Prediction of heat energy from the naturally aspirated internal combustion engine exhaust gas using artificial neural network

Herawan, Safarudin Gazali; Talib, Kamarulhelmy; Putra, Azma

doi:10.1016/j.procs.2018.08.174

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…19 The network was trained by altering the number of neurons in the hidden layer, whereas four different parameters were taken as the input. Heat energy prediction from the exhaust of an internal combustion engine was studied by Herawan et al 20 using throttle angle, engine speed, airflow, and exhaust temperature as the input parameters. They predicted the waste heat at the exhaust while collecting the input data through an experimental setup.…”

Section: Introductionmentioning

confidence: 99%

Power prediction of waste heat recovery system for a cement plant using back propagation neural network and its thermodynamic modeling

et al. 2021

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This work targets on the prediction of power produced by a waste heat recovery (WHR) system for a cement plant using the back-propagation neural network (BPNN) and comparing its results with the actual cycle. A regressionbased predictive model is developed to compare it with an actual WHR cycle that is analyzed thermodynamically. The predictive model is trained using the following parameters as input features: turbine inlet steam, pressure, temperature, and mass flow rate of both stages (i.e., high-pressure (HP) and lowpressure (LP) stages). The mean square error for the validation dataset is found to be 0.283 with 10 neurons in the hidden layer. Moreover, the significance of each parameter on the predicted power investigated shows that the HP stage parameters tend to have more impact on the power generated. The other half of the study comprises energy analysis of the power plant and a detailed parametric analysis to review the impact of each parameter on the power produced and thermal efficiency. The calculated thermal efficiency and generated power of the system are found to be 19.75% and 10.06 MW, respectively. Lastly, from the comparative study of the predictive model and the actual WHR simulated cycle, it is concluded that data science can be used as an alternative to thermodynamic modeling to avoid hefty calculations.

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Section: Introductionmentioning

confidence: 99%