International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064




Downloads: 130

Research Paper | Mechanical Engineering | India | Volume 5 Issue 1, January 2016


Prediction of Engine Emissions Characteristics by Using Radial Basis Function Neural Networks (RBFNN)

R. Ramachandra, V. Pandurangadu


Abstract: Biofuels tend to be environment friendly and also the utilization address worldwide problems about containment connected with CO2 emissions. Biodiesel accreditation entails considering these emissions pertaining to numerous biodiesel integrates in order to certify & propose the brand new gas to move segment. Completing the particular findings pertaining to emission examination is usually boring and also difficult. Modeling the particular emissions operated pertaining to numerous biodiesel integrates may help biodiesel producers and also accreditation specialists inside considering the particular doable pollutant levels. Synthetic sensory systems (ANN) can be utilized inside modelling and also conjecture connected with biodiesel emissions operated under varying functioning disorders. The goal of this specific analysis do the job is always to design some sort of neuro research style to analyze the particular intricate technique of diesel engine motor emissions formation and also estimation wear out emissions operated with biodiesels under changing functioning disorders. Experimental information of the sole tube 4 heart stroke diesel engine motor operate with numerous biodiesel integrates continues to be used by training the particular network. While in testing period, emissions tend to be predicted pertaining to brand new biodiesel & it's integrates. ANN developed is based on radial basis function operate neural network (RBFNN). Predictive potential of this sensory network is usually assessed applying record examination. This developed style shows increased coefficient connected with coefficient of determination (CoD) valuations connected with 0.99, 0.99, 0.96, 0.98 and also 0.97 pertaining to NOx, HC, CORP, CO2 and also Smoke emissions respectively. These types of final results show in which radial groundwork operate sensory systems tend to be superior to the more common rear distribution algorithm centered multiple stratum sensory systems with regards to exactness and also performance effectiveness. With this do the job, conjecture & emission modelling connected with diesel engine motor operated with diverse biodiesel integrates under varying functioning disorders is usually productively demonstrated. That's why, RBFNN can be utilized as being a powerful exclusive sensing technological know-how tool pertaining to prediction & modelling connected with biodiesel emissions.


Keywords: ANN, biodiesel, radial basis neural network system, coefficient connected with Determination, MAPE


Edition: Volume 5 Issue 1, January 2016,


Pages: 307 - 312


How to Cite this Article?

R. Ramachandra, V. Pandurangadu, "Prediction of Engine Emissions Characteristics by Using Radial Basis Function Neural Networks (RBFNN)", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=NOV152746, Volume 5 Issue 1, January 2016, 307 - 312

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