Zero-dimensional Model Research Articles

Isoprene peroxy chemistry operates competitively in areas of East China

We validated the recently updated Caltech Isoprene Mechanism (CIM), which represents the complex dynamics of isoprene peroxy isomers through repetitive O2 addition and dissociation reactions, using a dataset synthesized from two field measurements taken place in both urban and rural areas of East China. The dataset covers a large span of nitric oxide (NO) levels ranging from tens of ppt characteristic of clean rural air to tens of ppb typically found in the polluted urban environment. An observationally constrained zero-dimensional box model that incorporates the CIM mechanism was constructed to simulate the daytime profiles of methacrolein (MACR) and methyl vinyl ketone (MVK), two major first-generation products that account for over half of the total carbon oxidation flow of isoprene. A closer agreement with the measurements was found compared with the traditional mechanism that prescribes a fixed yield of MACR and MVK across all NO levels. This result demonstrates that the isoprene peroxy dynamics operate competitively in the area and play a governing role in the final distribution of isoprene oxidation products. The atmospheric implication is that, with effective measures taken to reduce pollutant emissions in East China, the prevailing chemical regime at play has evolved and implementing the updated isoprene mechanism into regional models will have a profound impact on the predicted radical cycling and ozone production of the local atmosphere.

Elucidating the Performance Limits for Electrochemical CO2 Separation Using Exergy Loss Analysis and Zero-Dimensional Modeling

The use of renewable electricity to power electrochemical CO2 removal and concentration from point sources, air, and seawater is receiving considerable interest as one strategy in our portfolio of options for mitigating climate change. Two common embodiments of this idea involve CO2 separation via electrochemically reversible binding of CO2 to a nucleophilic redox mediator, or a pH swing/gradient in aqueous solution. For either embodiment to be feasible, the energetic cost of regenerating the sorbent should be low at practical separation throughputs. Consequently, there have been a number of efforts to understand how the thermodynamic minimum work input for a given separation cycle varies under different conditions using modeling. In this talk, we demonstrate that the thermodynamic minimum work input for electrochemical CO2 separation is the sum of exergy losses incurred from differences between the partial pressure of CO2 within the CO2 source/exit streams and the partial pressure of CO2 in the sorbent. This framework rationalizes minimum work inputs for pH-swing and redox-mediator-based CO2 separation cycles, and motivates the measurement or estimation of the aforementioned CO2 partial pressures in experimental studies. Applying a zero-dimensional model recently developed in our group to pH-swing-driven CO2 separation, we will then discuss how the total energetic cost of CO2 separation can be simulated under a variety of scenarios relevant to practical CO2 concentration from flue gas.

(Invited) Modeling the Dependence of the Lithium-Sulfur Battery Performance on the Materials and Cell Design Factors

Lithium-sulfur (Li-S) batteries have attracted significant research interest in the last couple of decades due to their significantly high theoretical specific capacity in addition to the fact that the cathode active material sulfur is abundant and inexpensive and has a high capacity. However, several mechanisms hinder the Li-S battery performance; the depletion of the lithium metal or the electrolyte in the cell with cycling, the polysulfide shuttle mechanism due to the transport of the soluble reaction intermediates between the cathode and the anode, and the passivation of the cathode surface due to the precipitation of the insoluble product Li2S are some examples of these mechanisms that lead to fast capacity fade and thus limited cycle life of the Li-S batteries. As a result of the highly complicated reaction and degradation mechanisms in the cell, the performance of the Li-S battery significantly depends on the materials and cell design. Consequently, it is of significant importance to study this key relation. Herein, we develop electrochemical models to predict the impact of critical materials and cell design parameters: electrolyte-to-sulfur (E/S) ratio, carbon-to-sulfur (C/S) ratio, sulfur loading, and carbon and electrolyte properties, on the Li-S battery performance. We use zero-dimensional (concentration-independent) and one-dimensional (concentration-dependent) models to project the isothermal, constant-current discharge behavior of the Li-S cell. In our models, a novel definition for the electrochemically active area is proposed, which describes the cathode area as a function of the carbon amount and the porosity in the cathode. Experimental trends on the influence of cathode design parameters on the discharge performance are captured successfully. A mechanistic explanation of these trends is offered with the presented model by introducing the proposed electrochemically active area.

