Modeling and simulation of injection control system on a four-stroke type diesel engine development platform using artificial neural networks

Yılmaz, Serhat; Bılgın, Mehmet Zeki

doi:10.1007/s00521-012-1054-7

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…The weights (specified by the vector or alternatively by the matrices and ) are the adjustable parameters of the network and are determined starting from the set of examples during the training process [49]. The examples, or training data, constitute the set of inputs ( ( )) and the corresponding outputs ( ( )).…”

Section: Obtaining An Approximate Model Through Experimental Identificationmentioning

confidence: 99%

Post-combustion artificial neural network modeling of nickel-producing multiple hearth furnace

Góngora

Caneghem

Haeseldonckx

et al. 2020

International Journal of Chemical Reactor Engineering

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In a nickel-producing multiple hearth furnace, there is a problem associated to the automatic operation of the temperature control loops in two of the hearths, since the same flow of air is split into two branches. A neural model of the post-combustion sub-process is built and served to increase the process efficiency of the industrial furnace. Data was taken for a three-months operating time period to identify the main variables characterizing the process and a model of multilayer perceptron type is built. For the validation of this model, process data from a four-months operating time period in 2018 was used and prediction errors based on a measure of closeness in terms of a mean square error criterion measured through its weights for the temperature of two of the hearths (four and six) versus the air flow to these hearths. Based on a rigorous testing and analysis of the process, the model is capable of predicting the temperature of hearth four and six with errors of 0.6 and 0.3 °C, respectively. In addition, the emissions by high concentration of carbon monoxide in the exhaust gases are reduced, thus contributing to the health of the ecosystem.

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Section: Obtaining An Approximate Model Through Experimental Identificationmentioning

confidence: 99%

Post-combustion artificial neural network modeling of nickel-producing multiple hearth furnace

Góngora

Caneghem

Haeseldonckx

et al. 2020

International Journal of Chemical Reactor Engineering

View full text Add to dashboard Cite

show abstract

“…Different variables can be predicted by using the data obtained in previously conducted experiments. In recent years, the applicability of an artificial neural network method for internal combustion engines has gained considerable success (Sharkey et al, 2000;Yilmaz and Bilgin 2013).…”

Section: Introductionmentioning

confidence: 99%

Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network

Aydoğan

2017

J. energy South. Afr.

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The changes in the performance, emission and combustion characteristics of bioethanol-safflower biodiesel and diesel fuel blends used in a common rail diesel engine were investigated in this experimental study. E20B20D60 (20% bioethanol, 20% biodiesel, 60% diesel fuel by volume), E30B20D50, E50B20D30 and diesel fuel (D) were used as fuel. Engine power, torque, brake specific fuel consumption, NOx and cylinder inner pressure values were measured during the experiment. With the help of the obtained experimental data, an artificial neural network was created in MATLAB 2013a software by using back-propagation algorithm. Using the experimental data, predictions were made in the created artificial neural network. As a result of the study, the correlation coefficient was found as 0.98. In conclusion, it was seen that artificial neural networks approach could be used for predicting performance and emission values in internal combustion engines.

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“…In [12], decision tree (DT) has been used as an AI-based tool for developing an online preventive control. The artificial neural network (ANN) has been proposed in [13,14,[27][28][29] as an efficient tool for the preventive control strategy development.…”

Section: Introductionmentioning

confidence: 99%

State of charge preventive control of storage device to stabilize VSI-based microgrid after islanding occurrence using ANN-based control

Pamsari

SeyedShenava

Shayeghi

2014

Neural Comput & Applic

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This paper presents an effective method to control the state of charge (SOC) of energy storage system (ESS) installed in a microgrid (MG). Considering voltage and frequency variations after islanding occurrence and based on the stability criteria, MG pre-islanding conditions are divided into secure and insecure classes. It is shown that insecure MG can become secure, if appropriate value for SOC of the ESS is chosen in different conditions of the MG. To select the most important variables of MG, which can estimate proper values of the SOC, a feature selection procedure known as RELIEF is used in this paper. Among all the MG variables, critical ones are selected. Using them, appropriate value of the SOC can be calculated for different conditions of the MG. This approach is economic because it does not change the result of optimal power flow (OPF). In some conditions, the proper control of SOC cannot solely make the MG secure. In these conditions, DGs generated power should be also changed to make the MG secure. This condition leads to a violation from OPF solution and increases the cost of operation. The results demonstrate the effectiveness of the proposed method in comparison with other methods.

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Modeling and simulation of injection control system on a four-stroke type diesel engine development platform using artificial neural networks

Cited by 9 publications

References 19 publications

Post-combustion artificial neural network modeling of nickel-producing multiple hearth furnace

Post-combustion artificial neural network modeling of nickel-producing multiple hearth furnace

Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network

State of charge preventive control of storage device to stabilize VSI-based microgrid after islanding occurrence using ANN-based control

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