Particulate matter (soot) emissions from combustion processes have damaging health and environmental effects. Numerical techniques with varying levels of accuracy and computational time have been developed to model soot formation in flames. High-fidelity soot models come with a significant computational cost and as a result, accurate soot modelling becomes numerically prohibitive for simulations of industrial combustion devices. In the present study, an accurate and computationally inexpensive soot-estimating tool has been developed using a long short-term memory (LSTM) neural network. The LSTM network is used to estimate the soot volume fraction (fv) in a time-varying, laminar, ethylene/air coflow diffusion flame with 20 Hz periodic fluctuation on the fuel velocity and a 50% amplitude of modulation. The LSTM neural network is trained using data from CFD, where the network inputs are gas properties that are known to impact soot formation (such as temperature) and the network output is fv. The LSTM is shown to give accurate estimations of fv, achieving an average error (relative to CFD) in the peak fv of approximately 30% for the training data and 22% for the test data, all in a computational time that is orders-of-magnitude less than that of high-fidelity CFD modelling. The neural network approach shows great potential to be applied in industrial applications because it can accurately estimate the soot characteristics without the need to solve the soot-related terms and equations.
<p>Dimethyl ether (DME) is a non-toxic and <a href="https://www.sciencedirect.com/topics/engineering/renewable-fuel" target="_blank">renewable fuel</a> known for its soot emissions reduction tendencies. In laminar co-flow DME <a href="https://www.sciencedirect.com/topics/engineering/diffusion-flame" target="_blank">diffusion flames</a>, adding oxygen to the fuel stream increases the sooting tendency until a critical point is reached, at which point the trend suddenly reverses. This work unravels the mechanisms behind this reversal process, and characterizes their contribution to controlling soot production. A series of experimental measurements using diffuse-light line-of-sight attenuation and two-colour pyrometry were performed to measure soot volume fraction and soot temperature considering a fixed <a href="https://www.sciencedirect.com/topics/engineering/mass-flowrate" target="_blank">mass flow rate</a> of DME and variable addition of oxygen. Soot volume fraction increases from 0.095 ppm in the pure DME flame to 0.32 ppm when the added oxygen concentration reaches 33%. When the oxygen concentration is slightly increased to 35%, soot volume fraction is reduced by 60%. To explain the reasons behind the reversal, a series of numerical simulations were performed, which successfully demonstrated the same trend. Results show that the chemical effects of adding oxygen to the fuel stream are exceedingly more important than the thermal and dilution effects. It was found that the reversal occurred when nearly all DME disassociated before exiting the fuel tube, indicating a sudden transition from a partially premixed DME flame, to one which primarily burns C1 fuel fragments. An analysis of soot formation and <a href="https://www.sciencedirect.com/topics/chemical-engineering/oxidation-reaction" target="_blank">oxidation</a> rates showed that near the reversal, soot inception is the least affected process; furthermore, soot precursor availability is not significantly affected in magnitude, rather they appear further upstream. It is concluded that the favourable conditions for rapid DME decomposition into soot precursors enhances soot inception while depleting the necessary species for further soot mass growth, dramatically reducing soot concentration.</p>
<p>A diverse range of polycyclic <a href="https://www.sciencedirect.com/topics/chemical-engineering/aromatic-compound" target="_blank">aromatic compounds</a> (PACs) is thought to exist in flame environments before and during soot inception. This work seeks to develop a machine learning (ML)-based soot inception model that considers detailed and diverse PAC properties such as <a href="https://www.sciencedirect.com/topics/chemical-engineering/oxygenation" target="_blank">oxygenation</a>, aliphatic content, radical character, size, and shape. To this end, temporal rates of change of PAC properties were computed by the stochastic modelling code SNapS2 and used as input to an ML model that predicts soot inception rate. The model is trained using experimentally-derived soot inception rates for three atmospheric pressure laminar premixed ethylene/air flames. An ML model (kernel ridge regression with a linear kernel) was developed to predict the soot inception rate in the three <a href="https://www.sciencedirect.com/topics/engineering/premixed-flame" target="_blank">premixed flames</a>. The soot inception rate predictions from this SNapS2-informed ML model outperformed the predictions from both the advanced soot <a href="https://www.sciencedirect.com/topics/engineering/computational-fluid-dynamic-modeling" target="_blank">modelling CFD</a> code CoFlame and an ML model which used CFD-determined inputs (temperature and species concentrations). The final model had an R^2 value of approximately 0.71 and a <a href="https://www.sciencedirect.com/topics/engineering/mean-absolute-error" target="_blank">mean absolute error</a> approximately 25% of the target values. The performance of the SNapS2-informed model suggests that detailed PAC properties are important to consider in inception modelling. While expanding this approach to other types of flames and fuels is crucial for future improvement to the model’s accuracy and generality, this methodology provides a successful framework for the current system. The success of this method demonstrates that ML can offer improvements in accuracy compared to current <a href="https://www.sciencedirect.com/topics/engineering/computational-fluid-dynamics" target="_blank">CFD</a> inception models and the highlights the potential for ML in soot predictions.</p>
