Models Of Reaction Networks Research Articles

A reaction network model of microscale liquid-liquid phase separation reveals effects of spatial dimension.

Proteins can form droplets via liquid-liquid phase separation (LLPS) in cells. Recent experiments demonstrate that LLPS is qualitatively different on two-dimensional (2D) surfaces compared to three-dimensional (3D) solutions. In this paper, we use mathematical modeling to investigate the causes of the discrepancies between LLPS in 2D and 3D. We model the number of proteins and droplets inducing LLPS by continuous-time Markov chains and use chemical reaction network theory to analyze the model. To reflect the influence of space dimension, droplet formation and dissociation rates are determined using the first hitting times of diffusing proteins. We first show that our stochastic model reproduces the appropriate phase diagram and is consistent with the relevant thermodynamic constraints. After further analyzing the model, we find that it predicts that the space dimension induces qualitatively different features of LLPS, which are consistent with recent experiments. While it has been claimed that the differences between 2D and 3D LLPS stem mainly from different diffusion coefficients, our analysis is independent of the diffusion coefficients of the proteins since we use the stationary model behavior. Our results thus give new hypotheses about how space dimension affects LLPS.

In vitro analysis of a competitive inhibition model for T7 RNA polymerase biosensors

T7 RNA polymerase (T7 RNAP) biosensors, in which T7 RNAP transcribes some reporter gene or signal in response to external stimuli, have wide applications in synthetic biology and metabolic engineering. We adapted a biochemical reaction network model and used an in vitro transcription assay to determine network parameters for different T7 RNAP constructs. Under conditions where template DNA is limiting, the EC50 values of native and engineered T7 RNAPs ranged from 33 nM (29–37 95 % c.i.) to 570 nM (258–714 95 % c.i.) (wild-type T7 RNAP). The measured EC50 values were largely insensitive to free magnesium, pH, or other buffer conditions. Many biosensor configurations use a split RNAP construct, where the C-terminal (CT7) and N-terminal T7 (NT7) are fused to proximity induced dimerization modules. We used proteolysis and ion exchange chromatography to prepare a CT7 (80 kDa) product. The impact of free CT7 on T7 RNAP transcriptional activity was well described by a competitive inhibition model, with an inhibitory constant KI = 23 nM (18–28 95 % c.i.) of the sensor. These model parameters will be useful for forward modeling and design of T7 RNAP-based genetic circuits.

Emissions and Flame Stability Assessment of Hydrogen Addition and Air Dilution in a Micro Gas Turbine Combustor Using 0D/1D Modeling by Chemical Reactor Network

Abstract Hydrogen emerges as a promising fuel for clean and sustain- able electricity production when utilized in micro gas turbines (mGTs). However, some challenges linked to hydrogen usage must be overcome as its high reactivity can lead to increased NOx emissions and flame instabilities. Literature suggests that combustion air humidification and/or exhaust gas recirculation (EGR) may be methods to reduce this reactivity. However, these measures are limited due to the small operating range of the mGT combustor. In this context, a 20 kWth mGT combustor is investigated un- der various inlet conditions to assess the effect of EGR towards increased flame stability and emission control when operating under hydrogen-enriched methane firing, using a Chemical Re- action Network (CRN) model. The CRN modeling is a fast and low computational complexity tool to model combustion perfor- mance and emissions by representing complex reactive flow fields through idealized reactor models, leading to reduced computa- tional costs. This study examines partial load conditions, fuel compositions, and the effect of combustion air dilution through EGR. The CRN model of the combustion process is designed based on combustion zones extracted from the main flow fields with similar thermo-chemical states from CFD, while emission predictions are experimentally validated for different operating points. Predicted NOx and CO emissions by the proposed CRN model show an acceptable agreement with the experimental data at high power loads. However, its accuracy diminishes for loads below 70% due to the variability in model tuning parameters across different loads, whereas the

A Parametric Study on NOx Emissions From Ammonia Containing Product Gas in Rich–Quench–Lean Combustion