Understanding Capacity Fade in Organic Redox-Flow Batteries by Combining Spectroscopy with Statistical Inference Techniques

Aqueous-soluble organic and organometallic redox-active molecules show considerable promise as redox-flow battery (RFB) reactants because their costs at scale are projected to be low. However, most aqueous organic RFB undergo rapid degradation resulting in high rates of capacity fade. Considerable research has been devoted towards discovering chemically stable charge carriers for RFBs. However, the decomposition mechanisms of underperforming chemistries during flow cell cycling are often poorly understood, and in some instances, competing or incompatible hypotheses about the relationship between reactant decay and capacity fade have often been proposed.In this talk, we will demonstrate that combining spectroscopic and statistical inference techniques can be used to shed light on relationship between RFB reactant degradation and capacity fade. Specifically, we employ in operando ultraviolet-visible spectrophotometry along with Bayesian inference, Bayesian model selection, and multivariate curve regression in order to quantify the relative probabilities of different decay mechanisms given the data at hand, and to estimate rate constants for specific reactions within the most probable decay scheme. In principle, rate constants thus obtained can be fed into a zero-dimensional electrochemical flow cell model, recently developed by our group, that can simulate capacity fade given a reactant decay mechanism, associated rates, and the cycling protocol. This work has the potential to shed light on the relationship between capacity fade and reactant decay in various organic RFB chemistries, and to enable prediction of the lifetimes of commercial-scale RFBs.

A Computationally-Efficient, Zero-Dimensional Stack Model for Simulating Redox Flow Battery Performance

Redox flow batteries (RFBs) are an electrochemical energy storage platform with potential to support grid-scale decarbonization and resiliency efforts. The RFB architecture allows for decoupled energy and power, long lifetimes with simplified maintenance, and a range of potential chemistries. Despite their promising characteristics, present embodiments are too expensive for widespread adoption.1 , 2 Significant research efforts have focused on advancing new redox couples and constituent materials (e.g., electrodes, flow fields, membranes), but these contributions primarily focus on individual lab-scale cells, due, at least in part, to the time, material, and expertise required to investigate performance and durability in larger device formats (e.g., multi-cell stacks). This results in knowledge gaps within the field—specifically, electrochemical reactor scaling relationships are not yet well-understood and the relative importance of many tunable molecular and material properties at the stack level remains unclear.To aid with RFB stack design and understanding, computational models of varying dimensionality and complexity have been developed to simulate charge-discharge cycling.3 Zero- and one-dimensional models, which make simplifying assumptions about underlying electrochemistry and fluid dynamics, are particularly useful for capturing general trends in cell performance at a much lower computational cost than comprehensive multi-dimensional frameworks. However, previous models still employ numerical methods to solve complex systems of differential equations, which slows simulation time and impedes broader inquiry (e.g., durational cycling, parametric property sweeps).4 ,5 Further, existing models are typically designed to interrogate specific chemistries and, as such, cannot easily incorporate the array of properties and operating conditions possible for different redox couples and constituent materials.In this presentation, we will discuss the formulation and application of a computationally-lightweight zero-dimensional RFB stack model suitable for linking stack-level electrochemical and fluid dynamic performance characteristics to molecular- and cell-level property sets. By constructing analytical expressions for mass balances and cell voltages under galvanostatic cycling conditions,6 this framework facilitates connections between system design, component material properties, operating conditions, and cycling performance at the stack level with little computational expense. To validate the functionality of the model, we simulate durational performance (i.e., capacity fade, energy efficiency, power density) across a range of different representative chemistries, operating conditions, and reactor scales. Additionally, we evaluate the impact of typical parasitic losses—including shunt currents, crossover, and species decay —on cycling characteristics over hundreds of charge-discharge cycles. Finally, we will discuss pathways to integrate this framework into techno-economic assessments to enable physics-informed cost predictions that support more holistic technology comparisons. More broadly, the tools described here have the potential to advance understanding of cell-to-stack design principles by enabling assessments of system viability early in the innovation pipeline. Acknowledgments This work was supported by the Joint Center for Energy Storage Research, an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences. B.J.N gratefully acknowledges the NSF Graduate Research Fellowship Program under Grant Number 1122374. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. References B. R. Chalamala et al., Proceedings of the IEEE, 102, 976–999 (2014).Z. Yang et al., Chem. Rev., 111, 3577–3613 (2011).B. Kumar Chakrabarti et al., Sustainable Energy & Fuels, 4, 5433–5468 (2020).M. Pugach, M. Kondratenko, S. Briola, and A. Bischi, Applied Energy, 226, 560–569 (2018).J. L. Barton and F. R. Brushett, Batteries, 5, 25 (2019).B. J. Neyhouse, J. Lee, and F. R. Brushett, J. Electrochem. Soc., 169, 090503 (2022).

