SVM and ANFIS for prediction of performance and exhaust emissions of a SI engine with gasoline–ethanol blended fuels

Najafi, G.; Ghobadian, Barat; Moosavian, Ashkan; Yusaf, Talal; Mamat, Rizalman; Kettner, Maurice; Azmi, W.H.

doi:10.1016/j.applthermaleng.2015.11.009

Cited by 106 publications

(34 citation statements)

References 54 publications

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“…However, those changes were not statistically significant. This result agrees with our analytical results and those obtained by other authors [14][15][16]. Figure 6c,d show that EI CO is about two orders of magnitude higher for carbureted vehicles than for fuel-injected vehicles, when they reproduce the NEDC on a chassis dynamometer.…”

Section: Co Emission Indexsupporting

confidence: 92%

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The Effect of Using Ethanol-Gasoline Blends on the Mechanical, Energy and Environmental Performance of In-Use Vehicles

et al. 2018

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Abstract:The use of ethanol in gasoline has become a worldwide tendency as an alternative to reduce net CO 2 emissions to the atmosphere, increasing gasoline octane rating and reducing dependence on petroleum products. However, recently environmental authorities in large urban centers have expressed their concerns on the true effect of using ethanol blends of up to 20% v/v in in-use vehicles without any modification in the setup of the engine control unit (ECU), and on the variations of these effects along the years of operation of these vehicles. Their main concern is the potential increase in the emissions of volatile organic compounds with high ozone formation potential. To address these concerns, we developed analytical and experimental work testing engines under steady-conditions. We also tested carbureted and fuel-injected vehicles every 10,000 km during their first 100,000 km of operation. We measured the effect of using ethanol-gasoline blends on the power and torque generated, the fuel consumption and CO 2 , CO, NOx and unburned hydrocarbon emissions, including volatile organic compounds (VOCs) such as acetaldehyde, formaldehyde, benzene and 1,3-butadiene which are considered important ozone precursors. The obtained results showed statistically no significant differences in these variables when vehicles operate with a blend of 20% v/v ethanol and 80% v/v gasoline (E20) instead of gasoline. Those results remained unchanged during the first 100,000 km of operation of the vehicles. We also observed that when the vehicles operated with E20 at high engine loads, they showed a tendency to operate with greater values of λ (ratio of the actual air-fuel ratio to the stoichiometric air-fuel ratio) when compared to their operation with gasoline. According to the Eco-Indicator-99, these results represent a minor reduction (<1.3%) on the impact to human health, and on the deterioration of the ecosystem. However, it implies a 12.9% deterioration of the natural resources. Thermal equilibrium analysis, at the tailpipe conditions (~100 • C), showed that ethane, formaldehyde, ethylene and ethanol are the most relevant VOCs in terms of the amount of mass emitted. The use of ethanol in the gasoline reduced 20-40% of those emissions. These reductions implied an average reduction of 17% in the ozone formation potential.

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Section: Co Emission Indexsupporting

confidence: 92%

“…Ref. [14] analyzed a KIA 1.3 SOHC four-cylinder engine, operating with gasoline-E20 blends at full load and found that the power increased slightly with the ethanol content in the fuel. Ref.…”

Section: Power and Torquementioning

confidence: 99%

The Effect of Using Ethanol-Gasoline Blends on the Mechanical, Energy and Environmental Performance of In-Use Vehicles

et al. 2018

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“…It uses a structured risk reduction principle that minimizes the upper bound of the expected risk [13]. It also can provide excellent predictive accuracy and find the optimal global solution [14]. ARIMA is very appropriate and applicable when existing information or data is limited and the data stream acts as a predictor [15].…”

Section: A Prediction Techniquesmentioning

confidence: 99%

Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

Salleh

Yuhaniz

Sabri

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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This paper studies the machine learning techniques that can be used to enhance the prediction method of the ionosphere for space weather monitoring. Previously, the empirical model is used. However, there is a large deviation of the total electron content of ionosphere data for the areas located in the equatorial and low-latitude regions due to the lack of observation data contributed by these areas during the development of the empirical model. The machine learning technique is an alternative method used to develop the predictive model. Thus, in this study, the machine learning techniques that can be applied are investigated. The aim is to improve the predictive model in terms of reducing the total electron content deviation, increasing the accuracy and minimizing the error. In this review, the techniques used in previous works will be compared. The artificial neural network is found to be a suitable technique and the most favorable from the review conducted. Also, this technique can provide an accurate model for time series data and fewer errors compared to other techniques. However, due to the size and complexity of the data, the deep neural network technique that is an improved artificial neural network technique is suggested. By using this technique, an accurate ionosphere predictive model in equatorial and low region area is expected. In the future, this study will analyze further by using computing tools and real-time data.

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“…However, little research has been done based on mixtures of ethanol gasoline and H2O. When ethanol is added to gasoline the oxygen content of blended fuel increases and higher the oxygen content of the fuel, the less CO emissions are formed because the fuel is completely burned [5][6][7][8][9]. In addition, with the use of ethanol containing water, the emissions are even lower, compared to 100% of gasoline as fuel [10].…”

Section: Introductionmentioning

confidence: 99%

Performance Combustion and Emission Characteristics of 3-Cylinder SI Engine Fuelled Conventional Gasoline, Ethanol Blends, and Micro-emulsion Used as an Alternative Fuel

Qadiri¹,

Wani²

2019

MMEP

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In this contribution 15% ethanol blend with 85% gasoline, and micro-emulsion fuel (90% gasoline, 8% ethanol, & 2% H2O) was compared with 100% conventional gasoline fuel on 3-cylinder Spark Ignition engine to check its performance and emission characteristics. The AVL Boost software was used for predicting the performance and emission characteristics of 3-cylinder SI engine. It was found that although power increases for conventional 100% gasoline as the fuel, as compared to 15% ethanol blends and micro-emulsion fuel, but emissions were reduced more for micro-emulsion used as an alternative fuel in SI engine, and it meets the standards of present emission norms Euro 6. Micro-emulsion fuel was prepared in the lab by blending gasoline, ethanol and H2O was used as an additive in the fuel for improving its physical & thermal properties. The H2O molecule present in the fuel helped in reducing the emissions from 3-cylinder SI engine. As H2O addition increases the oxygen concentration in the micro-emulsion fuel which helps in enhancing the combustion of fuel, and in build oxygen atom inside the ethanol fuel also helps in better combustion which ultimately gives less CO, HC, & NOx emissions. The vibe-2 zone model was used for the combustion analysis of the engine which involves both burnt and unburnt gases in the combustion chamber. The results showed that micro-emulsion fuel not only improves the performance but also reduces the emissions. All the emissions CO, HC, and NOx showed decrease for ethanol blended fuel and micro-emulsion as compared to 100% gasoline fuel.

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SVM and ANFIS for prediction of performance and exhaust emissions of a SI engine with gasoline–ethanol blended fuels

Cited by 106 publications

References 54 publications

The Effect of Using Ethanol-Gasoline Blends on the Mechanical, Energy and Environmental Performance of In-Use Vehicles

The Effect of Using Ethanol-Gasoline Blends on the Mechanical, Energy and Environmental Performance of In-Use Vehicles

Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

Performance Combustion and Emission Characteristics of 3-Cylinder SI Engine Fuelled Conventional Gasoline, Ethanol Blends, and Micro-emulsion Used as an Alternative Fuel

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