Comparative analysis of artificial neural networks and dynamic models as virtual sensors

Yap, Wai Kean; Karri, Vishy

doi:10.1016/j.asoc.2012.08.022

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…The ANN approach has also been applied to internal combustion engines for performance modelling issues (gasoline [58], methanol [59] and ethanol-gasoline blend fuelled engines [60]) and emissions prediction [61] and control [62].…”

Section: Introductionmentioning

confidence: 99%

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

Rossi

Velázquez

2015

Energy

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Section: Introductionmentioning

confidence: 99%

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

Rossi

Velázquez

2015

Energy

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“…Recently, ANN techniques have often been used for modeling of biofuel-fueled engines. For instance, Yap and Karri [14] demonstrated the series ANN model as generic virtual power and emission sensors. Kökkülünk et al [15] estimated the emissions with a very high accuracy by means of the designed ANN structures.…”

Section: Introductionmentioning

confidence: 99%

Determining optimal artificial neural network training method in predicting the performance and emission parameters of a biodiesel-fueled diesel generator

Altun¹,

Ertuğrul²

2018

International Journal of Automotive Engineering and Technologies

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Artificial neural network (ANN) methods were employed and suggested in modeling the emissions and performance of a diesel generator fueled with waste cooking oil derived biodiesel during steady-state operation. These papers are generally built on determining optimal network structure, but the modelling accuracy of an ANN is also highly dependent on employed training method. In modeling, operating conditions and fuel blend ratio were used as the inputs while the performance and emission parameters were the outputs. The modeling results obtained by conventional ANNs that were trained by back propagation (BP) learning algorithm, radial basis function (RBF), and extreme learning machine (ELM) were compared with experimental results and each other. The accuracy of the estimations by ELM was above 95% for all the output parameters except for specific fuel consumption and thermal efficiency. Moreover, ELM performed better than BP and RBF with lower mean relative error (MRE) in case where the emissions were estimated. The ELM provided correlation coefficients of 0.987, 0.950 and 0.996 for unburned hydrocarbons (HCs), nitrogen oxides (NOx) and smoke opacity (SO), respectively, while for BP, they were 0.973, 0.818, 0.993, and for RBF, 0.975, 0.640 and 0.981. The most suitable training function for each emission and performance parameters of diesel generator was determined based on obtained accuracies.

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“…Usually, for training the above-mentioned models, least square estimation, principal component analysis (PCA), partial least square, and kernel PCA can be taken into account [42]. One of the major differences between the trainings of a soft sensor and a stationary soft model refers to the fact that, instead of a simple curve-fitting used for stationary systems, soft sensors should somehow capture the underlying dynamics of the system/plant to make sure that they work properly during the system's operation [42][43][44][45][46]. Therefore, the training of such models may include the consideration of incremental approaches, that is, the recursive version of the mentioned learning systems together with a block-wise moving time window which determines the horizon of the historical data used for the training [47].…”

mentioning

confidence: 99%

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Azad

Mozaffari

2015

Meccanica

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In this paper, an attempt is made to develop a bio-inspired soft sensor as a real-time identifier for online estimations of the engine-out hydrocarbon emission and exhaust gas temperature over the coldstart operation of an automotive engine. To date, a number of studies have been done to design soft models as offline identifiers for capturing the knowledge of a set of collected coldstart databases. The results indicated that bio-inspired computation can be of great use for the identification of coldstart phenomenon. It also can be a good alternative to classic approaches. However, bio-inspired soft sensors have not been used as online estimators for monitoring the behavior of coldstart phenomenon. This is while it is desirable to have a flexible, inexpensive online identifier which can be employed to detect the realtime behavior of the engine over coldstart periods.Here, an exhaustive numerical study is conducted to demonstrate the potentials of bio-inspired approaches for online measuring of engine-out hydrocarbon emission and exhaust gas temperature during the coldstart operation. To do so, a set of well-known bioinspired optimizers are combined with a time-delayed artificial neural network to design an evolvable soft sensor. The results clearly demonstrate the high potential of bio-inspired computation for handling the difficulties associated with the real-time identification of engine-out hydrocarbon emission and exhaust gas temperature.

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Comparative analysis of artificial neural networks and dynamic models as virtual sensors

Cited by 11 publications

References 25 publications

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

Determining optimal artificial neural network training method in predicting the performance and emission parameters of a biodiesel-fueled diesel generator

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

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