The Development of Artificial Neural Network for Prediction of Performance and Emissions in a Compressed Natural Gas Engine with Direct Injection System

2007 ◽  
Author(s):  
Wendy H. Kurniawan ◽  
Shahrir Abdullah ◽  
Zulkifli M. Nopiah ◽  
Kamaruzzaman Sopian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Natural Gas ◽  
Direct Injection ◽  
Injection System ◽  
Compressed Natural Gas ◽  
Gas Engine ◽  
Natural Gas Engine ◽  
Performance And Emissions ◽  
Artificial Neural

Artificial Neural Network-Based Prediction of Performances-Exhaust Emissions of Diesohol Piloted Dual Fuel Diesel Engine Under Varying Compressed Natural Gas Flowrates

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Abhishek Paul ◽  
Subrata Bhowmik ◽  
Rajsekhar Panua ◽  
Durbadal Debroy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Natural Gas ◽  
Goodness Of Fit ◽  
Specific Energy Consumption ◽  
Dual Fuel ◽  
Mean Square ◽  
Compressed Natural Gas ◽  
Artificial Neural

The present study surveys the effects on performance and emission parameters of a partially modified single cylinder direct injection (DI) diesel engine fueled with diesohol blends under varying compressed natural gas (CNG) flowrates in dual fuel mode. Based on experimental data, an artificial intelligence (AI) specialized artificial neural network (ANN) model have been developed for predicting the output parameters, viz. brake thermal efficiency (Bth), brake-specific energy consumption (BSEC) along with emission characteristics such as oxides of nitrogen (NOx), unburned hydrocarbon (UBHC), carbon dioxide (CO2), and carbon monoxide (CO) emissions. Engine load, Ethanol share, and CNG strategies have been used as input parameters for the model. Among the tested models, the Levenberg–Marquardt feed-forward back propagation with three input neurons or nodes, two hidden layers with ten neurons in each layer and six output neurons, and tansig-purelin activation function have been found to the optimal model topology for the diesohol–CNG platforms. The statistical results acquired from the optimal network topology such as correlation coefficient (0.992–0.999), mean square error (MSE) (0.0001–0.0009), and mean absolute percentage error (MAPE) (0.09–2.41%) along with Nash–Sutcliffe coefficient of efficiency (NSE), Kling–Gupta efficiency (KGE), mean square relative error, and model uncertainty established itself as a real-time robust type machine learning tool under diesohol–CNG paradigms. The study also incorporated a special type of measure, namely Pearson's Chi-square test or goodness of fit, which brings up the model validation to a higher level.

Experimental Test of a New Compressed Natural Gas Engine with Direct Injection

2009 ◽  
Author(s):  
M. A. Kalam ◽  
H. H. Masjuki ◽  
T. M. I. Mahlia ◽  
M. A. Fuad ◽  
Ku Halim ◽  
...  
Keyword(s):  
Natural Gas ◽  
Experimental Test ◽  
Direct Injection ◽  
Compressed Natural Gas ◽  
Gas Engine ◽  
Natural Gas Engine

The Combustion and Performance of a Converted Direct Injection Compressed Natural Gas Engine using Spark Plug Fuel Injector

2010 ◽  
Author(s):  
Taib Iskandar Mohamad ◽  
Ali Yusoff ◽  
Shahrir Abdullah ◽  
Mark Jermy ◽  
Matthew Harrison ◽  
...  
Keyword(s):  
Natural Gas ◽  
Direct Injection ◽  
Fuel Injector ◽  
Compressed Natural Gas ◽  
Gas Engine ◽  
Natural Gas Engine ◽  
Spark Plug ◽  
And Performance

Compressed-natural-gas optimised downsized demonstrator engine

2017 ◽  
Vol 232 (1) ◽  
pp. 75-89 ◽  
Author(s):  
Jonathan Hall ◽  
Benjamin Hibberd ◽  
Simon Streng ◽  
Michael Bassett
Keyword(s):  
Carbon Dioxide ◽  
Natural Gas ◽  
Fuel Consumption ◽  
Carbon Dioxide Emissions ◽  
Alternative Fuels ◽  
Direct Injection ◽  
Driving Performance ◽  
Compressed Natural Gas ◽  
Gas Engine ◽  
Natural Gas Engine

The complexity of modern powertrain development is demonstrated by the combination of requirements to meet future emission regulations and test procedures such as the real driving emissions, the reductions in the fuel consumption and the carbon dioxide emissions as well as the expectations of customers that there must be a good driving performance. Gasoline engine downsizing is already established as a proved technology to reduce the carbon dioxide emissions of automotive fleets. Additionally, alternative fuels such as natural gas offer the potential to reduce significantly both the tailpipe carbon dioxide emissions and the other regulated exhaust gas emissions without compromising the driving performance and the driving range. This paper presents results showing how the positive fuel properties of natural gas can be fully utilised in a heavily downsized engine. The engine was modified to cope with the significantly higher mechanical and thermal loads when operating at high specific outputs on compressed natural gas. In this study, peak cylinder pressures of up to 180 bar and specific power output levels of 110 kW/l were realised. It is also shown that having cylinder components specific to natural gas can yield significant reductions in the fuel consumption and, in conjunction with a variable-geometry turbine, a port-fuelled compressed-natural-gas engine can achieve a impressive low-speed torque (a brake mean effective power of 2700 kPa at 1500 r/min) and good transient response characteristics. The results achieved from the test engine while operating on compressed natural gas are compared with measurements from the baseline gasoline-fuelled direct-injection engine. In addition, a comparison between port fuel injection and direct injection of compressed natural gas is presented. This also includes an investigation into the specific performance challenges presented by port-fuel-injected compressed natural gas. The potential carbon dioxide savings offered by this heavily downsized compressed-natural-gas engine, of up to 50% at peak power and 20–40% for the driving-cycle region (including real-driving-emissions testing), are presented and discussed.

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