Soft Computing Model for Prediction of EGR Effects on Particle Sizing at CR Diesel Engine Exhaust

Scafati, Ferdinando Tagliatatela; Cesario, N.; Lavorgna, Mario; Merola, Simona Silvia; Vaglieco, Bianca Maria

doi:10.4271/2007-24-0104

Cited by 4 publications

(9 citation statements)

References 4 publications

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“…This group uses part of the power of the exhaust gases to increase the pressure of fresh air coming into the engine; and therefore, its flow (see Fig. 1) [14,15,31]. This increase of air into the cylinders allows the combustion of more fuel, and so produces greater power and torque in the engine.…”

Section: Introductionmentioning

confidence: 99%

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Air management in a diesel engine using fuzzy control techniques

García-Nieto

Salcedo

Martínez

et al. 2009

Information Sciences

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Section: Introductionmentioning

confidence: 99%

“…This technique is termed exhaust gas recirculation (EGR), and is implemented by a valve that links the intake and exhaust manifolds [14]. The technique is based on the recirculation of gases with high specific heat coming from combustion, while inert gases reduce the combustion temperature; and therefore, the generation of NO x [33,31].…”

Section: Introductionmentioning

confidence: 99%

Air management in a diesel engine using fuzzy control techniques

García-Nieto

Salcedo

Martínez

et al. 2009

Information Sciences

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“…[24][25][26][27][28][29] The current work, in contrast with all these studies, focuses on the challenges of real-time opacity spike detection during the turbocharger lag period and the causative difficulties of estimating the volumetric efficiency and the cylinder-to-cylinder variation and proposes a simple solution for production diesel engines. PN research, transient or steady state, is a relatively new area of research and hence investigators [30][31][32][33][34][35][36][37][38][39][40] have primarily focused their efforts on measuring the transient particle size distributions, characterizing these distributions, and studying sensitivities. Liu et al [30][31][32] have shown how the transient engine parameters affect both the size and the concentration of emitted particles and how these are different from corresponding steady state emissions.…”

Section: Introductionmentioning

confidence: 99%

A decision-tree-based approach to smoke spike detection in a heavy-duty diesel engine

Brahma

2012

Proceedings of the Institution of Mechanical Engineers, Part D:

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Smoke spikes occurring during transient engine operation have detrimental health effects and increase fuel consumption by requiring more frequent regeneration of the diesel particulate filter. This paper proposes a decision tree approach to real-time detection of smoke spikes for control and on-board diagnostics purposes. A contemporary, electronically controlled heavy-duty diesel engine was used to investigate the deficiencies of smoke control based on the fuel-to-oxygenratio limit. With the aid of transient and steady state data analysis and empirical as well as dimensional modeling, it was shown that the fuel-to-oxygen ratio was not estimated correctly during the turbocharger lag period. This inaccuracy was attributed to the large manifold pressure ratios and low exhaust gas recirculation flows recorded during the turbocharger lag period, which meant that engine control module correlations for the exhaust gas recirculation flow and the volumetric efficiency had to be extrapolated. The engine control module correlations were based on steady state data and it was shown that, unless the turbocharger efficiency is artificially reduced, the large manifold pressure ratios observed during the turbocharger lag period cannot be achieved at steady state. Additionally, the cylinder-to-cylinder variation during this period were shown to be sufficiently significant to make the average fuel-to-oxygen ratio a poor predictor of the transient smoke emissions. The steady state data also showed higher smoke emissions with higher exhaust gas recirculation fractions at constant fuel-to-oxygen-ratio levels. This suggests that, even if the fuel-to-oxygen ratios were to be estimated accurately for each cylinder, they would still be ineffective as smoke limiters. A decision tree trained on snap throttle data and pruned with engineering knowledge was able to use the inaccurate engine control module estimates of the fuel-to-oxygen ratio together with information on the engine control module estimate of the exhaust gas recirculation fraction, the engine speed, and the manifold pressure ratio to predict 94% of all spikes occurring over the Federal Test Procedure cycle. The advantages of this non-parametric approach over other commonly used parametric empirical methods such as regression were described. An application of accurate smoke spike detection in which the injection pressure is increased at points with a high opacity to reduce the cumulative particulate matter emissions substantially with a minimum increase in the cumulative nitrogrn oxide emissions was illustrated with dimensional and empirical modeling.

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“…Their model was capable of predicting particle distribution with the absolute square mean error of 3-7%. Ample evidence could be found in the literature in relation to application of neural network for predicting the behaviours of diesel particulate filter [37], NOx and soot emissions in diesel engine [38], prediction of emission levels using cylinder pressure [39][40][41] from diesel engines, cylinder pressure, NOx and CO 2 from gasoline engine [42] and neural network for CI and SI engines for predicting mainly emissions [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50]. This is not an exhaustive list but a few very studies relevant to current work.…”

Section: Introductionmentioning

confidence: 99%

“…Real time prediction of particle distribution based on the engine operating conditions for diesel engine have been demonstrated by Scafati et al [36]. Scafati et al employed neural network to predict the particle distribution for the size range of 8 to 381 nm diameter as a function of engine speed, load and EGR for a diesel engine.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for nano-scale particulate matter distribution from gasoline direct injection engine

Reddy

Samuel

2017

Applied Thermal Engineering

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Predicting the amount of combustion generated nano-scale particulate matter (PM) emitted by gasoline direct injection (GDI) is a challenging task, but immensely useful for engine calibration engineers in order to meet the stringent emission legislation norms. The present work aimed to link the in-cylinder combustion with engine-out nano-scale PM for the size range of 23.7 to 1000 nm diameter. Neural network with a single hidden layer using first 8 principal components of cylinder pressure was employed for training and predicting the number of nano-scale PM number count. Using a systematic computational approach and comparing its results with experimental data this work demonstrates that machine-learning approach based on neural network is sufficient for predicting engine out nano-scale PM count as a function of engine load and speed.

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Soft Computing Model for Prediction of EGR Effects on Particle Sizing at CR Diesel Engine Exhaust

Cited by 4 publications

References 4 publications

Air management in a diesel engine using fuzzy control techniques

Air management in a diesel engine using fuzzy control techniques

A decision-tree-based approach to smoke spike detection in a heavy-duty diesel engine

Machine learning for nano-scale particulate matter distribution from gasoline direct injection engine

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