Towards artificial intelligence based diesel engine performance control under varying operating conditions using support vector regression

Naradasu, Kumar Ravi; Jyothirmai, S.; Ramesh, R.

doi:10.2298/tsci120413218n

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Despite extensive research [50,51] carried out in this domain, it seems that more investigations are still needed in two main areas namely; modeling of engine performance under a wide range of operating conditions and development of an intelligent vehicle control system incorporating such a model to improve vehicle overall performance.…”

Section: Engine Controlmentioning

confidence: 99%

Artificial Intelligence in Diesel Engines

Koten,

Mostafa Namar

2024

Diesel Engines - Current Challenges and Future Perspectives

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The use of artificial intelligence in different parts of human life is becoming inevitable and it is expected that in the near future, the range of artificial intelligence applications will include all service, industrial, research, and educational activities. Providing a solution or performance enhancement in research and industrial activities, considering that these activities contain lots of dependent parameters with formulated/non-formulated correlations, is always a challenge for researchers. The recent approach of researchers in using statistical data and applying techniques based on artificial intelligence is a promising solution that provides the desired answers more quickly and accurately. The automobile industry and internal combustion engines have also benefited from the advantages of artificial intelligence in order to improve their performance and efficiency. Among the most important developments and achievements of this approach, we can mention real-time modeling, optimization and intelligent control, new fuel combinations, fault detection systems, and self-driving vehicles. Therefore, in this chapter, the recent research and industrial achievements of diesel engines due to the use of artificial intelligence techniques will be discussed.

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Section: Engine Controlmentioning

confidence: 99%

Artificial Intelligence in Diesel Engines

Koten,

Mostafa Namar

2024

Diesel Engines - Current Challenges and Future Perspectives

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“…Extensive combustion analysis of these blends and regression model was not reported. Some researchers were worked with regression model [8] and artificial neural networks for existing diesel engine using diesel [9,10], and diesel-biodiesel blends [11]. In this present investigation, biodiesel from pungamia methyl ester and ethanol were blended with diesel.…”

Section: Introductionmentioning

confidence: 97%

Experimental and regression analysis for multi cylinder diesel engine operated with hybrid fuel blends

Gopal¹,

Kavandappa-Goundar²,

Ramasamy³

et al. 2014

THERM SCI

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The purpose of this research work is to build a multiple linear regression model for the characteristics of multicylinder diesel engine using multicomponent blends (diesel-pungamia methyl ester-ethanol) as fuel. Nine blends were tested by varying diesel (100 to 10% by vol.), biodiesel (80 to 10% by vol.), and keeping ethanol as 10% constant. The brake thermal efficiency, smoke, oxides of nitrogen, carbon dioxide, maximum cylinder pressure, angle of maximum pressure, angle of 5% and 90% mass burning were predicted based on load, speed, diesel, and biodiesel percentage. To validate this regression model anoth26.506er multi component fuel comprising diesel-palm methyl ester-ethanol was used in same engine. Statistical analysis was carried out between predicted and experimental data for both fuel. The performance, emission, and combustion characteristics of multi cylinder diesel engine using similar fuel blends can be predicted without any expenses for experimentation.

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“…At earlier stage of the theory development, SVM has been considered not so successful method for solving practical problems, but recently it has proven that it can achieve same, or even better results comparing to the widely used NN [16]. The SVM model was successfully used so far to predict Diesel engine performance [17]. In [18] authors compared the SVM with various NN models for the prediction of the cooling load in the office building.…”

Section: Introductionmentioning

confidence: 99%

Support vector machine for the prediction of heating energy use

et al. 2018

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Prediction of a building energy use for heating is very important for adequate energy planning. In this paper the daily district heating use of one university campus was predicted using the support vector machine model. Support vector machine is the artificial intelligence method that has recently proved that it can achieve comparable, or even better prediction results than the much more used artificial neural networks. The proposed model was trained and tested on the real, measured data. The model accuracy was compared with the results of the previously published models (various neural networks and their ensembles) on the same database. The results showed that the support vector machine model can achieve better results than the individual neural networks, but also better than the conventional and multistage ensembles. It is expected that this theoretically well-known methodology finds wider application, especially in prediction tasks.

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Towards artificial intelligence based diesel engine performance control under varying operating conditions using support vector regression

Cited by 5 publications

References 13 publications

Artificial Intelligence in Diesel Engines

Artificial Intelligence in Diesel Engines

Experimental and regression analysis for multi cylinder diesel engine operated with hybrid fuel blends

Support vector machine for the prediction of heating energy use

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