Abstract Due to climate change, there has been an increasing demand for fuels that can accelerate the transition away from fossil fuels to clean energy. Humidified product gas obtained from gasifying biomass is emerging as a promising candidate to replace natural gas, as it is composed of a gaseous mixture of hydrogen, steam, carbon monoxide, and methane. However, the gasification process releases ammonia and other nitrogen bearing compounds into the product gas, resulting in substantial increases in nitric oxides, NOx, in the exhaust. As such, there has been a recent push to understand the underlying chemical kinetics that drive NOx formation in order to optimize gas turbines to mitigate emissions at the source. In this study, a simplified chemical reactor network (CRN) model for a gas turbine rich–quench–lean (RQL) combustor was developed in cantera. The following parameters were investigated in this study: equivalence ratio of the primary section, overall equivalence ratio, steam dilution, postflame residence time, and recirculation from the postquench region to the primary section. Additionally, a benchmark CRN representing a lean burner (LB) is also developed. Results of the CRN model suggest that, when comparing to LB, a RQL type combustor delivers up to a 75% reduction in emissions. Additionally, it was found that, for both the LB and RQL combustors, an overall lean to stoichiometric equivalence ratio is well suited to reduce emissions in highly humidified fuels, while for moderately humidified fuels it is preferable to operate in an overall slightly rich equivalence ratio. The difference observed is mainly due to the fact that, at high humidification and lean conditions, the temperature is favorable for the conversion of ammonia to nitrogen, while, at moderate humidification and rich conditions, NO reacts with ammonia in the reburn process. Finally, it is suggested that the incorporation of recirculation from the secondary section to the primary section of the RQL burner results in a broader low emission region, due to more favorable conditions for ammonia conversion to nitrogen in the primary section.

A new low NOx emission technique for NH3/H2 blends in a flameless combustor through offset injection

The application of ammonia (NH3) as a possible future fuel presents a plausible solution for green energy storage. It helps provide a carbon-neutral fuel alternative for industrial power generation and transportation. However, the combustion of NH3 presents a formidable challenge due to its low reactivity, inadequate flame stability, sluggish flame propagation, and high NOx emissions. Consequently, its integration into combustion systems necessitates substantial system and strategy modification to enable its deployment to industrial systems. The current study presents a novel fuel/air injection technique, which emphasizes the high recirculation of hot combustion products and the extended residence time of fuel/air mixtures. A comprehensive experimental and numerical investigation is conducted using a swirl air injection and offset fuel injection to achieve the flameless combustion mode for optimized NH3/H2 fuel blends. A range of mixture conditions (ϕ = 0.5–1.2) and NH3/H2 compositions (50/50–70/30) are experimentally examined. The investigations helped elucidate the effect of residence time and recirculation on NOx emissions through kinetic simulations using a reactor network model. Subsequently, 3-D numerical simulations helped identify regions of high recirculation, quantified through reactant dilution ratios and uniform temperature distribution. These aspects are determined using a new parameter, the temperature uniformity index along the axial direction of the combustor. The emissions of NOx, unburnt NH3, and unburnt H2 are quantified for different equivalence ratios and NH3 mole fractions in the fuel mixture. The investigations reveal that NOx emissions reached their minimum (450–654 ppm) and (344-211 ppm), when the burner operated at lean (ϕ = 0.5–0.8) and rich (ϕ = 1.0–1.2) conditions, respectively, for 70/30 NH3/H2 blend. The emissions of unburnt NH3 and H2 species remain minimal for lean conditions. Both lean and rich operational regimes demonstrated similar or superior emission characteristics in flameless combustion mode when compared to the conventional combustion mode.

Roles of network topology in the relaxation dynamics of simple chemical reaction network models

Understanding the relationship between the structure of chemical reaction networks and their reaction dynamics is essential for unveiling the design principles of living organisms. However, while some network-structural features are known to relate to the steady-state characteristics of chemical reaction networks, mathematical frameworks describing the links between out-of-steady-state dynamics and network structure are still underdeveloped. Here, we characterize the out-of-steady-state behavior of a class of artificial chemical reaction networks consisting of the ligation and splitting reactions of polymers. Within this class, we examine minimal networks that can convert a given set of sources (e.g., nutrients) to a specified set of targets (e.g., biomass precursors). By exploring the dynamics of the models with a simple setup, we find three distinct types of relaxation dynamics after perturbation from a steady-state: exponential-, power-law-, and plateau-dominated. We computationally show that we can predict this out-of-steady-state dynamical behavior from just three features computed from the network’s stoichiometric matrix, namely, (1) the rank gap, determining the existence of a steady-state; (2) the left null-space, being related to conserved quantities in the dynamics; and (3) the stoichiometric cone, dictating the range of achievable chemical concentrations. We further demonstrate that these three quantities relates to the type of relaxation dynamics of combinations of our minimal networks, larger networks with many redundant pathways, and a real example of a metabolic network. The relationship between the topological features of reaction networks and the relaxation dynamics presented here are useful clues for understanding the design of metabolic reaction networks as well as industrially useful chemical production pathways.

