Prediction of the main environmental and energy characteristics of diesel engines using an artificial neural network for pure diesel fuel, pure HVO, and a mixture of these fuels in the ratio 50/50 by volume

• Autorzy: Matijošius Jonas , Rimkus Alfredas , Gruodis Alytis

Identyfikatory

DOI

10.20858/tp.2024.19.4.06

• Języki publikacji: EN

Abstrakty

EN

This research assesses the influence of different quantities of hydrotreated vegetable oil (HVO) in diesel fuel on the performance of the engine and the emissions it produces. The particular areas of interest are the level of smoke emitted and the brake thermal efficiency (BTE). A series of engine experiments were undertaken to quantify emissions and performance characteristics under various operating regimes using three distinct fuel blends: D100 (pure diesel), HVO50 (50% HVO mixture), and HVO100 (pure HVO). The results show a tendency to reduce soot emissions when the amount of HVO in the mixture reaches 50%, but in order to accurately determine the correlation between the amount of HVO and emissions, additional studies with various concentrations of HVO and diesel mixtures are necessary. Similarly, the results show a slight improvement in BTE stability with a 50% HVO blend, but more studies with different percentages of HVOdiesel blends are needed to reliably determine changes in BTE. This indicates a trend of change in engine performance with increasing HVO concentration in diesel fuel. In order to forecast emissions and performance indicators under different operating scenarios, we used artificial neural networks ANNs, which demonstrated excellent prediction accuracy, exhibiting robust linear correlations between the expected and real values for all fuel types. This research emphasizes the advantages of utilizing HVO in diesel engines for both environmental impact and performance. It also emphasizes the usefulness of ANNs in optimizing engine settings to improve efficiency and minimize emissions. The results endorse the further use of HVO as a viable substitute for conventional diesel, leading to less ecological consequences and enhanced engine efficiency.

Słowa kluczowe

EN

machine learning; diesel engines; emission prediction; artificial neural networks; fuel variability; environmental regulations; predictive modeling

PL

uczenie maszynowe; silniki wysokoprężne; prognozowanie emisji; sztuczne sieci neuronowe; zmienność paliwa; przepisy dotyczące ochrony środowiska; modelowanie predykcyjne

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Transport Problems
• Rocznik: 2024
• Tom: T. 19, z. 4
• Strony: 69--82
• Opis fizyczny: Bibliogr. 28 poz.

Twórcy

autor

Matijošius Jonas

• Vilnius College of Higher Education, Technical Faculty; Olandu 16, LT-01100 Vilnius, Lithuania

• https://orcid.org/0000-0001-6006-9470

autor

Rimkus Alfredas

• Vilnius College of Higher Education, Technical Faculty; Olandu 16, LT-01100 Vilnius, Lithuania

• https://orcid.org/0000-0001-8995-7180

autor

Gruodis Alytis

• Mykolas Romeris University, Faculty of Public Governance and Business; Ateities 20, LT-08303 Vilnius, Lithuania

