University of Sussex
IJERT_ANN_Based_Modelling_IC_Engine_Performance_D.Boruah.pdf (1.3 MB)

Artificial neural network based modelling of internal combustion engine performance

Download (1.3 MB)
journal contribution
posted on 2023-06-09, 00:49 authored by Dibakor Boruah, Pintu Kumar Thakur, Dipal Baruah
The present study aims to quantify the applicability of artificial neural network as a black-box model for internal combustion engine performance. In consequence, an artificial neural network (ANN) based model for a four cylinder, four stroke internal combustion diesel engine has been developed on the basis of specific input and output factors, which have been taken from experimental readings for different load and engine speed circumstances. The input parameters that have been used to create the model are load, engine speed (RPM), fuel flow rate (FFR) & air flow rate (AFR); contrariwise the output parameters that have been used are brake power (BP), brake thermal efficiency (BTE), volumetric efficiency (VE), brake mean effective pressure (BMEP) and brake specific fuel consumption (BSFC). To begin with, databank has been alienated into training sets and testing sets. At that juncture, an ANN based model has been developed using training dataset which is based on standard back-propagation algorithm. Subsequently, performance and validation of the ANN based models have been measured by relating the predictions with the experimental results. Correspondingly, four different statistical functions have been used to examine the performance and reliability of the ANN based models. Moreover, Garson equation has been used to estimate the relative importance of the four different input variables towards their specific output. The results of the model suggests that, ANN based model is impressively successful to forecast the performance parameters of diesel engines for different input variables with a greater degree of accurateness and to evaluate relative impact of input variables.


Publication status

  • Published

File Version

  • Published version


International Journal of Engineering Research & Technology (IJERT)




ESRSA Publication





Page range


Department affiliated with

  • Engineering and Design Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date


First Open Access (FOA) Date


First Compliant Deposit (FCD) Date