Compositionally Unbalanced Symmetric Cell Cycling As a Method for Quantifying Crossover Rates in Redox Flow Cells

Redox flow batteries (RFBs) are a promising grid-scale energy storage platform, but broad deployment is stymied by technical and economic constraints.1 Within this context, the membrane serves a central role in determining battery performance, selectively facilitating the conduction of supporting salts to mitigate resistive losses while preventing the undesired crossover of active species.2 Tradeoffs between these two facets—namely, conductivity and permeability—requires a thorough understanding of how transport proceeds under the dynamic conditions observed in practical embodiments (e.g., varying compositions, alternating polarity). To this end, a wide range of experimental tools exists for characterizing membrane properties under controlled environments, but few methods directly assess crossover and resistive losses in conventional redox flow cells.3 In this presentation, we introduce a facile method for quantifying crossover rates in flow cells—compositionally unbalanced symmetric cell cycling (CUSCC). Using a previously developed zero-dimensional modeling framework,4 we first examine fundamental processes that give rise to a characteristic “capacity gain” profile. We then validate the technique experimentally using FeCl2 / FeCl3 and Nafion as a representative system, confirming theoretical predictions and establishing the efficacy of CUSCC for characterizing crossover. Finally, we apply the model to estimate transport parameters from experimental capacity gain profiles, yielding results that are consistent with conventional ex situ measurements. Overall, CUSCC is a robust method for quantifying crossover rates with standard flow cell hardware, potentially expanding the experimental toolkit for RFB development. Acknowledgments This work was supported as part of the Joint Center for Energy Storage Research, an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, contract number DE-AC02-06CH11357. B.J.N gratefully acknowledges the NSF Graduate Research Fellowship Program under Grant Number 2141064. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. References M. L. Perry and A. Z. Weber, J. Electrochem. Soc., 163, A5064–A5067 (2016).J. Yuan, Z.-Z. Pan, Y. Jin, Q. Qiu, C. Zhang, Y. Zhao, and Y. Li, Journal of Power Sources, 500, 229983 (2021).Y. A. Gandomi, D. S. Aaron, J. R. Houser, M. C. Daugherty, J. T. Clement, A. M. Pezeshki, T. Y. Ertugrul, D. P. Moseley, and M. M. Mench, J. Electrochem. Soc., 165, A970 (2018).B. J. Neyhouse, J. Lee, and F. R. Brushett, J. Electrochem. Soc., 169, 090503 (2022).

In-cylinder thermochemical fuel reforming for high efficiency in ammonia spark-ignited engines through hydrogen generation from fuel-rich operations

The increasing trend towards global carbon neutrality is driving interest in ammonia fuel as a potential zero-carbon solution for transportation. However, due to the non-ideal combustion characteristics of ammonia, it is necessary to mix it with hydrogen to achieve better engine performance. While generating hydrogen from ammonia on-board is a key technology, there has been limited research in this area, which is hindering the introduction of ammonia engines to the market. This paper proposes an initial study on the feasibility of thermochemical fuel reforming (TFR) technology for generating hydrogen from ammonia fuel-rich operations. A zero-dimensional engine model, validated against experimental results, is used to assess the potential of the TFR approach and identify any barriers to implementation. The results indicate that the optimum air-fuel ratio for hydrogen generation is an equivalence ratio of two, as it maximizes hydrogen production rates while minimizing operational complexities. Moreover, increasing intake temperature, pressure, and compression ratio enhances hydrogen generation from the ammonia-rich mixture. However, owing to the limited reactivity of the ammonia fuel, the rate of hydrogen production remains relatively small (less than 1%/cycle), significantly falling short of the demand. Additionally, the combustion of the fuel-rich mixture in the TFR cylinder results in approximately 20%/cycle of hydrogen in the emitted gases, potentially enhancing the efficiency of working cylinders, but it also poses the risk of engine component failures due to the high pressure rise rate. In conclusion, the use of solely ammonia to produce hydrogen in the TFR cylinder proves to be inefficient, warranting further investigations into the potential benefits of incorporating additives to promote hydrogen production. Alternatively, if a catalyst is utilized to promote hydrogen production from ammonia, a separate electrically heated hydrogen generation system independently of the engine system proves to be more effective, allowing continuous hydrogen production, unlike the intermittent high-temperature environment provided by TFR technology.

