Predicting the Exhaust Emissions of a Spark Ignition Engine Using Adaptive Neuro-Fuzzy Inference System

Isin, Ovun; Uzunsoy, Erdem

doi:10.1007/s13369-013-0637-7

Cited by 14 publications

(3 citation statements)

References 22 publications

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“…In fuzzy logic, nonlinearity and complexity of modelling can be handled by rules, membership functions and inference processes [9]. ANFIS can construct set-of if-then rules with suitable membership functions to constitute input-output pairs [18].…”

Section: Adaptive Neuro Fuzzy Inference Systemmentioning

confidence: 99%

“…ANFIS modelling is very powerful technique with the ability of interpretable if-then rules [8]. Isin and Uzunsoy presented fuzzy logic-based prediction method to reveal the performance and emission characteristics of a single cylinder spark ignition (SI) engine, which uses different fuel mixtures [9]. Ozkan et al used ANFIS to estimate the effect of methanol mixtures in different proportions on emission and performance of the motor [10].…”

Section: Introductionmentioning

confidence: 99%

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Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives

Tosun¹,

Özgür²,

Özgür³

et al. 2017

European Mechanical Science

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In this study, emissions of compression ignition engine fueled by diesel fuel with nanoparticle additives was modeled by regression analysis, artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) methods. Cetane number (CN) and engine speed (rpm) were selected as input parameters for estimation of carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO 2 ) emissions. The results of estimation techniques were compared with each other and they showed that regression analysis was not accurate enough for prediction. On the other hand, ANN and ANFIS modelling techniques gave more accurate results with respect to regression analysis; linear and non-linear. Especially ANFIS models can be suggested as estimation method with minimum error compared to experimental results. Keywords:Adaptive neuro fuzzy inference system; Artificial neural network; Diesel engine; Regression analysis INTRODUCTIONIn recent years, depletion of fossil fuels forces researchers to search new alternative fuels. In literature, there are a lot of studies about fuels which have potential to replace fossil fuels used in internal combustion engines. In this respect, various biofuels and alcohols seem as good option [1]. In addition to scarcity of conventional fuels, efforts on performance en-hancement and emission reduction of engines are the other important issues on which engineers and engine manufacturers are working on it. Especially, the stringent emission legislations enforced manufacturers to develop new technologies [2]. Traditional engine research and development studies are both difficult and costly to meet emission limits imposed by legislations. Therefore, these costly studies are replaced by various cost-effective approaches as artificial neural networks (ANN) and computational fluid dynamics (CFD) [3]. ANNs are nonlinear computer algorithms, which can model the behavior of complex nonlinear processes. Recently, this method has been widely applied to various disciplines as automotive engineering [4]. Yusaf et al. studied the effect of using CPO (crude palm oil) -OD (ordinary diesel) blends as fuel on the performance of CI (compression ignition) engine. In addition, engine power output, fuel consumption, and exhaust-gas emission are evaluated and then predicted using ANN technique [5]. Shanmugam et al. used ANN modeling to predict the performance and exhaust emissions of the diesel engine using hybrid fuel and they revealed that the ANN approach could be confidently used to predict the performance and emissions of the diesel engine accurately [6]. Ghazikhani and Mirzaii predicted soot emission of a waste-gated turbo-charged DI diesel engine using ANN. The results showed the ANN approach can be used to accurately predict soot emis-sion of a turbo-charged diesel engine in different opening ranges of waste-gate (ORWG) [7].

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Section: Adaptive Neuro Fuzzy Inference Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives

Tosun¹,

Özgür²,

Özgür³

et al. 2017

European Mechanical Science

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“…After adding 10% and 20% aqueous ethanol by volume, the tests were conducted at full load and at different engine speeds ranging from 1500 to 5000 rpm. 5,6 The results were compared to those obtained from the use of free gasoline; they showed a decrease in exhaust noise. However, at high speed, the engine with three fuels was showed similar noise emissions.…”

Section: Introductionmentioning

confidence: 99%

Real world evaluation and control of vehicle engine exhaust air pollution using adaptive neural fuzzy inference system

Abouel-Seoud

Shiba

Abdallah

2022

International Journal of Engine Research

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Internal combustion engines are considered primarily responsible for air pollution. Accordingly, a concentrated effort has been made to reduce these pollutants which are emitted from the exhaust system of these engines while preserving energy and fuel consumption. There are also other sources besides internal combustion engines, such as power stations and those released from industry and home consumption. Industrial and household fuel consumers are also adding pollution. The industrial development requires the establishment of effective control systems to deal with its equipment to increase its operational life and increase its productivity with lower maintenance costs. Therefore, the aim of the study in this research is exposed to be used of adaptive neural fuzzy inference system to control the components of the emission of exhaust pollutants for gasoline vehicle. Exhaust pollutants consist of unburned carbon monoxide, hydrocarbons, carbon dioxide, water vapor nitrogen oxides, and energy produced from the combustion process which emit into atmosphere through the exhaust tailpipe. Also, there are hydrocarbons produced from gasoline vaporization and from the crankcase of the vehicle. The results indicate that the adaptive neural fuzzy inference system proves to be a useful tool for simulating and controlling vehicle engine exhaust emissions.

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ANFIS modeling of biodiesels' physical and engine characteristics: A review

et al. 2021

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Population increase has resulted in an increase in the worldwide demand for alternative fuels due to depleting resources. There is a periodic increase in concern about the engine performance, pollutant emissions, and their predictions, from an engine using biodiesels. The use of intelligent algorithms in modeling and forecasting alternative fuels characteristics and their performance in engines are critically reviewed in this study. The paper aims at demonstrating with artificial intelligence methodologies the main conclusions of the recent research done for the above topic from 2012 to 2020. This article attempted to demonstrate an exploratory examination of the adaptive neuro-fuzzy inference system (ANFIS) soft computing technique used for the exact measurement and analysis of engine performance, emissions of exhaust engines when biodiesel is used as an alternative fuel. Additionally, the yield of biodiesel and their different characteristics predicted using ANFIS are also reviewed. Integration of particle swarm optimization (PSO), genetic algorithm (GA), and response surface methodology (RSM), either for comparison or optimization with ANFIS is presented. The summary of all studies is provided in tabular form. For the demonstration purpose, the ANFIS studies predicting different biodiesel and engine characters are provided with illustrative figures. The ANFIS prediction related to biodiesel used engine and biodiesel self-characteristics is found to be excellent. The ANFIS accuracy reported is better than the artificial neural network (ANN) accuracy. A minimum of 0.9 R 2 value is generally obtained which is around 5% greater than the ANN modeling results reported.However, the ANFIS predictions are much more fitter than the RSM predictions. The integration of ANFIS-PSO and ANFIS-GA provided much more optimized results.

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Predicting the Exhaust Emissions of a Spark Ignition Engine Using Adaptive Neuro-Fuzzy Inference System

Cited by 14 publications

References 22 publications

Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives

Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives

Real world evaluation and control of vehicle engine exhaust air pollution using adaptive neural fuzzy inference system

ANFIS modeling of biodiesels' physical and engine characteristics: A review

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