Developing a smart fuel using artificial neural network for compression ignition engine fueled with Calophyllum inophyllum diesel blend at various compression ratio

2020 ◽  
Vol 39 (5) ◽  
Author(s):  
Paramaguru Venugopal ◽  
Ramesh Kasimani ◽  
Suresh Chinnasamy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compression Ratio ◽  
Compression Ignition ◽  
Compression Ignition Engine ◽  
Calophyllum Inophyllum ◽  
Artificial Neural

The model of compression ignition engine with artificial neural networks

2008 ◽  
Vol 132 (1) ◽  
pp. 44-49
Author(s):  
Krzysztof BRZOZOWSKI ◽  
Jacek NOWAKOWSKI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Compression Ignition ◽  
Control Parameters ◽  
Working Process ◽  
Compression Ignition Engine ◽  
The Neural Networks ◽  
Artificial Neural ◽  
Model Equations

The paper presents an application of artificial neural network in modelling the working process in compression ignition engine. In order to determine the usefulness of proposed method the optimisation task has been formulated. The aim of optimisation process was to find the engine control parameters which enable reduction of the NOx emission. In order to solve the problem, the model equations has to be integrated for values of control parameters whose are given as output from the neural networks implemented.

scholarly journals Artificial Neural Network Modeling and Sensitivity Analysis of Performance and Emissions in a Compression Ignition Engine Using Biodiesel Fuel

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2410 ◽  
Author(s):  
Farzad Jaliliantabar ◽  
Barat Ghobadian ◽  
Gholamhassan Najafi ◽  
Talal Yusaf
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Research Work ◽  
Neural Network Modeling ◽  
Biodiesel Fuel ◽  
Compression Ignition ◽  
Compression Ignition Engine ◽  
Performance And Emissions ◽  
Artificial Neural

In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.

Computational optimization of reactivity controlled compression ignition combustion to achieve high efficiency and clean combustion

2020 ◽  
pp. 146808742093173 ◽  
Author(s):  
Avilash Jain ◽  
Anand Krishnasamy ◽  
Pradeep V
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Injection ◽  
Direct Injection ◽  
Network Models ◽  
Compression Ignition ◽  
Neural Network Models ◽  
Reactivity Controlled Compression Ignition ◽  
Artificial Neural ◽  
Artificial Neural Network Models

One of the major limitations of reactivity controlled compression ignition is higher unburned hydrocarbon and carbon monoxide emissions and lower thermal efficiency at part load operating conditions. In the present study, a combined numerical approach using a commercial three-dimensional computational fluid dynamics code CONVERGE along with artificial neural network and genetic algorithm is presented to address the above limitation. A production light-duty diesel engine is modified to run in reactivity controlled compression ignition by replacing an existing mechanical fuel injection system with a flexible electronic port fuel injection and common rail direct injection systems. The injection schedules of port fuel injection and direct injection injectors are controlled using National Instruments port and direct injection driver modules. Upon validation of combustion and emission parameters, parametric investigations are carried out to establish the effects of direct-injected diesel fuel timing start of injection (SOI), premixed fuel ratio and intake charge temperature on the engine performance and emissions in reactivity controlled compression ignition. The results obtained show that the start of injection timing and intake charge temperature significantly influence combustion phasing, while the premixed fuel ratio controls mixture reactivity and combustion quality. By utilizing the data generated with the validated computational fluid dynamics models, the artificial neural network models are trained to predict the engine exhaust emissions and efficiency. The artificial neural network models for gross indicated efficiency and oxides of nitrogen (NOx) are then coupled with genetic algorithm to maximize gross indicated efficiency while keeping the NOx and soot emissions within Euro VI emission limits. By optimizing the start of injection timing, premixed fuel ratio and intake charge temperature simultaneously using the artificial neural network models coupled with genetic algorithm, 19% improvement in gross indicated efficiency, 60% and 64% reduction in hydrocarbon and carbon monoxide emissions, respectively, are obtained in reactivity controlled compression ignition compared to the baseline case.

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