Chemical Kinetics in Combustion and Reactive Flows

Naoumov, Viatcheslav; Krioukov, V. G.; Abdullin, A. L.; Демин, А. В.

doi:10.1017/9781108581714

Cited by 13 publications

(3 citation statements)

References 166 publications

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“…The algorithm based on the representation of the condensed phase as conditional particles with a high number (10 3 or 10 4 ) of carbon atoms is used to calculate the chemical and phase equilibrium of heterogeneous mixtures [14].…”

Section: Methodsmentioning

confidence: 99%

Thermo-chemical equilibrium modeling and simulation of biomass gasification

2020

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Here, we present the results of numerical studies of biomass gasification using poultry litter, sewage sludge and wood waste (pine shavings) as examples of starting materials. The aim of the study was to find ways to increase the degree of biomass conversion to combustible gaseous products (CO, H2, CnHm) and to achieve high calorific value of the generated synthesis gas. Modeling biomass gasification was performed for a multicomponent reacting system in a state of thermodynamic and chemical equilibrium. The mathematical model and the calculation program created by the authors were used. The presence of a condensed phase in the form of fine particles of solid carbon and ash in biomass gasification products was taken into account. The optimal levels of gasification temperatures and conditions that help minimizing the concentration of solid carbon particles in gasification products were determined. For optimal biomass gasification, we recommend using the thermal energy obtained from burning part of the generated syngas.

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Section: Methodsmentioning

confidence: 99%

Thermo-chemical equilibrium modeling and simulation of biomass gasification

2020

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“…Reactive-transport equations arise in a wide range of scientific domains like epidemiology [53], combustion [41], hypersonic flows [26,45,46,56], subsurface energy [55], contamination remediation [9], air and water quality research [58], and population dynamics [42]. Although the underlying mathematical models in these applications are similar, the associated spatial and temporal scales are much different.…”

Section: Introductionmentioning

confidence: 99%

“…Reactive-transport equations arise in a wide range of scientific domains like epidemiology [Viguerie et al, 2020], combustion [Naoumov et al, 2019], hypersonic flows [Huangwei et al, 2020;Ren et al, 2018;Sabelnikov and Vlasenko, 2018;Xiaofeng et al, 2020], subsurface energy [Xiao et al, 2018], contamination remediation [Arbogas et al, 1996], air and water quality research [Zhu and Anderson, 2002], and population dynamics [Petrovskii and Li, 2003]. Although the underlying mathematical models in these applications are similar, the associated spatial and temporal scales are much different.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Jagtap¹,

Mudunuru²,

Nakshatrala³

2022

CICP

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Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework-placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction (≈ O(10 −6 )) of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach-a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media-will be a valuable tool for modeling reactive-transport in a wide range of applications.

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Evaluating the thermal stability of chemicals and systems: A review

Andriani,

Pio,

Salzano

et al. 2024

Can J Chem Eng

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In the realm of chemical processing, particularly at the industrial scale, safety is of utmost importance. A predominant factor causing accidents within the chemical industry is runaway phenomena, primarily initiated by uncontrolled exothermic reactions. This review critically examines the often‐overlooked decomposition mechanisms as a significant contributor to thermal energy release, necessitating a comprehensive revision and understanding of both experimental and theoretical strategies for assessing thermal degradation. Key to this discourse is the explication of calorimetry as the principal experimental technique, alongside ab initio quantum chemistry simulations as a robust theoretical framework for quantifying the most relevant properties. However, more than mere cognisance of these methodologies is required for a meticulous thermal stability assessment. The review emphasizes identifying and quantifying fundamental parameters through experimental and theoretical investigations. Only upon acquiring these parameters, including kinetic, thermodynamic, onset, and peak characteristics of the exothermic decomposition reactions, can one effectively mitigate risks and hazards in designing and optimizing chemical processes and apparatus. Furthermore, this review delineates qualitative and quantitative methodologies for hazard assessment, proffering strategies for estimating safe operational conditions and sizing relief devices. The paper culminates in exploring future trajectories in thermal stability assessments, focusing on emerging applications in lithium‐ion batteries, electrolyzers, electrified reactors, ionic liquids, artificial intelligence and machine learning approaches. Thus, the paper underlines the evolving landscape of thermal risk management in contemporary and future chemical industries.

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Chemical Kinetics in Combustion and Reactive Flows

Cited by 13 publications

References 166 publications

Thermo-chemical equilibrium modeling and simulation of biomass gasification

Thermo-chemical equilibrium modeling and simulation of biomass gasification

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Evaluating the thermal stability of chemicals and systems: A review

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