Towards Low NOx Emissions Performance of A 65KW Recuperated Gas Turbine Operated on 100% Hydrogen

Abstract This work supports the development of a low NOx emission 65 kW natural gas turbine capable of operating on 100% hydrogen. This gas turbine has been demonstrated to operate from cold start to full load on up to 30% hydrogen mixed into natural gas with single digit ppm NOx emissions. To reach operation on 100% hydrogen, injectors specifically designed to 1) avoid challenges with flashback and 2) be field retrofittable were developed and tested. Successful operation of the engine from cold start to full load on 100% hydrogen was demonstrated. To support the development, a chemical reactor network (CRN) is used in conjunction with experimental injector mixing characterization. A strategy to account for variation in mixing performance was developed and utilized with the CRN to connect NOx emissions to the injector mixing performance. Measured fuel concentration profiles and the CRN model was used to infer the effects of mixing on NOx emission. The results illustrate how NOx emissions are directly influenced by local fuel rich regions found at the injector outlet. The CRN model can thus be used to screen injector designs and infer NOx performance and will be used to guide the development of injectors for hydrogen that can attain desired fuel distributions, concentrations, and velocities. The results affirm the direction needed to attain improved mixing and to operate at overall leaner conditions made possible by the stabilizing features inherent to hydrogen.

Study of NO and CO Formation Pathways in Jet Flames with CH4/H2 Fuel Blends

The existing natural gas transportation pipelines can withstand a hydrogen content of 0 to 50%, but further research is still needed on the pathways of NO and CO production under moderate or intense low oxygen dilution (MILD) combustion within this range of hydrogen blending. In this paper, we present a computational fluid dynamics (CFD) simulation of hydrogen-doped jet flame combustion in a jet in a hot coflow (JHC) burner. We conducted an in-depth study of the mechanisms by which NO and CO are produced at different locations within hydrogen-doped flames. Additionally, we established a chemical reaction network (CRN) model specifically for the JHC burner and calculated the detailed influence of hydrogen content on the mechanisms of NO and CO formation. The findings indicate that an increase in hydrogen content leads to an expansion of the main NO production region and a contraction of the main NO consumption region within the jet flame. This phenomenon is accompanied by a decline in the sub-reaction rates associated with both the prompt route and NO-reburning pathway via CHi=0–3 radicals, alongside an increase in N2O and thermal NO production rates. Consequently, this results in an overall enhancement of NO production and a reduction in NO consumption. In the context of MILD combustion, CO production primarily arises from the reduction of CO2 through the reaction CH2(S) + CO2 ⇔ CO + CH2O, the introduction of hydrogen into the system exerts an inhibitory effect on this reduction reaction while simultaneously enhancing the CO oxidation reaction, OH + CO ⇔ H + CO2, this dual influence ultimately results in a reduction of CO production.

Reaction network and kinetic modeling for the direct catalytic cracking of Arabian light crude oil to chemicals

We investigate the catalytic cracking of Arabian light crude oil into chemicals. A kinetic model has been established for an in-house developed catalyst using a micro-activity testing unit under a wide temperature range (525–650 °C) and various catalyst-to-oil ratios (0–4.14). Our conditions are adjusted to promote light olefins and propylene-to-ethylene ratio. We trained different reaction networks with the experimental data to calculate the kinetic parameters and, at the same time, select a statistically relevant model. The most representative model, with eight lumps and forward–backward first-order reactions, enables the acceptable prediction of the yield of chemicals (light olefins). The obtained kinetic parameters suggest that diesel and gasoline fractions are important intermediates in which forward reactions are preferred due to lower activation energy requirements and faster rate constant. The model was validated by reparametrization using Arabian extra light data.

