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Novel prediction approach for exhaust gases using Elman neural network combined with particle swarm optimization

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  • Miranda, Matheus H.R.
  • Silva, Fabrício L.
  • Frutuoso, Felipe S.
  • Eckert, Jony J.
  • Oliveira, Mona Lisa M.
  • Silva, Ludmila C.A.

Abstract

The paper introduces a novel approach for predicting vehicle pollutant emissions using Elman artificial neural networks (ENN) trained by the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Three training sets were conducted, including simulated data from the ADVISOR™ software and experimental data collected from a real-world driving cycle with a vehicle fueled with regular gasoline and hydrous ethanol. The main objective is to predict the emissions of hydrocarbons (HC), nitrogen oxides (NOx), and non-methane organic gases (NMOG), minimizing the root mean square error (RMSE) and maximizing the correlation coefficient between estimated and actual values. Results show that the ENN solution with the best trade-off exhibited a difference of less than 1% in emissions prediction during training with ADVISOR™ data. For experimental data, remarkable correlation indices were achieved, reaching up to 0.9932 for NMOG emissions, 0.9975 for NOx, and 0.9926 for HC in gasoline-fueled vehicle. Ethanol-fueled vehicles showed similarly high correlations.

Suggested Citation

  • Miranda, Matheus H.R. & Silva, Fabrício L. & Frutuoso, Felipe S. & Eckert, Jony J. & Oliveira, Mona Lisa M. & Silva, Ludmila C.A., 2025. "Novel prediction approach for exhaust gases using Elman neural network combined with particle swarm optimization," Energy, Elsevier, vol. 331(C).
    • Handle: RePEc:eee:energy:v:331:y:2025:i:c:s0360544225024119
    DOI: 10.1016/j.energy.2025.136769
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    Cited by:

    1. Olga Orynycz & Magdalena Zimakowska-Laskowska & Ewa Kulesza, 2025. "CO 2 Emission and Energy Consumption Estimates in the COPERT Model—Conclusions from Chassis Dynamometer Tests and SANN Artificial Neural Network Models and Their Meaning for Transport Management," Energies, MDPI, vol. 18(13), pages 1-20, July.

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