Developing a model to predict the start of combustion in HCCI engine using ANN-GA approach

Taghavi, M.; Gharehghani, Ayat; Bakhtiari-Nejad, Firooz; Mirsalim, Mojtaba

doi:10.1016/j.enconman.2019.05.015

Cited by 90 publications

(34 citation statements)

References 40 publications

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“…In the current study, ANN was applied to estimate the emission gases such as CO, CO 2 , NO x , HC, and smoke density for all blend samples. Feed forward backpropagation in combination with "TRAINLM" training function has been used as algorithm, and mean square error treated as performance function [11,12,[19][20][21][22][23][24][25].…”

Section: Analysis Using Annmentioning

confidence: 99%

“…Levenberg-Marquardt (Trainlm) training function has been practiced for error-free evaluation, in which mean square error decides the dereliction consequence of the network [20]. Most of the experimental investigations have been carried out on various engines with the application of ANN for optimizing the outcomes [21][22][23][24][25]. As time passes on, researchers may use various ceramic coatings to the combustion chamber, piston, etc., in order to improve the performance and reduce the emission with the enhancement of heat transfer rate in combustion reaction [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Analysis on Emissions and Performance of Ceramic Coated Diesel Engine Fueled with Novel Blends Using Artificial Intelligence

Kotteda¹,

Chekuri²,

raju³

et al. 2021

Advances in Materials Science and Engineering

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The exhaustive nature of petroleum products triggers the obstacles of scarcity, economic imbalance, and environmental depletion. It is difficult to avoid their usage all of a sudden and switch to clean electric prime movers. Under all these circumstances, the researchers may initiate their investigations on alternative fuels for preeminent solution. The present study covers the performance and emissions of a single cylinder, four-stroke, diesel engine fueled with Pongamia pinnata and Calophyllum inophyllum biodiesels added with n-butanol additive at various proportions. In this investigation, the piston has been coated with ceramic material with a thickness of 200 µm topcoat. The blends have been tested at 1500 rpm speed and rated compression ratio of 17.5 : 1 at various operating loads. A comparative result analysis has been made on the engine parameters operated by diesel and showed that mechanical efficiency gradually increases with a percentile increment of n-butanol in the blend. Moreover, emissions such as CO, CO2, NOx, and opacity were found to be reduced for the samples having high amount of n-butanol, whereas HC emissions slightly increased. In addition, all the exhaust gases have been predicted by using second-order polynomial equations generated and artificial Intelligence technique, and the comparative analysis has been made. It has been identified that ANN showed an average accuracy of prediction superior than regression analysis.

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Section: Analysis Using Annmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis on Emissions and Performance of Ceramic Coated Diesel Engine Fueled with Novel Blends Using Artificial Intelligence

Kotteda¹,

Chekuri²,

raju³

et al. 2021

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Predicting the start of combustion is very crucial for determining the engine characteristics of the HCCI engine. The model to detect the start of combustion was developed by Taghavi et al [ 138 ]. Three models opting for MLP, RBF, and NARX were compared.…”

Section: Modeling Of Internal Combustion Enginesmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.

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“…In fact, these three methods provide a more diluted mixture of fuels in higher homogeneity to have more lean and complete combustion [2]. Among these three methods, RCCI engines, which use two different fuels with two different reactivities, in addition to having a more diluted mixture, also have the possibility to have more complete combustion due to low reactivity fuel (LRF) [3][4][5]. In this type of engines, usually low reactivity fuel imports to the cylinder from the intake port and high reactivity fuel injects to the cylinder directly.…”

Section: Introductionmentioning

confidence: 99%

Numerical Investigation of an RCCI Engine Fueled with Natural Gas/Dimethyl-Ether in Various Injection Strategies

et al. 2021

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Reactivity control compression ignition engines illustrated suitable abilities in emission reduction beside high thermal efficiency. In this research, nine various direct fuel injection strategies were studied numerically: three cases with single injection strategy and six cases with split injection and different start of injection (SOI). In all simulated cases, equivalence ratio kept constant (i.e., 0.3). Among various strategies, single injection showed higher IMEP as a factor of efficiency with about 5.39 bar that occurred at SOI=60 before top dead center (bTDC), while lower efficiency was observed for split injection case with 50%-50% injections of fuel in each injection stage. Start of combustion (SOC), burn duration and CA50 as factors for combustion characteristics were affected with SOI changes. In single SOI strategies, more advanced injection caused more advanced SOC where there was about 1.3 CAD advancing from 40 to 80 bTDC injection. Spilt SOI showed more advanced SOC, which, also more advanced, was allocated to 50%-50%split injection strategy. There was also the same trend in CA50 changes during change in SOI. Burn duration variations were insignificant and all of them approximately close to 4.5 CAD. According to the emissions researched in this study (Nitrogen Oxides (NOx), monoxide carbon (CO) and unburned hydro carbons (UHC)), all of these pollutants are below euro six diesel standards. Contours of emissions show that there were appropriate SOI for each case study, which were 45 degree bTDC for single strategy, 48 degree bTDC for 80%-20% mass injection and 70 degree bTDC for 50%-50% cases.

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Developing a model to predict the start of combustion in HCCI engine using ANN-GA approach

Cited by 90 publications

References 40 publications

[Retracted] Analysis on Emissions and Performance of Ceramic Coated Diesel Engine Fueled with Novel Blends Using Artificial Intelligence

[Retracted] Analysis on Emissions and Performance of Ceramic Coated Diesel Engine Fueled with Novel Blends Using Artificial Intelligence

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Numerical Investigation of an RCCI Engine Fueled with Natural Gas/Dimethyl-Ether in Various Injection Strategies

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