Nonpremixed Approaches for Fuel Flexible, Low NOx Combustors in High-Efficiency Gas Turbines

Abstract Lean, premixed combustor designs have almost completely replaced non-premixed combustors for industrial gas turbine applications where NOx emissions are regulated. Nonetheless, these designs have also introduced turndown and fuel flexibility constraints and made combustion instabilities and flashback more problematic. Future gas turbine applications will require combustors to accommodate a range of alternative fuels, provide operational flexibility, and compete for services with a host of new technologies, including energy storage and fuel cells. The purpose of this paper is to propose nonpremixed, multi-stage designs for the next generation of high turndown, high fuel flexibility, low NOx combustion designs – referred to here as a Nonpremixed, Rich, Relaxation, Lean (NRRL) combustor. The key concept we explore is non-premixed combustion, followed by additional fuel mixing to locally fuel-rich conditions, a relaxation stage, and then a lean stage. This non-premixed approach can handle essentially any fuel composition, including pure hydrogen, liquid fuels, pure methane, pure ammonia, and any combination in between while breaking the NOx-CO tradeoff and reducing combustion instability risk. This paper provides chemical reactor network modeling calculations to identify key kinetic processes and time scales required for such a concept. This concept has completely inverted sensitivities from lean, premixed systems which prefer short residence times, low pressures, and low temperatures to minimize NO formation. This concept prefers long residence times, high pressures, and high temperatures, indicating a very different set of design trades for part load and off-design performance.

Theoretical and experimental constraints on hydrogen isotope equilibrium in C1-C5 alkanes

Stable isotope ratios of C1–C5 alkanes, the major constituents of subsurface gaseous hydrocarbons, can provide valuable insights on their origins, transport, and fates. Equilibrium isotope effects are fundamental to interpreting stable isotope signatures, as recognition of them in natural materials indicates reversible processes and constrains the temperatures of equilibrated systems. Hydrogen isotope equilibrium of C1–C5 alkanes is of particular interest because evidence shows that alkyl H can undergo isotopic exchange with coexisting compounds under subsurface conditions. We present the results of a combined experimental and theoretical effort to determine equilibrium hydrogen isotope distributions in mixtures of these hydrocarbon compounds. We created two mixtures: one with C1, C2 and C3 (where C1 indicates methane, C2 ethane, etc., hereinafter called ‘C1–C3 mixture’) and another one with C2, C3, iC4, nC4, iC5 and nC5 (hereinafter called ‘C2–C5 mixture’); in both cases, the mixtures were created to start out of hydrogen isotope equilibrium. We tested the performance of several metal catalysts as aids to H isotopic exchange by exposing these mixtures to different metal catalysts at 100 or 200 °C and analyzing the compound-specific hydrogen isotope ratios of the product gases. The C1–C3 mixture exchanged hydrogen isotopes among the starting gas components rapidly in the presence of Ru/Al2O3 at 200 °C. The isotope ratios reach a steady-state (presumably equilibrium) after 72 h of heating (up to 120 h). The hydrogen isotopic ratios of alkanes in the C2–C5 mixture shifted significantly in the presence of Rh/Al2O3 at 100 °C and C3-C5 compounds approached steady state after 70 h, whereas C2 had not yet reached a steady state D/H ratio after 216 h of heating. We evaluated the reaction progress of isotope exchange for each compound as a function of time with a reaction-network model informed by constraints on chemical kinetics. The model indicates that C3, iC4, nC4, iC5 and nC5 in the C2-C5 mixture reached internal isotope equilibrium after 70 h, hence we used their values to calculate experimental equilibrium results. We also calculated equilibrium isotope fractionations with the Bigeleisen-Mayer theorem using vibrational frequencies computed from the density functional theory (B3LYP/aug-cc-pVTZ). We included torsional conformers and explicit H positions in the calculations. We found that the experimental results from both the 200 ˚C C1–C3 experiment and 100 ˚C C2–C5 experiment are consistent with theoretical equilibrium results within experimental uncertainty. Additionally, our reaction-network model for catalyzed hydrogen isotope exchange between alkanes succeeded in fitting our experimental results. This modeling framework can be adjusted to simulate exchange in natural settings for constraining temperature histories and sources of natural gases.

Systems modeling of oncogenic G-protein and GPCR signaling reveals unexpected differences in downstream pathway activation

Mathematical models of biochemical reaction networks are an important and emerging tool for the study of cell signaling networks involved in disease processes. One promising potential application of such mathematical models is the study of how disease-causing mutations promote the signaling phenotype that contributes to the disease. It is commonly assumed that one must have a thorough characterization of the network readily available for mathematical modeling to be useful, but we hypothesized that mathematical modeling could be useful when there is incomplete knowledge and that it could be a tool for discovery that opens new areas for further exploration. In the present study, we first develop a mechanistic mathematical model of a G-protein coupled receptor signaling network that is mutated in almost all cases of uveal melanoma and use model-driven explorations to uncover and explore multiple new areas for investigating this disease. Modeling the two major, mutually-exclusive, oncogenic mutations (Gαq/11 and CysLT2R) revealed the potential for previously unknown qualitative differences between seemingly interchangeable disease-promoting mutations, and our experiments confirmed oncogenic CysLT2R was impaired at activating the FAK/YAP/TAZ pathway relative to Gαq/11. This led us to hypothesize that CYSLTR2 mutations in UM must co-occur with other mutations to activate FAK/YAP/TAZ signaling, and our bioinformatic analysis uncovers a role for co-occurring mutations involving the plexin/semaphorin pathway, which has been shown capable of activating this pathway. Overall, this work highlights the power of mechanism-based computational systems biology as a discovery tool that can leverage available information to open new research areas.

