Artificial Neural Networks Approach for the Prediction of Thermal Balance of SI Engine Using Ethanol-Gasoline Blends

Kiani, Mostafa Kiani Deh; Ghobadian, Barat; Ommi, Fathollah; Najafi, G.; Yusaf, Talal

doi:10.1007/978-3-642-32498-7_3

Cited by 11 publications

(3 citation statements)

References 23 publications

(26 reference statements)

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“…ANNs can be defined as computational models which are inspired and generated by the human brain's neural network structure [73]. They comprise of interconnected neurons (artificial) that can process and, hence, transmit information.…”

Section: Artificial Neural Network-based Predictionmentioning

confidence: 99%

Core Challenges and Prospects of Methanol Utilization, Prediction and Optimization for Sustainable Environment

Usman,

Kashif Jamil,

Hanif

et al. 2023

Methanol Fuel in Transportation Sector and Fuel Cells

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The transportation sector stands at the forefront of global challenges, where its significant contribution to greenhouse gas emissions and air pollution has become an urgent matter demanding immediate attention. For addressing these compelling concerns and leading the automotive industry toward a sustainable future, it is, therefore, imperative to explore the realm of alternative fuel that can effectively mitigate the environmental impact of automobiles. Methanol, a renewable alternative fuel, has gathered quite an attention due to its potential to be used as a wonderful alternative to neat gasoline in spark ignition engines. However, there are some core challenges that must be addressed to utilize methanol on a commercial scale in the transport sector. These core challenges include cold start issues, enhanced NOx emissions, 100% methanol utilization, transportation concerns and lubricant oil deterioration. In this chapter, these challenges along with their potential solutions have been discussed in detail. Moreover, different techniques such as artificial neural network and response surface methodology have been discussed to predict and optimize the usage of methanol in SI engines. The adoption of methanol, as an alternative to gasoline, will help us achieve some important sustainable development goals, thus fulfilling the promise of a sustainable future for the upcoming world.

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Section: Artificial Neural Network-based Predictionmentioning

confidence: 99%

Core Challenges and Prospects of Methanol Utilization, Prediction and Optimization for Sustainable Environment

Usman,

Kashif Jamil,

Hanif

et al. 2023

Methanol Fuel in Transportation Sector and Fuel Cells

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show abstract

“…One alternative fuel that has been widely used in compression ignition (CI) engines is biodiesel [1][2][3][4]. Some researchers have analyzed the first law of thermodynamics (thermal balance) at different operating conditions, especially by using biodiesel fuel [5][6][7][8][9]. They have showed that the first low is inadequate to investigate the thermodynamic details of engines.…”

Section: Introductionmentioning

confidence: 99%

The Differential Impact of Various Injection Pressures on the Exergy of a Diesel Engine Using Biodiesel-Diesel Fuel Blends

et al. 2021

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Contrary to energy, exergy may be destroyed due to irreversibility. Exergy analysis can be used to reveal the location, and amount of energy losses of engines. Despite the importance of the exergy analysis, there is a lack of information in this area, especially when the engine is fueled with biodiesel–diesel fuel blends under various injection operating parameters. Thus, in this research, the exergy analysis of a direct-injection diesel engine using biodiesel–diesel fuel blends was performed. The fuel blends (B0, B20, B40, and B100) were injected into cylinders at pressures of 200 and 215 bars. Moreover, the simulation of exergy and energy analyses was done by homemade code. The simulation model was verified by compression of experimental and simulation in-cylinder pressure data. The results showed there was good agreement between simulation data and experimental ones. Results indicated that the highest level of in-cylinder pressure at injection pressure of 215 bars is more than that of 200 bars. Moreover, by increasing the percentage of biodiesel, the heat transfer exergy, irreversibility, burnt fuel, and exergy indicator decreased, but the ratio of these exergy parameters (except for heat transfer exergy) to fuel exergy increased. These ratios increased from 46 to 50.54% for work transfer exergy, 16.57 to 17.97% for irreversibility, and decreased from 16 to 15.49% for heat transfer exergy. In addition, these ratios at 215 bars are higher than at 200 bars for all fuels. However, with increasing the injection pressure and biodiesel concentration in fuel blends, the exergy and energy efficiencies increased.

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“…reliable, particularly in case of failure of numerical and mathematical methods. Mostafa Kiani Deh Kiani et al, 3 developed an ANN model with BP algorithm to predict Spark Ignition (SI) engine thermal balance under different engine speeds, loads and ethanol gasoline blends. The correlation coefficient of 0.997, 0.998, 0.996 and 0.992 were observed for work utilized, heat lost through the engine exhaust, heat lost through engine cooling water and unaccounted losses respectively.…”

mentioning

confidence: 99%

Efficient ELM Model With Parameter Optimization Using Pso Algorithms in the Prediction of Combustion Pressure Parameters of Dsi Engine Using Ethanol- Gasoline Blends

Shetty,

Hampali

2023

jmmf

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The present study focused mainly on developing PSO based ELM model to predict cylinder pressure associated parameters. Performance of PSO-ELM model then compared with ELM model to obtain its credential. For training and testing the models, data has been acquired through experiments on a Twin Spark Ignition (TSI) gasoline engine using EGB as fuel. The various operating variables are treated as input data whereas cylinder pressure associated parameters are treated as output data for the model. The result of the proposed modelling study indicated that PSO-ELM model has obtained the best performance with lowest value of MSE, MAPE (%) and hidden layer size as compared to ELM model. Hence PSO-ELM results in an efficient model structure with great generalization performance. Further, it is also observed that PSO-ELM takes more time as it calls for an iterative procedure for searching the optimal solution as compared to ELM, which takes only a single epoch.

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Artificial Neural Networks Approach for the Prediction of Thermal Balance of SI Engine Using Ethanol-Gasoline Blends

Cited by 11 publications

References 23 publications

Core Challenges and Prospects of Methanol Utilization, Prediction and Optimization for Sustainable Environment

Core Challenges and Prospects of Methanol Utilization, Prediction and Optimization for Sustainable Environment

The Differential Impact of Various Injection Pressures on the Exergy of a Diesel Engine Using Biodiesel-Diesel Fuel Blends

Efficient ELM Model With Parameter Optimization Using Pso Algorithms in the Prediction of Combustion Pressure Parameters of Dsi Engine Using Ethanol- Gasoline Blends

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