Analysis of optimal expansion dynamics in a reciprocating drive for a micro-CAES production system

This paper presents the unique design and processing conditions of a micro-CAES device that generates, stores and delivers electrical energy with highly efficient total energy conversion. The micro-CAES installation is divided into six parts, where air, the energy carrier, is compressed, stored, heated and expanded in appropriate sections. We focus mainly on the post-preparation section, where the air mass flow is pulse heated and controlled by Pulse Width Modulation (PWM), and the expansion section, where multiple piston drives are used to achieve maximum energy conversion efficiency or the required amount of electricity to be fed into the grid. We discover that the controllability of expansion process performance is improved by exploiting the inertia of the following elements in multiple reciprocating engines: piston, piston rod, connecting rod, crank, and crankshaft, connected with air injection dynamics and pulse heating by Thermal Energy Storage (TES). Furthermore, we investigate the efficiency using our own zero-dimensional mathematical model illustrating the complex dynamics of air processing. The mathematical model was compared with experimental data for the single-drive air expander, showing qualitatively and quantitatively satisfying model reliability. Moreover, we performed a sensitivity analysis to determine the best number of reciprocating drives involved in the expansion section and the best operating conditions. The use of the PWM function of mass flow of air supplied the expander with an index of 0.5 together with pulsating air heating in the expander installation, including three- reciprocating drives, allows a maximum total energy conversion efficiency of 81%. The presented piston drive expander seems to be a promising solution for micro-CAES in residential and industrial applications, especially where additional low-temperature waste heat is available, and an intermittent control strategy is implemented.

Peroxy radical chemistry during ozone photochemical pollution season at a suburban site in the boundary of Jiangsu–Anhui–Shandong–Henan region, China

Ambient peroxy radical (RO2⁎ = HO2 + RO2) concentrations were measured at a suburban site in a major prefecture-level city (Huaibei) in the boundary of Jiangsu–Anhui–Shandong–Henan region, which is the connecting belt of air pollution in the Beijing–Tianjin–Hebei region and the Yangtze River Delta. Measurements were carried out during the period of September to October 2021 to elucidate the formation mechanism of O3 pollution. The observed maximum concentration of peroxy radicals was 73.8 pptv. A zero-dimensional box model (Framework for 0-Dimensional Atmospheric Modeling, F0AM) based on Master Chemical Mechanism (MCM3.3.1) was used to predict radical concentrations for comparison with observations. The model reproduced the daily variation of peroxy radicals well, but discrepancies still appear in the morning hours. As in previous field campaigns, systematic discrepancies between modelled and measured RO2⁎ concentrations are observed in the morning for NO mixing ratios higher than 1 ppbv. Between 6:00 and 9:00 am, the model significantly underpredicts RO2⁎ by a mean factor of 7.2. This underprediction can be explained by a missing RO2⁎ source of 1.2 ppbv h−1 which originated from the photochemical conversion of an alkene-like chemical species. From the model results it shows that the main sources of ROx (= OH + HO2 + RO2) are the photolysis of oxygenated volatile organic compounds (OVOCs, 33 %), O3 and HONO (25 %), and HCHO (24 %). And the major sinks of ROx transitioned from a predominant reaction of radicals with NOx in the morning to a predominant peroxy self- and cross-reaction in the late afternoon. The introduction of an alkene-like species increased RO2 radical concentration and resulted in 14 % increase in net daily integrated ozone production, indicating the possible significance of the mechanism of alkene-like species oxidation to peroxy radicals. This study provides important information for subsequent ozone pollution control policies in Jiangsu–Anhui–Shandong–Henan region.