Stochastic filtering of reaction networks partially observed in time snapshots

Stochastic reaction network models arise in intracellular chemical reactions, epidemiological models and other population process models, and are a class of continuous time Markov chains which have the nonnegative integer lattice as state space. We consider the problem of estimating the conditional probability distribution of a stochastic reaction network given exact partial state observations in time snapshots. We propose a particle filtering method called the targeting method. Our approach takes into account that the reaction counts in between two observation snapshots satisfy linear constraints and also uses inhomogeneous Poisson processes as proposals for the reaction counts to facilitate exact interpolation. We provide rigorous analysis as well as numerical examples to illustrate our method and compare it with other alternatives.

Chemical reaction network analysis on N2O emissions control strategies of ammonia/ methanol Co-combustion

As a kind of substitute fuel of hydrocarbon fuel, ammonia has gained increasing attention for its potential to reduce carbon emissions. Blending with carbon-neutral methanol is expected to deal with the slow flame speed of ammonia. Nevertheless, the emission of N2O, a byproduct of ammonia combustion, would undermine the carbon reduction benefits due to its high global warming potential. This study employed Chemkin to construct a Chemical Reaction Network (CRN) model to analyze the reaction kinetics of ammonia combustion when blended with a small amount of methanol. It finds that the increases in the methanol blending ratio, pressure, and temperature can reduce greenhouse gas emissions effectively. Further analysis indicates that pressure and temperature have distinct effects on the emission mechanisms of N2O and NO. Increasing the combustion pressure weakens the chain reactions, thereby reducing the formation of both NO and N2O; the increase in initial temperature raises NO emissions, but also promotes the decomposition of N2O due to the abundance of H in the post-combustion phase. Optimizing post-combustion efficiency and controlling the formation of NO are crucial for N2O emission control. Thus, a staged methanol injection strategy is proposed, where early injection reduces NO emissions and late injection facilitates the decomposition of N2O.

Identifying Bayesian optimal experiments for uncertain biochemical pathway models

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

Effects of mRNA conformational switching on translational noise in gene circuits.

Intragenic translational heterogeneity describes the variation in translation at the level of transcripts for an individual gene. A factor that contributes to this source of variation is the mRNA structure. Both the composition of the thermodynamic ensemble, i.e., the stationary distribution of mRNA structures, and the switching dynamics between those play a role. The effect of the switching dynamics on intragenic translational heterogeneity remains poorly understood. We present a stochastic translation model that accounts for mRNA structure switching and is derived from a Markov model via approximate stochastic filtering. We assess the approximation on various timescales and provide a method to quantify how mRNA structure dynamics contributes to translational heterogeneity. With our approach, we allow quantitative information on mRNA switching from biophysical experiments or coarse-grain molecular dynamics simulations of mRNA structures to be included in gene regulatory chemical reaction network models without an increase in the number of species. Thereby, our model bridges a gap between mRNA structure kinetics and gene expression models, which we hope will further improve our understanding of gene regulatory networks and facilitate genetic circuit design.

Chemical Reactor Network modeling of ammonia–hydrogen combustion in a gas turbine: stochastic sensitivity analysis

To tackle climate change, we need to incorporate renewable energy sources on a large scale. One potential solution is the use of hydrogen, as an alternative energy vector that can be burnt in existing devices with little modification. Hydrogen can be produced from excess wind and solar power, ammonia, as a hydrogen carrier, is very appealing as it can be easily liquefied and distributed through the existing infrastructure. This study aims to explore the feasibility of using ammonia–hydrogen mixtures in industrial settings. This exploration is made by modeling a gas turbine system through a network of chemical reactors (CRN) and it is aimed at improving our understanding of the energy conversion process involving hydrogen, ammonia, and their mixtures. In this work, we analyze the most significant parameters of the CRN model, leveraging stochastic techniques. Specifically, we study the effects of the model and operational parameters on NOx emissions. This could represent a valuable aid in the development of CRN models capable of predicting emissions from gas turbine systems, to understand the impact of various operating conditions, such as pressure, temperature, equivalence ratio, and mixture composition.

