rediction of spark ignition engine performance responses fueled with fusel oil/gasoline blends by artificial neural network

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.

Download Full-text

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

Thermal Science ◽

10.2298/tsci160302151t ◽

2017 ◽

Vol 21 (1 Part B) ◽

pp. 401-412 ◽

Cited By ~ 4

Author(s):

Erdi Tosun ◽

Kadir Aydin ◽

Simona Merola ◽

Adrian Irimescu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Analysis ◽

Catalytic Converter ◽

Direct Injection ◽

Spark Ignition ◽

Spark Ignition Engine ◽

Back Propagation Algorithm ◽

Brake Mean Effective Pressure ◽

Artificial Neural

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 back-propagation 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.

Download Full-text

An artificial neural network developed for predicting of performance and emissions of a spark ignition engine fueled with butanol–gasoline blends

Advances in Mechanical Engineering ◽

10.1177/1687814017748438 ◽

2018 ◽

Vol 10 (1) ◽

pp. 168781401774843 ◽

Cited By ~ 5

Author(s):

Zhiqiang Liu ◽

Qingsong Zuo ◽

Gang Wu ◽

Yuelin Li

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Equivalence Ratio ◽

Engine Performance ◽

Spark Ignition Engine ◽

Performance And Emissions ◽

Artificial Neural

The engine experiments require multiple tests that are hard, time-consuming, and high cost. Therefore, an artificial neural network model was developed in this study to successfully predict the engine performance and exhaust emissions when a port fuel injection spark ignition engine fueled with n-butanol–gasoline blends (0–60 vol.% n-butanol blended with gasoline referred as G100-B60) under various equivalence ratio. In the artificial neural network model, compression ratio, equivalence ratio, blend percentage, and engine load were used as the input parameters, while engine performance and emissions like brake thermal efficiency, brake-specific fuel consumption, carbon monoxide, unburned hydrocarbons, and nitrogen oxides were used as the output parameters. In comparison between experimental data and predicted results, a correlation coefficient ranging from 0.9929 to 0.9996 and a mean relative error ranging from 0.1943% to 9.9528% were obtained. It is indicated that the developed artificial neural network model was capable of predicting the combustion of n-butanol–gasoline blends due to a commendable accuracy.

Download Full-text

Combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural network

Alexandria Engineering Journal ◽

10.1016/j.aej.2020.06.023 ◽

2020 ◽

Vol 59 (5) ◽

pp. 3687-3697

Author(s):

M.A. Hashim ◽

M.H. Nasef ◽

A.E. Kabeel ◽

Nouby M. Ghazaly

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fault Detection ◽

Wavelet Packet ◽

Spark Ignition ◽

Wavelet Packet Transform ◽

Spark Ignition Engine ◽

Detection Technique ◽

Artificial Neural

Download Full-text

Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine

Applied Energy ◽

10.1016/j.apenergy.2004.07.008 ◽

2005 ◽

Vol 81 (2) ◽

pp. 187-197 ◽

Cited By ~ 76

Author(s):

Mustafa Gölcü ◽

Yakup Sekmen ◽

Perihan Erduranlı ◽

M. Sahir Salman

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Spark Ignition ◽

Spark Ignition Engine ◽

Variable Valve Timing ◽

Valve Timing ◽

Artificial Neural ◽

Variable Valve

Download Full-text

Prediction of generated power from steam turbine waste heat recovery mechanism system on naturally aspirated spark ignition engine using artificial neural network

Soft Computing ◽

10.1007/s00500-017-2873-3 ◽

2017 ◽

Vol 22 (18) ◽

pp. 5955-5964 ◽

Cited By ~ 3

Author(s):

Safarudin Gazali Herawan ◽

Kamarulhelmy Talib ◽

Azma Putra ◽

Ahmad Faris Ismail ◽

Shamsul Anuar Shamsudin ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Steam Turbine ◽

Heat Recovery ◽

Waste Heat Recovery ◽

Waste Heat ◽

Spark Ignition ◽

Spark Ignition Engine ◽

Recovery Mechanism ◽

Artificial Neural

Download Full-text

Prediction on Power Produced from Power Turbine as a Waste Heat Recovery Mechanism on Naturally Aspirated Spark Ignition Engine Using Artificial Neural Network

Modelling and Simulation in Engineering ◽

10.1155/2016/5072404 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Safarudin Gazali Herawan ◽

Abdul Hakim Rohhaizan ◽

Ahmad Faris Ismail ◽

Shamsul Anuar Shamsudin ◽

Azma Putra ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heat Recovery ◽

Waste Heat Recovery ◽

Waste Heat ◽

Simulation Method ◽

Spark Ignition ◽

Spark Ignition Engine ◽

Recovery Mechanism ◽

Artificial Neural

The waste heat from exhaust gases represents a significant amount of thermal energy, which has conventionally been used for combined heating and power applications. This paper explores the performance of a naturally aspirated spark ignition engine equipped with waste heat recovery mechanism (WHRM) in a sedan car. The amount of heat energy from exhaust is presented and the experimental test results suggest that the concept is thermodynamically feasible and could significantly enhance the system performance depending on the load applied to the engine. However, the existence of WHRM affects the performance of engine by slightly reducing the power. The simulation method is created using an artificial neural network (ANN) which predicts the power produced from the WHRM.

Download Full-text