Development of ANN model for prediction of performance and emission characteristics of hydrogen dual fueled diesel engine with Jatropha Methyl Ester biodiesel blends

Javed, Syed; Murthy, Y.V.V. Satyanarayana; Baig, Rahmath Ulla; Rao, Dr.P.Ramachandra

doi:10.1016/j.jngse.2015.06.041

Cited by 99 publications

(40 citation statements)

References 23 publications

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“…To validate and to test the network, 70-15-15% of the data set was used to train, respectively. Since there is no certain rule of defining these ratios, it can clearly be seen from the literature that this data division configuration is very common and can be used [19][20][21]. In the present study, Levenberg-Marquardt algorithm was selected as learning algorithm.…”

Section: The Ann Resultsmentioning

confidence: 99%

Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

et al. 2017

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This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and backpropagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.

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Section: The Ann Resultsmentioning

confidence: 99%

Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

et al. 2017

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“…Seven training algorithms each with five combinations of trainings functions were investigated. Levenberg-Marquardt back-propagation, training algorithm with logarithmic sigmoid and hyperbolic tangent sigmoid transfer function results in best model for prediction of performance and emissions characteristics (Syed et al, 2015). In the present study developed a Back-Propagation Neual Network (BPNN) predictive model of a process parameters influencing on performance and emissions of diesel engine.…”

Section: Introductionmentioning

confidence: 97%

An Artificial Neural Network and Taguchi Integrated Approach to the Optimization of Performance and Emissions of Direct Injection Diesel Engine

Beeravelli

Chanamala

Rayavarapu

et al. 2018

European Journal of Sustainable Development Research

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Prediction of operating parameters as a function of brake thermal efficiency (BTHE), brake specific fuel consumption (BSFC), carbon monoxide (CO), Hydrocarbons (HC), nitrogen oxide (NOX) and Smoke opacity is very important in performance and emission characteristics of the engine. In this study, the effect of operating parameters such as load, blend, compression ratio (CR), injection pressure (IP) and injection timing (IT) on the output responses above mentioned were investigated by using ANN (Artificial neural networks) and trained the signal-to-noise ratio (S/N) results obtained from Taguchi L16 orthogonal design. These results are compared with the artificial neural network and confirmation test was conducted and the results obtained were well supported.

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“…With the introduction of high-speed computational facilities, artificial neural network (ANN) has widely been used as a forecasting tool for estimating the IC engine performance and emissions. Some researchers proposed an ANN for forecasting the characteristics of the engine, and reported that ANN results are very accurate with acceptable limits of errors [17]- [19]. It is a complicated and dynamic process to find an optimum solution to a multi-objective problem.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation, ANN Modelling and TOPSIS Optimization of a Gasoline Premixed HCCI-DI Engine with Direct Injection of FeCl3 Nanodditive Blended WCO

Leo

Subramani²,

Arivazhagan

2019

TFAMENA

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Experiments have been carried out to compute performance, combustion and emission characteristics of a homogeneous charge compression ignition-direct injection (HCCI-DI) engine in which 20% of the fuel was premixed in the intake manifold and the remaining 80% of the fuel was injected directly. Gasoline was selected as the premixed fuel and three different fuel combinations, namely, diesel, B50 (50% waste cooking oil (WCO) and 50% diesel by volume) and WCO were selected as direct injection (DI) fuels. 100 ppm of FeCl 3 nanoadditive was blended with the DI fuels aimed at enhancing favourable fuel properties. The experimental investigations show a reduction of 54.17% and 50% in hydrocarbon (HC) and carbon monoxide (CO) emissions, respectively, in the case of WCO fuelled DI combustion compared with the diesel fuelled combustion. Significant increase in the cylinder pressure (p cyl) and the rate of heat release (ROHR) values was observed when the FeCl 3 nanoadditive blended fuel was used. Also, with this type of fuel smoke emissions were reduced by 34.8%. Significant increase in the brake thermal efficiency (η bth) with reduced nitrogen oxide (NO x) emissions was observed in the HCCI-DI combustion. Artificial neural network (ANN) was used for forecasting the performance of and emissions from the engine in different operating conditions. The technique for order preference by similarity to ideal solution (TOPSIS) was used for optimizing engine input parameters, which can result in maximum efficiency and minimum emissions.

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Development of ANN model for prediction of performance and emission characteristics of hydrogen dual fueled diesel engine with Jatropha Methyl Ester biodiesel blends

Cited by 99 publications

References 23 publications

Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

An Artificial Neural Network and Taguchi Integrated Approach to the Optimization of Performance and Emissions of Direct Injection Diesel Engine

Experimental Investigation, ANN Modelling and TOPSIS Optimization of a Gasoline Premixed HCCI-DI Engine with Direct Injection of FeCl3 Nanodditive Blended WCO

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