Improving the power output estimation for a tidal power plant: a case study

The aim of this study is to analyse the importance of considering the variation in the discharge coefficient (Cd) of a sluice passage and the tidal current speed (i.e. the flow speed approaching the passage) in improving the estimation of power output for a tidal power plant. The simulation results, using a commercial computational fluid dynamics software package, Flow 3D, have shown that the Cd value of a sluice passage varies with the upstream (i.e. on the sea side of the impounded area) water level. In addition, the results of a case study carried out in the Taedong bay (Democratic People's Republic of Korea), using a zero-dimensional model, highlight the importance of considering variations in the tidal current speed as well as Cd when estimating the power output for a tidal power plant. In this study, a detailed scheme is proposed to consider the approach speed at each stage of filling, holding, generating and holding for the mode of ebb generation. It is hoped that the results of this study will help scientists, engineers and decision makers to accurately evaluate the tidal power output of tidal barrages and lagoons in many regions of the world.

State estimation of the dynamic behavior of plasma properties in a Hall effect thruster discharge

The dynamic behaviors of the plasma properties in a Hall effect thruster discharge channel are estimated using a physics-constrained extended Kalman filter (PC-EKF) with a zero-dimensional global plasma model. The state estimates obtained using the global plasma EKF model are augmented by experimental data, in this case a discharge current signal. The use of state estimation allows for the determination of time-resolved plasma behaviors that may be difficult to measure experimentally, such as electron temperature and bulk velocity. The model is applied to investigate the plasma behavior at various thruster operating conditions, i.e. at varying discharge voltages and discharge currents. The estimated plasma properties are used to determine the classical electron collision frequency and the anomalous electron scattering frequency, which are shown to vary in time.

A Method for Quantifying Crossover in Redox Flow Cells through Compositionally Unbalanced Symmetric Cell Cycling

Active species crossover continues to frustrate durational performance for redox flow batteries (RFBs), requiring thorough evaluation of membrane/separator properties. Characterization workflows typically employ a suite of ex situ experimental techniques, but these approaches do not capture the dynamic conditions (e.g., variable concentrations, alternating polarity) encountered in redox flow cells. Here, we report a facile method for assessing crossover directly in redox flow cells—compositionally unbalanced symmetric cell cycling (CUSCC). Based on conventional symmetric cell cycling, CUSCC imposes a concentration gradient between two chemically similar half-cells, inducing species crossover during galvanostatic cycling, which results in a characteristic “capacity gain” over time. We first develop a zero-dimensional model to describe fundamental processes that underpin the technique and examine the dependence of capacity gain on membrane/separator properties and operating conditions. Subsequently, we perform proof-of-principle experiments using FeCl2/FeCl3 and NafionTM 117 as a representative system and demonstrate results consistent with those predicted from simulations. Finally, we use model fits of the capacity gain data to extract membrane transport parameters, obtaining similar values to those measured from ex situ techniques. Overall, this work describes a promising new approach for characterizing species crossover and expands the RFB testing toolbox.

The development of a zero-dimensional collisional-radiative model for interpreting plasma emission in low temperature divertor plasmas in tokamaks

Collisional-radiative models are commonly used to analyse atomic and molecular processes in low temperature plasmas by determining the distribution functions of excited states as functions of various plasma parameters. This paper outlines the improvements to a zero-dimensional collisional-radiative model, developed at Keio University, for purposes relevant to the analysis of emission measurements in low temperature hydrogen plasmas. The extension of this 0D model centres on the inclusion of additional molecular species and reactions, a calculation of emission intensity in order to directly compare with experimental work, and the addition of a simple wall model to allow for a deeper understanding of recycling of atoms and molecules in low temperature plasmas close to plasma facing components in fusion machines. Results from the improved model are then compared with both an existing CR model for benchmarking and experimental emission spectroscopy data from an inductively-coupled plasma device at the University of Liverpool. These results show how the developments to the model have increased the relevance to experimental plasmas, such as those in the power exhaust regions of fusion machines, with the ratio of the Hβ/Hα line intensities outputted from the improved CR model mirroring more closely the emission measured experimentally in the ICP device.

