taking non-genetic variability into consideration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Matching diverse feedstocks to conversion processes for the future bioeconomy

A wide variety of wasted or underutilized organic feedstocks can be leveraged to build a sustainable bioeconomy, ranging from crop residues to food processor residues and municipal wastes. Leveraging these feedstocks is both high-risk and high-reward. Converting mixed, variable, and/or highly contaminated feedstocks can pose engineering and economic challenges. However, converting these materials to fuels and chemicals can divert waste from landfills, reduce fugitive methane emissions, and enable more responsible forest management to reduce the frequency and severity of wildfires. Historically, low-value components, including ash and lignin, are poised to become valuable coproducts capable of supplementing cement and valuable chemicals. Here, we evaluate the challenges and opportunities associated with converting a range of feedstocks to renewable fuels and chemicals.

09 BIOMASS FUELS↗
DOE OSTI2024

Development and Validation of a Process Model and Open-Source Process Simulator for Microalgae-Based Tertiary Phosphorus Recovery

Microalgae-based tertiary wastewater treatment has the potential to meet stringent effluent phosphorus limits, with the added benefit of producing a marketable feedstock. However, the lack of validated mechanistic models and their implementation in process simulators have limited the adoption of this technology. In this study, an updated lumped pathway metabolic model (Phototrophic-Mixotrophic Process Model, PM 2 ), including both photoautotrophic and heterotrophic metabolisms of microalgae, was developed to predict effluent phosphorus concentration and biomass yield in response to dynamic influent and varying environmental conditions. The model was implemented in QSDsan – an open-source, Python-based design and simulation platform – for robust simulation under uncertainty. A global sensitivity analysis was performed to prioritize model parameters for calibration. The model was then calibrated and validated using batch experimental data and 45 days of continuous online monitoring data from a full-scale (568 m 3 ·d -1 ) microalgae-based tertiary wastewater treatment plant (EcoRecover process). In particular, along with dynamic influent composition, temperature and light intensity data with diel variation were provided as model inputs to reflect the microalgal behavior under day-night cycling. Overall, the QSDsan-based microalgae process simulator was able to predict effluent phosphorus within 0.02–0.04 mg-P·L -1 , while also capturing the general trends of state variables according to nutrient availability.

Lumped pathway metabolic model↗