Radial basis function neural networks in prediction and modeling of diesel engine emissions operated for biodiesel blends under varying operating conditions

Manjunatha, R.; Narayana, P. Badari; Reddy, K. Hema Chandra; Reddy, K. Vijaya Kumar

doi:10.17485/ijst/2012/v5i3.14

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…RBF is another kind of learning algorithm method of ANNs, which has viewed the design of a NN as a curve-fitting problem in a high dimensional space [22]. Moreover, a RBF-based NN structure offers faster prediction than a conventional simulation program or mathematical technique [21]. A RBF-based NN structure includes 3 layers, the input, hidden, and output layers.…”

Section: Neural Network Structurementioning

confidence: 99%

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Prediction of emissions and exhaust temperature for direct injection diesel engine with emulsified fuel using ANN

Kökkülünk¹,

Akdoğan²,

Ayhan³

2013

Turk J Elec Eng & Comp Sci

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Exhaust gases have many effects on human beings and the environment. Therefore, they must be kept under control. The International Convention for the Prevention of Pollution from Ships (MARPOL), which is concerned with the prevention of marine pollution, limits the emissions according to the regulations. In Emission Control Area (ECA) regions, which are determined by MARPOL as ECAs, the emission rates should be controlled. Direct injection (DI) diesel engines are commonly used as a propulsion system on ships. The prediction and control of diesel engine emission rates is not an easy task in real time. Therefore, in this study, an artificial neural network (ANN) structure using the back propagation (BP) learning algorithm and radial basis function (RBF) has been developed to predict the emissions and exhaust temperature for DI diesel engines with emulsified fuel. In order to show the ANN performance, the network outputs and experimental results of the BP and RBF have been compared in this paper. The experimental results were obtained from a real diesel engine. The results showed that the emissions and exhaust temperature were estimated with a very high accuracy by means of the designed neural network structures and the RBF is more reliable than the BP.

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Section: Neural Network Structurementioning

confidence: 99%

“…Zhang and Tian predicted the CO, NO x , and smoke emissions in a dual fuel engine, fueled with coal water slurry (CWS)-diesel, using a RBF NN [18]. Wang [21].…”

Section: Introductionmentioning

confidence: 99%

Prediction of emissions and exhaust temperature for direct injection diesel engine with emulsified fuel using ANN

Kökkülünk¹,

Akdoğan²,

Ayhan³

2013

Turk J Elec Eng & Comp Sci

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show abstract

“…Du and Zhang also adopted the RBF network for modeling and prediction (Du and Zhang, 2008), in which genetic algorithm (GA) was used to optimize several parameters (the width and center of Gaussian kernel, the number of hidden layers) in the RBF network unlike Cowper et al (Cowper et al, 2002). Manjunatha et al adopted the RBF network for predicting diesel engine emissions, and concluded that the highly accurate prediction could be made in comparison with back propagation neural network (Manjunatha et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear prediction using radial basis function network incorporating coordinate transformation

Kitayama

Tamada

Kanno

2019

Mechanical Engineering Letters

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A nonlinear modeling and prediction method using radial basis function (RBF) network is developed in this paper, where the coordinate system of training data is transformed using the affine transformation. It is difficult to accurately predict the time series by conventional approach using the RBF network, in which the coordinate system is always fixed and this makes the prediction poor. For highly accurate prediction using the RBF network, the coordinate system is transformed using the affine transformation. The coordinate system of training data for the RBF network is transformed, and consequently it is expected that the highly accurate prediction can be made in comparison with the conventional approach. The proposed approach is numerically easy to implement with the computationally inexpensive procedure. The validation and characteristics of the proposed approach are examined through benchmarks.

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“…Initially, the network modifies the primary set of internal parameters to find the situation of “best fit.” Afterward, consequent outputs resulting from the desired input pattern are determined . In most cases the neural networks, which are non‐linear mathematical modeling instruments, are used to recognize patterns in data or model complicated relationships between input and output parameters with the use of optimization .…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of pollution and fuel consumption on a CI engine operated on alumina nanoparticles—Diesel fuel with the aid of artificial neural network

Saraee

Jafarmadar

Alizadeh-Haghighi

et al. 2015

Env Prog and Sustain Energy

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The purpose of this study is to analyze the performance, the pollutant emissions, and the neural network modeling of a compression ignition (CI) engine operating on nanoparticles-diesel fuel. Alumina nanoparticles that ranged in dosing level from 20 to 80 ppm were used as additives in diesel fuel along with the simultaneous usage of a 2% by volume surfactant. To achieve the uniform dispersion of AL 2 O 3 nanoparticles, an ultrasonic vibrator was used. The results indicate a perceptible effect on engine performance and a decrease in the specific fuel consumption as compared to similar procedures that used base fuels. In addition, it was found that the use of this fuel resulted in lower amounts of NO x , HC, and CO emissions than that of diesel fuel. A generalized regression artificial neural network (GRNN) model was developed to predict a correlation between brake power, fuel consumption, HC, CO, NO x using different amounts of nanoparticles and speeds as input data. Predictive ability of this neural network is investigated considering mean square error (MSE) and correlation coefficient (R) values. The predicted results of the model led to the MSE values of 9.6346 3 10 25 , 8.6470 3 10 24 , 0.0213, 0.0088, and 2.5836 3 10 24 for power, fuel consumption, HC, CO, and NO x , respectively . Also, the R values that were obtained for these outputs are: 0.99999 , 0.99912, 0.98506, 0.99977, and 0.9998, respectively.

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Radial basis function neural networks in prediction and modeling of diesel engine emissions operated for biodiesel blends under varying operating conditions

Cited by 9 publications

References 3 publications

Prediction of emissions and exhaust temperature for direct injection diesel engine with emulsified fuel using ANN

Prediction of emissions and exhaust temperature for direct injection diesel engine with emulsified fuel using ANN

Nonlinear prediction using radial basis function network incorporating coordinate transformation

Experimental investigation of pollution and fuel consumption on a CI engine operated on alumina nanoparticles—Diesel fuel with the aid of artificial neural network

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