Exhaust emissions prognostication for DI diesel group-hole injectors using a supervised artificial neural network approach

Taghavifar, Hadi; Taghavifar, Hamid; Mardani, Aref; Mohebbi, Arash

doi:10.1016/j.fuel.2014.02.016

Cited by 36 publications

(16 citation statements)

References 23 publications

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“…The majority of research work has recommended that data should be normalized between 0.1 and 0.9 instead of 0 and 1 to prevent saturation of the sigmoid function leading to slow or no learning. 4,11,14,[38][39][40] In this study, a multilayer FF neural network with backpropagation algorithm has been utilized for ANN modelling. The single hidden layer has been chosen for developing the network.…”

Section: Ann Modellingmentioning

confidence: 99%

“…12,13 Ismail et al 11 predicted nine engine output responses of a single cylinder diesel engine using back-propagation feed-forward (FF) neural network with acceptable error. Taghavifar et al 14 illustrated the feasibility of ANN prediction of soot and NO X . An ANN model has also been formulated by Ghobadian et al, 1 to accurately predict the engine torque, SFC, carbon monoxide (CO) and total hydrocarbon (THC) emissions of a twin cylinder four-stroke diesel engine.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of performance and exhaust emissions of diesel engine fuelled with adulterated diesel: An artificial neural network assisted fuzzy-based topology optimization

Bhowmik

Panua

Ghosh

et al. 2018

Energy & Environment

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This study evaluates the effects of diesel fuel adulteration on the performance and exhaust emission characteristics of an existing diesel engine. Kerosene is added to diesel fuel in volumetric proportions of 5, 10, 15, and 20%. Adulterated fuel significantly reduced the oxides of nitrogen emissions of the engine. In view of the engine experimentations, artificial intelligence-based artificial neural network model has been developed to accurately predict the input–output relationships of the diesel engine under adulterated fuel. The investigation also attempts to explore the applicability of fuzzy logic in the selection of the network topology of artificial neural network model under adulterated fuel. A (2–7–5) topology is found to be optimal for predicting input parameters, namely load, diesel–kerosene blend and output parameters, namely brake thermal efficiency, brake-specific energy consumption, oxides of nitrogen, total hydrocarbon, carbon monoxide of the network. The developed artificial neural network model is enabled for predicting engine output responses with high accuracy. The regression coefficient (R) of 0.99887, mean square error of 1.5e-04 and mean absolute percentage error of 2.39% have been obtained from the plausible artificial neural network model.

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Section: Ann Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of performance and exhaust emissions of diesel engine fuelled with adulterated diesel: An artificial neural network assisted fuzzy-based topology optimization

Bhowmik

Panua

Ghosh

et al. 2018

Energy & Environment

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“…In the current research, the authors chose the trainlm and trainsig approach since in the studies conducted by our team [22][23][24], the specified algorithm has always exhibited the lowest MSE with respect to number of neurons in hidden layer, meanwhile other papers also reported Table 1 The basic assumptions for analysis of the tri-generation system [20].…”

Section: Ann Configuration and Network Performance Evaluationmentioning

confidence: 99%

Implementation of ANN on CCHP system to predict trigeneration performance with consideration of various operative factors

Anvari

Taghavifar

Saray

et al. 2015

Energy Conversion and Management

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“…Recently, neural network becomes more popular as an automotive engine diagnose or a specific engine activities forecasting. A number of researches have been carried out using different methodology on engine diagnose [2][3] [4]. The current study proposed a modeling and forecasting of the car speed based on HMLP with MRPE as the training algorithm.…”

Section: Introductionmentioning

confidence: 99%

Modelling and forecasting of car speed using detrended inputs and hybrid multilayered percepton network

Saad¹,

Osman²,

Mashor

2014

ces

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The study explores the capability of hybrid multilayered perceptron network (HMLP) trained using modified recursive prediction error (MRPE) algorithm as a nonlinear model for a speed of a car. This model is used for speed forecasting for a car in order to avoid speed limit on the road traffic. The injected fuel was measured from the experimented car and used as an input for the HMLP model. The input and output variables were recorded from the car sensors. The modeling performances are measured based on the correlation tests. The simulation result shows that preprocessing data using digital high-pass filter is better than the first differential to pass the correlation test. This preliminary result shows that this modeling system is capable to be used as an alternative speed-forecaster for cars.

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Exhaust emissions prognostication for DI diesel group-hole injectors using a supervised artificial neural network approach

Cited by 36 publications

References 23 publications

Prediction of performance and exhaust emissions of diesel engine fuelled with adulterated diesel: An artificial neural network assisted fuzzy-based topology optimization

Prediction of performance and exhaust emissions of diesel engine fuelled with adulterated diesel: An artificial neural network assisted fuzzy-based topology optimization

Implementation of ANN on CCHP system to predict trigeneration performance with consideration of various operative factors

Modelling and forecasting of car speed using detrended inputs and hybrid multilayered percepton network

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