<p>A diverse range of polycyclic <a href="https://www.sciencedirect.com/topics/chemical-engineering/aromatic-compound" target="_blank">aromatic compounds</a> (PACs) is thought to exist in flame environments before and during soot inception. This work seeks to develop a machine learning (ML)-based soot inception model that considers detailed and diverse PAC properties such as <a href="https://www.sciencedirect.com/topics/chemical-engineering/oxygenation" target="_blank">oxygenation</a>, aliphatic content, radical character, size, and shape. To this end, temporal rates of change of PAC properties were computed by the stochastic modelling code SNapS2 and used as input to an ML model that predicts soot inception rate. The model is trained using experimentally-derived soot inception rates for three atmospheric pressure laminar premixed ethylene/air flames. An ML model (kernel ridge regression with a linear kernel) was developed to predict the soot inception rate in the three <a href="https://www.sciencedirect.com/topics/engineering/premixed-flame" target="_blank">premixed flames</a>. The soot inception rate predictions from this SNapS2-informed ML model outperformed the predictions from both the advanced soot <a href="https://www.sciencedirect.com/topics/engineering/computational-fluid-dynamic-modeling" target="_blank">modelling CFD</a> code CoFlame and an ML model which used CFD-determined inputs (temperature and species concentrations). The final model had an R^2 value of approximately 0.71 and a <a href="https://www.sciencedirect.com/topics/engineering/mean-absolute-error" target="_blank">mean absolute error</a> approximately 25% of the target values. The performance of the SNapS2-informed model suggests that detailed PAC properties are important to consider in inception modelling. While expanding this approach to other types of flames and fuels is crucial for future improvement to the model’s accuracy and generality, this methodology provides a successful framework for the current system. The success of this method demonstrates that ML can offer improvements in accuracy compared to current <a href="https://www.sciencedirect.com/topics/engineering/computational-fluid-dynamics" target="_blank">CFD</a> inception models and the highlights the potential for ML in soot predictions.</p>
<p>Dimethyl ether (DME) is a non-toxic and <a href="https://www.sciencedirect.com/topics/engineering/renewable-fuel" target="_blank">renewable fuel</a> known for its soot emissions reduction tendencies. In laminar co-flow DME <a href="https://www.sciencedirect.com/topics/engineering/diffusion-flame" target="_blank">diffusion flames</a>, adding oxygen to the fuel stream increases the sooting tendency until a critical point is reached, at which point the trend suddenly reverses. This work unravels the mechanisms behind this reversal process, and characterizes their contribution to controlling soot production. A series of experimental measurements using diffuse-light line-of-sight attenuation and two-colour pyrometry were performed to measure soot volume fraction and soot temperature considering a fixed <a href="https://www.sciencedirect.com/topics/engineering/mass-flowrate" target="_blank">mass flow rate</a> of DME and variable addition of oxygen. Soot volume fraction increases from 0.095 ppm in the pure DME flame to 0.32 ppm when the added oxygen concentration reaches 33%. When the oxygen concentration is slightly increased to 35%, soot volume fraction is reduced by 60%. To explain the reasons behind the reversal, a series of numerical simulations were performed, which successfully demonstrated the same trend. Results show that the chemical effects of adding oxygen to the fuel stream are exceedingly more important than the thermal and dilution effects. It was found that the reversal occurred when nearly all DME disassociated before exiting the fuel tube, indicating a sudden transition from a partially premixed DME flame, to one which primarily burns C1 fuel fragments. An analysis of soot formation and <a href="https://www.sciencedirect.com/topics/chemical-engineering/oxidation-reaction" target="_blank">oxidation</a> rates showed that near the reversal, soot inception is the least affected process; furthermore, soot precursor availability is not significantly affected in magnitude, rather they appear further upstream. It is concluded that the favourable conditions for rapid DME decomposition into soot precursors enhances soot inception while depleting the necessary species for further soot mass growth, dramatically reducing soot concentration.</p>
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