Parameter identification algorithm for dynamic modeling of industrial-scale vanadium redox flow batteries

In this work, we propose a two-stage parameter identification algorithm for the zero-dimensional model of vanadium redox flow battery (VRFB) based on the fitting of the available experimental data. Specifically, in the first stage, the parameters of overpotentials are obtained from the discharge polarization curve at the state of charge of 50%. In the second stage, the parameters of capacity loss and crossover are identified by the minimization of square error between the predicted and experimental charge-discharge curves at a certain value of load current. The algorithm allows to assess the degree of degradation of the battery components, and capture the effects associated with crossover and mass transfer limitations at high load currents and low flow rates. We verify this algorithm with a reference simulation considering the most interesting practical cases of battery use and validate it with three experimental VRFB systems with power and capacity ratings in the range of 5–10 kW and 5–100 kWh, correspondingly. The results show a good agreement with experimental data having an average error value of less than 1%. The results of the study are an important step in further developments of advanced control algorithms for large-scale energy storage systems.

Zero-dimensional model for the prediction of carbon nanotube (CNT) growth region in heterogeneous methane-flame environment

Zero-dimensional model for the prediction of carbon nanotube (CNT) growth region in heterogeneous methane-flame environment

Source apportionment of gaseous Nitrophenols and their contribution to HONO formation in an urban area

Nitrophenols (NPs) have significant impacts on human health, climate, and atmospheric chemistry. Despite numerous measurements of particulate NPs, still little is known about their gaseous atmospheric abundances, sources, and fate. Here, four gaseous NPs [2,4-dinitrophenol (2,4-DNP), 4-nitrophenol (4-NP), 2-nitrophenol (2-NP), and 2-Methyl-4-nitrophenol (2-Me-4-NP)] were continuously monitored during late Spring at an urban site in Houston, Texas. Among the four NPs, 4-NP showed the highest abundance, followed by 2-Me-4-NP, 2-NP, and 2,4-DNP with average concentrations of 1.07 ± 0.19 ppt, 0.47 ± 0.12 ppt, 0.41 ± 0.16 ppt, and 0.27 ± 0.09 ppt, respectively. The positive matrix factorization (PMF) model identified seven sources: industrial NPs, secondary formation, phenol sources, acetonitrile source, natural gas/crude oil, traffic, and petrochemical industries/oil refineries. A zero-dimensional photochemical box model was used to simulate the observed 2-NP and 2,4-DNP. A 50.0% and 70.0% jNO2 was found to be consistent with the measured 2-NP and 2,4-DNP. This yields a nitrous acid (HONO) production of 7.5 ± 2.5 ppt/h from 06:00 to 18:00 Central Standard Time (CST) from both NPs. An extrapolation including other known NPs suggests a maximum HONO formation of 13.8 ppt/h. The results of this study suggest that using PMF analysis supplemented by photochemical box model provides identification of the NPs sources and their atmospheric implication to HONO formation.

Efficiency improvement and Thermo-economic analysis of proton exchange membrane fuel cell system with energy recovery for both air and hydrogen

Proton exchange membrane fuel cells (PEMFCs) supplies as one of the promising solutions for carbon neutrality in transportation. However, excessive energy consumption of the auxiliary equipment remains one of the major obstacles in realizing large-scale commercialization. Therefore, in this study, a novel PEMFC system with energy recovery for both air and hydrogen was developed to enhance the energy efficiency. A zero-dimensional thermodynamic model was established to forecast and estimate system's performance. Additionally, the economic analysis considering cost sensitivity was conducted to provide feasibility evidence. The effects of current density, cathode air pressure, and turbo-expander cost on the system's efficiency and economic revenue were investigated. The results showed that the energy recovery effect and economic revenue increase with an increase in current density when the operating temperature constant was kept constant and increase with an increase in cathode air pressure when the current density constant was kept constant. Compared to that of the conventional PEMFC system, the system's efficiency of the novel model increased by 6.27%, corresponding to a current density of 1.21 A/cm2 when the cathode air pressure remains constant at 0.31 MPa. The lower of the cost of the turbo-expanders, the higher of the net revenue. The PEMFC system with turbo-expanders can achieve more revenue at larger current density and cathode air pressure conditions.