• https://orcid.org/0000-0001-8622-597X

Bibliografia

• 1. Aatola, H. & Larmi, M. & Sarjovaara, T. & Mikkonen, S. Hydrotreated Vegetable Oil (HVO) as a renewable diesel fuel: trade-off between NOx, particulate emission, and fuel consumption of a heavy duty engine. SAE Int J Engines. 2008. Vol. 1(1). P. 1251-1262. DOI: 10.4271/2008-01-2500.
• 2. Sunde, K. & Brekke, A. & Solberg, B. Environmental impacts and costs of hydrotreated vegetable oils, transesterified lipids and woody BTL – A Review. Energies. 2011. Vol. 4(6). P. 845-877. DOI: 10.3390/en4060845.
• 3. Bortel, I. & Vávra, J. & Takáts, M. Effect of HVO fuel mixtures on emissions and performance of a passenger car size diesel engine. Renew Energy. 2019. Vol. 140. P. 680-691. DOI: 10.1016/j.renene.2019.03.067.
• 4. Dimitriadis, A. & Natsios, I. & Dimaratos, A. & Katsaounis, D. & Samaras, Z. & Bezergianni, S. & et al. Evaluation of a hydrotreated vegetable oil (HVO) and effects on emissions of a passenger car diesel engine. Front Mech Eng. 2018. Vol. 4. No. 7. DOI: 10.3389/fmech.2018.00007.
• 5. Suarez-Bertoa, R. & Kousoulidou, M. & Clairotte, M. & Giechaskiel, B. & Nuottimäki, J. & Sarjovaara, T. & et al. Impact of HVO blends on modern diesel passenger cars emissions during real world operation. Fuel. 2019. Vol. 235. P. 1427-1435. DOI: 10.1016/j.fuel.2018.08.031.
• 6. Omari, A. & Pischinger, S. & Bhardwaj, O.P. & Holderbaum, B. & Nuottimäki, J. & Honkanen, M. Improving engine efficiency and emission reduction potential of HVO by fuel-specific engine calibration in modern passenger car diesel applications. SAE International Journal of Fuels and Lubricants. 2017. Vol. 10(3). No. 2017-01-2295.
• 7. Aakko-Saksa, P. & Murtonen, T. & Kuronen, M. & Mikkonen, S. Emissions with heavy-duty diesel engines and vehicles using FAME, HVO and GTL fuels with and without DOC+POC aftertreatment. SAE International Journal of Fuels and Lubricants. 2010. Vol. 2(2). P. 147-166. DOI: 10.4271/2009- 01-2693.
• 8. Dimitriadis, A. & Seljak, T. & Vihar, R. & Žvar Baškovič, U. & Dimaratos, A. & Bezergianni, S. & et al. Improving PM-NOx trade-off with paraffinic fuels: A study towards diesel engine optimization with HVO. Fuel. 2020. Vol. 265. No. 116921.
• 9. McCaffery, C. & Zhu, H. & Sabbir Ahmed, C.M. & Canchola, A. & Chen, J.Y. & Li, C. & et al. Effects of hydrogenated vegetable oil (HVO) and HVO/biodiesel blends on the physicochemical and toxicological properties of emissions from an off-road heavy-duty diesel engine. Fuel. 2022. Vol. 323. No. 124283. DOI: 10.1016/j.fuel.2022.124283
• 10. Zoldy, M. & Szalmane Csete, M. & Kolozsi, P.P. & Bordas, P. & Torok, A. Cognitive Sustainability. Cognitive Sustainability. 2022. Vol. 1(1). DOI: 10.55343/cogsust.7.
• 11.Kozak, M. & Lijewski, P. & Fuc, P. Exhaust emissions from a city bus fuelled by oxygenated diesel. Fuel. 2020. No. 2020-01-2095. Available at: https://www.sae.org/content/2020-01-2095/.
• 12. Bereczky, A. Effect of the use of waste vegetable oil based biodiesel on the landscape in diesel engines. Therm Sci. 2017. Vol. 21(1 Part B). P. 567-579.
• 13. Žvirblis, T. & Vainorius, D. & Matijošius, J. & Kilikevičienė, K. & Rimkus, A. & Bereczky, Á. & et al. Engine vibration data increases prognosis accuracy on emission loads: a novel statistical regressions algorithm approach for vibration analysis in time domain. Symmetry. 2021. Vol. 13(7). No. 1234. DOI: 10.3390/sym13071234.
• 14. Žvirblis, T. & Hunicz, J. & Matijošius, J. & Rimkus, A. & Kilikevičius, A. & Gęca, M. Improving diesel engine reliability using an optimal prognostic model to predict diesel engine emissions and performance using pure diesel and hydrogenated vegetable oil. Eksploat Niezawodn – Maint Reliab. 2023. Vol. 25(4). No. 174358. DOI: 10.17531/ein/174358.
• 15. Mlynski, R. & Kozlowski, E. Selection of level-dependent hearing protectors for use in an indoor shooting range. Int J Environ Res Public Health. 2019. Vol. 16(13). No. 2266. DOI: 10.3390/ijerph16132266.
• 16. Kozłowski, E. & Borucka, A. & Świderski, A. Application of the logistic regression for determining transition probability matrix of operating states in the transport systems. Eksploat Niezawodn – Maint Reliab. 2020. Vol. 22(2). P. 192-200. DOI: 10.17531/ein.2020.2.2.
• 17. Borucka, A. & Kozłowski, E. & Parczewski, R. & Antosz, K. & Gil, L. & Pieniak, D. Supply sequence modelling using Hidden Markov Models. Appl Sci. 2022. Vol. 13(1). No. 231. DOI: 10.3390/app13010231.
• 18. Yang, R. & Yan, Y. & Sun, X. & Wang, Q. & Zhang, Y. & Fu, J. & et al. An artificial neural network model to predict efficiency and emissions of a gasoline engine. Processes. 2022. Vol. 10(2). No. 204. DOI: 10.3390/pr10020204.
• 19. Buscema, M. Back Propagation Neural Networks. Subst Use Misuse. 1998. Vol. 33(2). P. 233-270.
• 20. Sekhar Ch. & Meghana P.S. A study on backpropagation in artificial neural networks. Asia-Pac J Neural Netw Its Appl. 2020. Vol. 4(1). P. 21-28.
• 21. Murat, H.S. A brief review of feed-forward neural networks. Commun Fac Sci Univ Ank. 2006. Vol. 50(1). P. 11-17.
• 22. Zhang, Z. & Feng, F. & Huang, T. FNNS: An effective feedforward neural network scheme with random weights for processing large-scale datasets. Appl Sci. 2022. Vol. 12(23). No. 12478. DOI: 10.3390/app122312478.
• 23. Karunamurthy, K. & Janvekar, A.A. & Palaniappan, P.L. & Adhitya, V. & Lokeswar, T.T.K. & Harish, J. Prediction of IC engine performance and emission parameters using machine learning: A review. J Therm Anal Calorim. 2023. Vol. 148(9). P. 3155-3177.
• 24. Gruodis, A. Realizations of the artificial neural network for process modeling. overview of current implementations. Appl Bus Issues Solut. 2023. Vol. 2. P. 22-27. DOI: 10.57005/ab.2023.2.3.
• 25. Gruodis, Alytis. VALLUM01. Advanced tool for implementation of Artificial Neural Network containing tabular interface for input/output. Vilnius, Lithuania; 2023.
• 26. Gruodis, A. Advanced Tool for Implementation of Artificial Neural Network Containing Tabular Interface for Input/Output 2023. Vilnius, Lithuania. 2024. Available at: https://github.com/solo51/VALLUM.
• 27. Matijošius, J. & Rimkus, A. & Gruodis, A. Validation challenges in data for different diesel engine performance regimes utilising HVO fuel: a study on the application of artificial neural networks for emissions prediction. Machines. 2024. Vol. 12(4). No. 279. DOI: 10.3390/machines12040279.
• 28. Matijošius, J. & Rimkus, A. & Gruodis, A. Validation of ecology and energy parameters of diesel exhausts using different fuel mixtures, consisting of hydrogenated vegetable oil and diesel fuels, presented at real market: approaches using artificial neural network for large-scale predictions. Machines. 2024. Vol. 12(6). No. 353. DOI: 10.3390/machines12060353.