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Prediction of exhaust emission in transient conditions of a diesel engine fueled with animal fat using Artificial Neural Network and Symbolic Regression

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  • Domínguez-Sáez, Aida
  • Rattá, Giuseppe A.
  • Barrios, Carmen C.

Abstract

The objective of this study is the development and evaluation of two models to predict instantaneous exhaust emissions of CO2, NOx, particle number concentration and geometric mean diameter in accumulation mode (30–560 nm) and in nucleation mode (5.6–30 nm) of a 2.0 euro 4 diesel engine fueled with pure diesel and animal fat in different proportions. To acquire data for training, validation and testing, 4 repetitions of the urban part of the New European Driving Cycle and 5 steady-state conditions (15, 30, 50, 70 and 100 km/h) were reproduced in a dynamic engine test bench. The used prediction models were Artificial Neural Networks and Symbolic Regression. Vehicle speed and acceleration, engine speed and torque, air intake temperature, boost pressure, mass air flow and fuel consumption were used as inputs variables. Artificial Neural Networks provided a R2 for testing dataset equal to 0.91, 0.78, 0.87 and 0.81 for CO2, NOx, number of particles in accumulation mode and geometric mean diameter, respectively. Symbolic regression showed a R2 of 0.91, 0.82, 0.87 and 0.82 for the mentioned pollutants. Particle number concentration in nucleation mode presents low correlation with the considered inputs due to the variability of the formation process of this particle mode.

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  • Domínguez-Sáez, Aida & Rattá, Giuseppe A. & Barrios, Carmen C., 2018. "Prediction of exhaust emission in transient conditions of a diesel engine fueled with animal fat using Artificial Neural Network and Symbolic Regression," Energy, Elsevier, vol. 149(C), pages 675-683.
    • Handle: RePEc:eee:energy:v:149:y:2018:i:c:p:675-683
    DOI: 10.1016/j.energy.2018.02.080
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    References listed on IDEAS

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    5. Zhihong Wang & Kai Feng, 2024. "NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural Network," Energies, MDPI, vol. 17(2), pages 1-23, January.
    6. Liang, Xiao & Zhang, Tianyu & Xie, Meiquan & Jia, Xudong, 2021. "Analyzing bicycle level of service using virtual reality and deep learning technologies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 115-129.
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    8. El-Shafay, A.S. & Gad, M.S. & Ağbulut, Ümit & Attia, El-Awady, 2023. "Optimization of performance and emission outputs of a CI engine powered with waste fat biodiesel: A detailed RSM, fuzzy multi-objective and MCDM application," Energy, Elsevier, vol. 275(C).

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