Machine learning based prediction of biomass pyrolysis with detailed reaction kinetics for thermally-thick particles: from 1D to 0D

In reactor-scale CFD modeling of biomass pyrolysis with thermally-thick particles, zero-dimensional (0D) models coupled with lumped kinetics are commonly used, as they are simple and computationally efficient. However, intra-particle heat transfer, which cannot be directly implemented in 0D models, has significant effects on pyrolysis behaviors of thermally-thick biomass particles. Additionality, lumped kinetics usually fails to predict detailed composition of pyrolysis products. To overcome these issues, a widely-used one-dimensional (1D) model that can directly incorporate intra-particle heat transfer was employed with a detailed pyrolysis kinetics in this work to develop a corrected 0D (Cor-0D) model for accurate CFD modeling of biomass pyrolysis inside thermally-thick particles. Correction coefficients of external heat transfer, particle diameter, and pyrolysis reactions were introduced by comparing predictions of the 1D model with those of the 0D model quantitatively to reflect the effects of respective factors. The comparison demonstrates that if correction coefficients are properly determined, predictions of the developed Cor-0D model are in good agreement with experimental data as well as those of the employed 1D model under various conditions, while the 0D model overestimates mass loss rate and particle heating rate for thermally-thick biomass particles. Considering that correction coefficients are case dependent and determination of their values are tedious, artificial neural network (ANN) was used to correlate correction coefficients as functions of convective heat transfer coefficient, particle size, gas temperature, moisture content, and particle’s dimensionless temperature to derive an ANN-Cor-0D model. Results show that the ANN-Cor-0D model has the same performance as the Cor-0D model.

Quantitative characterization of the temporal-spatial evolution of RONS in air surface micro-discharge using UV–VIS optical absorption spectroscopic diagnosis and modeling approach

The research of the chemically active species of cold atmospheric pressure plasmas is a essential step for a more in-depth comprehension of the effects of its interaction with the target. In this paper, the temporal and spatial evolution of key species O3, NO2 and NO3 produced by surface micro-discharge in air were investigated. UV–VIS optical absorption spectroscopy at 254 nm, 400 nm and 662 nm were used to measure the concentrations of O3, NO2 and NO3, respectively. The results show that the temporal evolution of O3, NO2 and NO3 are revealed a significant correlation with the surface power density (SPD). The phenomenon of O3 and NO3 quenching occur once the SPD overcomes a critical value of 0.15 W cm−2. An O3-enriched atmosphere (peak concentration around 3000 ppm) is formed when the SPD is below the critical value, and a NO2-enriched atmosphere (maximum NO2 density around 600 ppm) is formed at higher SPD. In addition, the concentration distribution of O3, NO2 and NO3 in the chamber ranging from 10–100 mm of the downstream of the mesh electrode tends to be uniform. Finally, a zero-dimensional model of the afterglow chemistry, validated using the experimental measurements, is developed to determined important reactions affecting O3, NO2 and NO3 respectively, and obtain insight into the evolutionary behavior of the considered reactive species.

Importance of spatial heterogeneity of nutrient loading on the ecological status of lake Tana, Ethiopia

Understanding spatial variability of nutrient loading and transport in large lakes, and its effect on the eutrophication status are often lacking and hinder effective and sustainable management of lakes. Yet knowledge on the extent of spatial variability of eutrophication in large shallow tropical lakes hinders sustainable management. Understanding how eutrophication varies across lakes helps spatially targeted nutrient load mitigation strategies. This modelling study assesses how spatial heterogeneity in nutrient loads may drive spatial variability in in-lake eutrophication effects for Lake Tana, a large tropical lake. We applied a novel method of coupling a 2D application of the flow model Duflow with the ecosystem model PCLake+ (a zero-dimensional food-web model). We defined different impact zones of major tributaries with a tracer model and simulated ecological processes and food web relations for each impact zone. Furthermore, we defined critical nitrogen and phosphorus loads for each impact zone and the whole lake. Subsequently, we analysed the spatial variability of phytoplankton and aquatic vegetation dynamics among the impact zones, as well as the differences in critical loadings. The model results indicate different ecological conditions in the impact zones and the importance of spatial heterogeneity of eutrophication reflected in different critical loading. The model shows that the north impact zone and northwest impact zone are mainly vegetation-dominated, and southeast impact zone and southwest impact zone are phytoplankton-dominated. Simulated chlorophyll-a concentrations ranged from 0 to 59 µg l−1 and the coverage of the vegetation ranged from 1 to 127 g d.w.m−2, aligning with the range of observed values in the lake. We demonstrate that this model approach can help to identify spatial heterogeneity in hydrology and eutrophication, as such lakes cannot be regarded as completely mixed systems. Our new approach demonstrates how spatially variable nutrient loading correspond to variability in eutrophication effects in a large shallow tropical lake, and will aid in setting more spatially-targeted lake management strategies.