Performance and emission prediction of a<i>tert</i>butyl alcohol gasoline blended spark-ignition engine using artificial neural networks

Danaiah, Puli; Kumar, P. Ravi; Rao, Y. V. Hanumantha

doi:10.1080/01430750.2013.820147

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…After testing, the R 2 data for SFC and engine torque were 0.999915 and 0.999977, respectively. Similarly, Danaiah et al [ 66 ] used a tert butyl alcohol–gasoline blend and developed a 3-1-10 network to forecast the various engine characteristics and found an excellent correlation among the experimental and forecasting results.…”

Section: Modeling Of Internal Combustion Enginesmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.

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Section: Modeling Of Internal Combustion Enginesmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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“…Other researches were conducted using ANN to predict performance and emission characteristics for a number of engines. [11][12][13][14][15][16][17][18][19] And engine performance parameters, such as torque, power, fuel consumption, exhaust temperature, cylinder parameters, and vibration signal, were also modeled by researchers using ANN with different kinds of network structures and learning algorithms. [20][21][22][23][24][25][26][27] The study of literature cited above has established the foundation that ANN with appropriate structure and learning algorithm could predict various engine performance parameters.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network–based performance modeling of a diesel engine within the whole operating region considering dynamic conditions

Sun

Wei

Yan

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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Engine performance under full working conditions, especially dynamic ones, is indispensable in many vehicle-level research fields. To acquire the engine performance parameters, a novel whole-region engine model, considering both steady and dynamic conditions, was developed based on limited test data in this work. This model used throttle position, engine speed, and its acceleration as the input variables to predict torque and brake-specific fuel consumption under all practical conditions within its operating envelope. The engine bench test was first conducted under typical operating conditions to collect test data for model development and validation. Then, the backpropagation neural network with designed structure was employed to perform data fitting for test conditions. After the analysis of parameter distribution tendency, the two-step interpolation method was used to generalize performance parameters under conditions apart from those test ones. The cross-condition prediction accuracy of developed engine model was validated by test data under various operating conditions. Also, the parameter prediction error of proposed modeling method was lower compared to that of existing neural network methods, which further proved its applicability to dynamic engine modeling issues.

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“…The goal of the learning procedure is to find the optimal set of weights which in the ideal case would produce the right output for any input [28,29]. Once the ANN is properly and sufficiently trained, it can generalize to similar cases which it has never seen [28,29,33]. The ANN consists of an input layer, hidden layers and an output layer.…”

Section: Artificial Neural Network Structure and Modelmentioning

confidence: 99%

“…Training is done to modify the connection weights, in some orderly manner using a suitable learning algorithm. In ANNs, an input is fed into the network along with the desired output and the weights are then adjusted so that the network attempts to produce the desired output [29,33]. The weights after training contain meaningful information whereas before training they are at random [41,42].…”

Section: Artificial Neural Network Structure and Modelmentioning

confidence: 99%

“…However, Cay [32] showed that ANN approach could also be used for predicting performance values in a petrol engines but the available works on the use of ANN to predict performance of spark ignition engines were not elaborate with respect to the actual percentage blends of alcohol used. Puli et al [33] looked at performance and emission prediction of a tertiary butyl alcohol gasoline blended spark ignition engine using artificial neural networks. In the study, experimental results for different load conditions with various percentages of tertiary butyl alcohol petrol blends such as 0-5% and 10% were investigated at 1500, 2000, and 2500rpm experimentally and blends of 6-9% were predicted with the ANN.…”

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confidence: 99%

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The Application of Artificial Neural Network in Prediction of the Performance of Spark Ignition Engine Running on Ethanol-Petrol Blends

Nwufo

Okwu

Nwaiwu

et al. 2017

IJET

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Abstract. The performance analysis of a single cylinder spark ignition engine fuelled with ethanol -petrol blends were carried out successfully at constant load conditions. E0 (Petrol), E10 (10% Ethanol, 90% Petrol), E20 (20% Ethanol, 80% Petrol) and E30 (30% Ethanol, 70% Petrol) were used as fuel. The Engine speed, mass flow rate, combustion efficiency, maximum pressure developed, brake specific fuel consumption and Exhaust gas temperature values were measured during the experiment. Using the experimental data, a Levenberg Marquardt Artificial Neural Network algorithm and Logistic sigmoid activation transfer function with a 4-10-2 model was developed to predict the brake specific fuel consumption, maximum pressure and combustion efficiency of G200 IMEX spark ignition engine using the recorded engine speed, mass flow rate, biofuels ratio and exhaust gas temperature as input variables. The performance of the Artificial Neural Network was validated by comparing the predicted data with the experimental results. The results showed that the training algorithm of Levenberg Marquardt was sufficient enough in predicting the brake specific fuel consumption, combustion pressure and combustion efficiency of the test engine. Correlation coefficient values of 0.974, 0.996 and 0.995 were obtained for brake specific fuel consumption, combustion efficiency and pressure respectively. These correlation coefficient obtained for the output parameters are very close to one (1) showing good correlation between the Artificial Neural Network predicted results and the experimental data while the Mean Square Errors were found to be very low (0.00018825 @ epoch 10 for brake specific fuel consumption, 1.0023 @ epoch 3 for combustion efficiency and 0.0013284@ epoch 5 for in-cylinder pressure). Therefore, Artificial Neural Network toolbox called up from MATLAB proved to be a useful tool for simulation of engine parameters. Artificial Neural Network model provided accurate analysis of these complex problems and has been found to be very useful for predicting the performance of the spark ignition engine. Thus, this has proved that Artificial Neural Network model could be used for predicting performance values in internal combustion engines, in this way it would be possible to conduct time and cost efficient studies instead of long experimental ones.

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Performance and emission prediction of atertbutyl alcohol gasoline blended spark-ignition engine using artificial neural networks

Cited by 11 publications

References 10 publications

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Artificial neural network–based performance modeling of a diesel engine within the whole operating region considering dynamic conditions

The Application of Artificial Neural Network in Prediction of the Performance of Spark Ignition Engine Running on Ethanol-Petrol Blends

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