Estimating Soot Emission in Diesel Engines Using Gated Recurrent Unit Networks

Alcan, Gökhan; Yılmaz, Emre; Ünel, Mustafa; Aran, Volkan; Yılmaz, Metin; Gurel, Cetin; Köprübasi, Kerem

doi:10.1016/j.ifacol.2019.09.086

Cited by 20 publications

(7 citation statements)

References 22 publications

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“…The extra gates and a more complex internal mechanism allow the LSTM cell to manage a memory of the past events at both short-and long-term scales (in addition to the hidden state h t , a cell-state stream of information C t carries longterm memories and the combination of the gates let the cell keep or forget all or a part of the memory). Finally, the Gated Recurrent Units (GRU) are a more recent type of RNN cell structures [62,63]. The RNN architectures founded upon GRU cells have less parameters to be trained; hence, the model will become less computationally expensive both at the training and the execution stages compared to LSTM.…”

Section: Vehicle-specific Rnn Modelingmentioning

confidence: 99%

A Mixed Ensemble Learning and Time-Series Methodology for Category-Specific Vehicular Energy and Emissions Modeling

Moradi

Miranda-Moreno

2022

Sustainability

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The serially-correlated nature of engine operation is overlooked in the vehicular fuel and emission modeling literature. Furthermore, enabling the calibration and use of time-series models for instrument-independent eco-driving applications requires reliable forecast aggregation procedures. To this end, an ensemble time-series machine-learning methodology is developed using data collected through extensive field experiments on a fleet of 35 vehicles. Among other results, it is found that Long Short-Term Memory (LSTM) architecture is the best fit for capturing the dynamic and lagged effects of speed, acceleration, and grade on fuel and emission rates. The developed vehicle-specific ensembles outperformed state-of-the-practice benchmark models by a significant margin and the category-specific models outscored the vehicle-specific sub-models by an average margin of 6%. The results qualify the developed ensembles to work as representatives for vehicle categories and allows them to be utilized in both eco-driving services as well as environmental assessment modules.

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Section: Vehicle-specific Rnn Modelingmentioning

confidence: 99%

A Mixed Ensemble Learning and Time-Series Methodology for Category-Specific Vehicular Energy and Emissions Modeling

Moradi

Miranda-Moreno

2022

Sustainability

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“…These correlations can be used then to identify or estimate the critical aspects of the process under consideration (e.g., by performing feature selection). A number of studies have successfully applied ML algorithms to predict soot output in engines and burners [29,30,31], to assist with experimental soot measurement procedures [32,33], and to classify soot in TEM images [34]. A pair of recent studies by Dworkin and co-workers [35,36] used artificial neural networks (ANN), a class of ML algorithms, to predict the soot volume fraction (f v ) in laminar coflow diffusion flames.…”

Section: Introductionmentioning

confidence: 99%

Exploring soot inception rate with stochastic modelling and machine learning

Liddo

Saldinger

Jadidi

et al. 2023

Combustion and Flame

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“…These works, and a selection of others (e.g., [21][22][23][24][25][26][27]), show that an ANN is a versatile tool that can be used to reduce computational time at the expense of a minor decrease in accuracy (compared to traditional tools). As such, a handful of recent works have applied ANNs to the field of soot estimation [17,[28][29][30][31]. The work of Talebi-Moghaddam et al [28] used an ANN to efficiently and accurately estimate the light scattering kernel that is used in experiments to infer the morphological characteristics of soot particles.…”

Section: Introductionmentioning

confidence: 99%

“…The work of Talebi-Moghaddam et al [28] used an ANN to efficiently and accurately estimate the light scattering kernel that is used in experiments to infer the morphological characteristics of soot particles. Furthermore, a handful of studies [29][30][31] have used ANNs to couple engine operational parameters such as crank angle or fuel consumption to the soot emissions from engines. In [29], a shallow (1 hidden layer) multilayer perceptron ANN was used to predict the soot, CO 2 , and NO x emissions from a direct injection diesel engine.…”

Section: Introductionmentioning

confidence: 99%

A Long Short-Term Memory Neural Network for the Low-Cost Prediction of Soot Concentration in a Time-Dependent Flame

Jadidi¹,

Liddo²,

Dworkin³

2021

Energies

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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.

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Estimating Soot Emission in Diesel Engines Using Gated Recurrent Unit Networks

Cited by 20 publications

References 22 publications

A Mixed Ensemble Learning and Time-Series Methodology for Category-Specific Vehicular Energy and Emissions Modeling

A Mixed Ensemble Learning and Time-Series Methodology for Category-Specific Vehicular Energy and Emissions Modeling

Exploring soot inception rate with stochastic modelling and machine learning

A Long Short-Term Memory Neural Network for the Low-Cost Prediction of Soot Concentration in a Time-Dependent Flame

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