CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network

Yusaf, Talal; Buttsworth, David; Saleh, Khalid; Yousif, B. F.

doi:10.1016/j.apenergy.2009.10.009

Cited by 210 publications

(80 citation statements)

References 12 publications

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“…Using this setting at lower rotation speed values, besides admitting a smaller amount of O2 to the injection pump, and trying to offset falls in rotation, it injects a greater amount of diesel oil inside combustion chamber, increasing the ratio between air and fuel and, thus, emitting more CO whether compared to the standard setting. These results corroborate those of AN et al (2013) andYUSAF et al (2010), who concluded that reductions of CO emissions in exhaust gases derives from a complete combustion.…”

Section: Resultssupporting

confidence: 82%

Performance and Emission of Pollutants of an Agricultural Engine With Two Power Take-Off Settings

Schlosser

Farias

Estrada³

et al. 2017

Eng. Agríc.

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Farming operations are usually performed implements driven by a power take-off (PTO) from tractors. Some manufacturers offer PTO settings named as economic (540E). Such configuration drives implements at lower tractor's engine rotation, thus providing lower fuel consumption. An engine performance using standard (540) or 540E settings are different and poorly studied. The amounts of pollutants emitted by engines vary mainly with the applied load and rotation. The aim of this study was to compare the performance and exhaust emissions of a farming tractor engine under different PTO loads, using both standard (540) and 540E settings. Specific fuel consumption using 540E was an average 14.7% less than the standard setting. However, 540E promoted increasing levels of gas opacity, CO, CO2, NOx, and HC for the highest PTO power demands.

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Section: Resultssupporting

confidence: 82%

Performance and Emission of Pollutants of an Agricultural Engine With Two Power Take-Off Settings

Schlosser

Farias

Estrada³

et al. 2017

Eng. Agríc.

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“…According to Yusaf et al (2010) ANN is widely used to predict the performance and exhaust emissions of a diesel engine and gasoline both similar results were reported recently (Phacbhai et al, 2014). The ANN principle is similar to the black box model that provides information system based on the fixed or not fixed input parameters with the train data previously (Golcu et al, 2005).…”

Section: Introductionsupporting

confidence: 67%

Optimization and Prediction of Motorcycle Injection System Performance with Feed-Forward Back-Propagation Method Artificial Neural Network (ANN)

Syahrullah¹,

Sinaga²

2016

American Journal of Engineering and Applied Sciences

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This research studied the use of Artificial Neural Network (ANN) using feed-forward back-propagation model to optimize and predict the performance of a motorcycle fuel injection systems of gasoline. The parameters such as speed, throttle position, ignition timing and injection timing is used as the input parameters. While the parameters of fuel consumption and engine torque is used as the output layer. LavenbergMarquardt model type with train function tanh sigmoid and 25 neurons number is used to generate the target value and the desired output. Variation of ignition timing as optimization variable in a wide range of speed and throttle position is used in experimental tests. ANN is used to investigate the prediction of performance motorcycle engines and compared with the test results. Results showed that the operation of ANN in predicting engine performance is very good. From the test results obtained a smooth contour MAP compared to the initial state. The prediction result and performance test show a good correlation in small error value of training and test that is regression with range 0.98-0.99, mean relative error with range 0.1315-0.4281% and the root mean square error with range 0.2422-0.9754%. This study shows that the feed-forward back propagation on ANN model can be used to predict accurately the performance of a motorcycle engine injection system.

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“…NNs can predict the environmental impacts of different energy resources on the atmosphere, oceans, and the whole of the planet through analysing relevant historical data (Juang et al, 2009;Linker et al, 1998;Sözen & Ali Akçayol, 2004;Yusaf et al).…”

Section: Introductionmentioning

confidence: 99%

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Safa

Samarasinghe

2011

Energy

116

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New Zealand farmers practice a form of ‗industrialised' agriculture that relies on relatively high inputs of fossil fuels, not only to power machinery directly but also for manufacturing artificial fertilizers and agrichemicals (Wells, 2001). Consequently, New Zealand is one of the countries with the highest energy input per unit weight (in agriculture) in the world (Conforti & Giampietro, 1997). Furthermore, in terms of shipping, the influence of increasing global fuel costs is greater on New Zealand farming than in other countries. The main aim of this study was to estimate energy consumption in wheat production. Energy determination can give a clear picture of farms in order to compare different farming systems and energy inputs. The second main target of this study was to develop a neural network model to simulate and predict energy use in wheat production under different conditions incorporating social, geographical, and technical factors. Additionally, the interaction effects between different factors were examined in this study.This study was conducted on irrigated and dryland wheat fields in Canterbury, New Zealand, in the 2007-2008 harvest year. Canterbury represents 87% of the wheat area and 66% of the arable area harvested in New Zealand.Energy consumption here is defined as the energy used for the production of wheat until it leaves the farm. The data were collected from three different sources:questionnaire, literature review, and field measurements. The energy inputs estimated in this study are those that go into on-farm production systems before the post-harvest processes. The study considered only the energy used in wheat production, without taking into account the natural sources of energy (radiation, wind, rain, etc). IVA survey was conducted to collect the most important data and to identify farmers' attitudes and opinions about energy consumption. In this study, 40 arable farms were selected randomly, as far as possible. From the initial analysis, it was found that 30 farms were irrigated and the rest were dryland farms. Irrigated farms were irrigated between one to ten times annually depending on the rainfall. Some irrigated farms have also been converted to dryland farms, or vice versa, in different years. The data for a large number of farming factors were gathered in the survey.Average energy consumption for wheat production was estimated at around 22,600 MJ/ha. On average, fertilizer and electricity (mostly for irrigation) were used more than other energy sources, at around 10,654 MJ/ha (47%) and 4,870 MJ/ha (22%), respectively. The average energy consumption for wheat production in irrigated farming systems and dryland farming systems was estimated at 25,600 and 17,458 MJ/ha, respectively. This study is the first to create an appropriate Artificial Neural Network (ANN) model to predict energy consumption in wheat production with optimum variables. This study would be the first to investigate the factors related to the efficient use of energy in agricultural production. A careful study o...

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CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network

Cited by 210 publications

References 12 publications

Performance and Emission of Pollutants of an Agricultural Engine With Two Power Take-Off Settings

Performance and Emission of Pollutants of an Agricultural Engine With Two Power Take-Off Settings

Optimization and Prediction of Motorcycle Injection System Performance with Feed-Forward Back-Propagation Method Artificial Neural Network (ANN)

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

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