Elevated temperature extraction of β-carotene from freeze-dried carrot powder into sunflower oil: Extraction kinetics and thermal stability.

β-Carotene, a precursor of vitamin A, can alleviate the deficiency of this vitamin prevalent worldwide. Earlier research studies have addressed the extraction of β-carotene at relatively low temperatures (up to 70°C) due to its perceived instability at higher temperatures, as a result of which extraction rates recorded are relatively low. This study models the net rate of β-carotene extraction by considering both extraction and degradation kinetics. The model developed, which accounts for degradation occurring in solid and extract phases, has been experimentally validated for the extraction of β-carotene from freeze-dried carrot powder into sunflower oil over a range of temperatures 90-150°C. This study also gives insights into the application of sunflower oil as a carrier for β-carotene during cooking and food processing, by monitoring and modeling the thermal degradation and isomerization of β-carotene at temperatures up to 220°C. The modeling of extraction kinetics shows that it is possible to achieve viable extraction rates by employing temperatures in the range (90-150°C) for relatively short times (<5min). The degradation kinetics shows that almost 75% of the β-carotene can survive heating at 180°C for 10min-indicating the possibility of using β-carotene enriched edible oils for frying. This study also reports on the formation of three isomers of β-carotene identified using HPLC: trans-, 9-cis, and 13-cis. The reaction network model developed in this study was able to account for the transient variation of the concentration of all three isomers. PRACTICAL APPLICATION: β-Carotene is a precursor of vitamin A and its consumption can potentially alleviate the deficiency of this vitamin prevalent worldwide. This study validates a model for the extraction of β-carotene in sunflower oil, which takes into account extraction as well as degradation occurring during extraction, so that a rational method is available for the design of efficient extractors for this purpose. This paper also establishes the thermal stability of β-carotene under frying conditions by quantifying its thermal degradation as well as isomerization.

Chemical reactor network modeling in the context of solid fuel combustion under oxy-fuel atmospheres

A multi-phase chemical reactor network (CRN) is developed and applied to investigate solid fuel combustion under oxy-fuel atmospheres in a laboratory-scale combustion chamber. The development of a novel solid-gas plug flow reactor (sPFR) allows the full coupling of solid and gas phase processes to increase the fidelity in CRN modeling. Together with the recently developed solid-gas perfectly stirred reactor (sPSR), the new sPFR is applied within a multi-phase reactor network to model the complex features of the investigated application. To design an initial reactor network, a hybrid experimental and computational fluid dynamics (CFD)-driven approach is being pursued, taking into account both global and local information on flow and temperature fields. Initially, an in-depth investigation of the physical processes is conducted, covering solid fuel conversion processes such as devolatilization and char conversion, along with the thermochemical characteristics of the flue gas. In particular, the prediction of carbon monoxide (CO) emissions is analyzed in detail by means of sensitivity analyses. Based on the findings of the sensitivity study, the network complexity of the initial reactor network is increased to enable an accurate prediction of the CO formation in comparison to the experiments. Finally, the formation of nitric oxides (NOx), sulfur oxides (SOx), and aromatic pollutants is discussed, highlighting the importance of detailed chemistry in solid fuel combustion at technically relevant scales.

Core-shell equivalent reactor network model to bridging CFD and process simulations of a fluidized bed reactor

Equivalent reactor network (ERN) is expected to narrow the gap between the computational fluid dynamics (CFD) and process simulations of a fluidized bed reactor (FBR). In view of this, a novel CFD-based core–shell ERN framework is proposed, where the core compartment is represented by a variable cross-section PFR (vPFR) and shell zones are simulated by CSTRs. The CFD simulation indicates that the gas phase flow in a propane dehydrogenation (PDH) FBR deviates significantly from the ideal plug-flow. The developed core–shell ERN is reliable to reproduce the non-ideal flow. With the process model integrating the core–shell ERN, the PDH reactor performance and process efficiency are evaluated. Process defects are found mainly in the heat exchange section because of the largest lost work (i.e., 45.34 %) and lowest second-law efficiency (i.e., 20.75 %). Overall, the core–shell ERN is capable of bridging CFD and process simulations of an FBR and detecting process weakness.