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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Anand, A.
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Publications (5/5 displayed)
- 2024Advances in Perovskite Nanocrystals and Nanocomposites for Scintillation Applicationscitations
- 2024Epoxy/hollow glass microsphere syntactic foams for structural and functional application - A review
- 2023An open-access database and analysis tool for perovskite solar cells based on the FAIR data principlescitations
- 2022An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles
- 2021Generalized Regression Neural Network (GRNN) for the Prediction of CRDI Engine Responses Fuelled with Pongamia Biodieselcitations
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article
Generalized Regression Neural Network (GRNN) for the Prediction of CRDI Engine Responses Fuelled with Pongamia Biodiesel
Abstract
<jats:p>The application of General Regression Neural Network (GRNN) for the prediction of performance and emission responses of Common Rail Direct Injection (CRDI) engine using B5, B10 and B20 blend of pongamia biodiesel is presented in this paper. Data required for the prediction is obtained through experimentation on CRDI engine by varying parameters like injection pressure, injection timing and fuel preheating temperature. The experiments were conducted based on L9 Taguchi Orthogonal Array (OA). The experimental values for performance parameters like brake thermal efficiency, specific fuel consumption and emission parameters like CO, Nox and HC were recorded and used for GRNN. GRNN model is trained with 70% of samples and is validated with testing dataset of 30% by selecting optimum spread parameter (σ). The proposed model was found to be reliable and provides a cost effective way for determining performance parameters of engine.The results presented in this study substantially promote the use of GRNN model for the prediction of parameters in CRDI engine.</jats:p>