IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i8p2809-d792168.html
   My bibliography  Save this article

Prediction of the Total Base Number (TBN) of Engine Oil by Means of FTIR Spectroscopy

Author

Listed:
  • Artur Wolak

    (Department of Quality and Safety of Industrial Products, Institute of Quality and Product Management Sciences, Cracow University of Economics, 27 Rakowicka St., 31-510 Cracow, Poland)

  • Jarosław Molenda

    (Łukasiewicz Research Network—Institute for Sustainable Technologies, 6/10 Pułaskiego St., 26-600 Radom, Poland)

  • Kamil Fijorek

    (Department of Statistics, College of Economics, Finance and Law, Cracow University of Economics, 27 Rakowicka St., 31-510 Cracow, Poland)

  • Bartosz Łankiewicz

    (Department of Quality and Safety of Industrial Products, Institute of Quality and Product Management Sciences, Cracow University of Economics, 27 Rakowicka St., 31-510 Cracow, Poland)

Abstract

The objective of this study is to develop a statistical model to accurately estimate the total base number (TBN) value of diesel engine oils on the basis of the Fourier transform infrared spectroscopy (FTIR) analysis. The research sample consisted of oils used in the course of 14,820 km. The samples were collected after each 1000 km and both FTIR and TBN measurements were performed. By applying the measured absorbance values, five statistical models aimed at predicting TBN values were elaborated with the use of the following information: aggregated values of measured absorbance in defined spectral ranges, extremes at wavenumbers, or the surface area of spectral bands related to the vibrations of specific molecular structures. The obtained models may be considered a continuation and an extension of previous studies of this type described in the literature on the subject. The results of the study and the analysis of the obtained data have led to the development of two models with high predictive capabilities (R 2 > 0.98, RMSE < 0.5). Another model, which had the smallest number of variables in comparison to other models, had markedly lower R 2 value (0.9496) and the highest RMSE (0.5596). Yet another model, where the dimensionality of the pre-processed full spectra was reduced to four aggregates through averaging, turned out to be slightly worse than the best one (R 2 = 0.9728). The study contributes to a more in-depth understanding of the FTIR-based TBN prediction tools that may be readily available to all interested parties.

Suggested Citation

  • Artur Wolak & Jarosław Molenda & Kamil Fijorek & Bartosz Łankiewicz, 2022. "Prediction of the Total Base Number (TBN) of Engine Oil by Means of FTIR Spectroscopy," Energies, MDPI, vol. 15(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2809-:d:792168
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/8/2809/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/8/2809/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tayari, Sara & Abedi, Reza & Rahi, Abbas, 2020. "Comparative assessment of engine performance and emissions fueled with three different biodiesel generations," Renewable Energy, Elsevier, vol. 147(P1), pages 1058-1069.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Solmaz, Hamit & Ardebili, Seyed Mohammad Safieddin & Calam, Alper & Yılmaz, Emre & İpci, Duygu, 2021. "Prediction of performance and exhaust emissions of a CI engine fueled with multi-wall carbon nanotube doped biodiesel-diesel blends using response surface method," Energy, Elsevier, vol. 227(C).
    2. Dong Lin Loo & Yew Heng Teoh & Heoy Geok How & Jun Sheng Teh & Liviu Catalin Andrei & Slađana Starčević & Farooq Sher, 2021. "Applications Characteristics of Different Biodiesel Blends in Modern Vehicles Engines: A Review," Sustainability, MDPI, vol. 13(17), pages 1-31, August.
    3. Dariusz Kurczyński & Grzegorz Wcisło & Piotr Łagowski, 2021. "Experimental Study of Fuel Consumption and Exhaust Gas Composition of a Diesel Engine Powered by Biodiesel from Waste of Animal Origin," Energies, MDPI, vol. 14(12), pages 1-22, June.
    4. K. M. V. Ravi Teja & P. Issac Prasad & K. Vijaya Kumar Reddy & N. R. Banapurmath & Manzoore Elahi M. Soudagar & Nazia Hossain & Asif Afzal & C Ahamed Saleel, 2021. "Comparative Analysis of Performance, Emission, and Combustion Characteristics of a Common Rail Direct Injection Diesel Engine Powered with Three Different Biodiesel Blends," Energies, MDPI, vol. 14(18), pages 1-19, September.
    5. Małgorzata Hawrot-Paw & Patryk Ratomski & Adam Koniuszy & Wojciech Golimowski & Mirosława Teleszko & Anna Grygier, 2021. "Fatty Acid Profile of Microalgal Oils as a Criterion for Selection of the Best Feedstock for Biodiesel Production," Energies, MDPI, vol. 14(21), pages 1-14, November.
    6. Oyetola Ogunkunle & Noor A. Ahmed, 2021. "Overview of Biodiesel Combustion in Mitigating the Adverse Impacts of Engine Emissions on the Sustainable Human–Environment Scenario," Sustainability, MDPI, vol. 13(10), pages 1-28, May.
    7. Viswanathan, Vinoth Kannan & Kaladgi, Abdul Razak & Thomai, Pushparaj & Ağbulut, Ümit & Alwetaishi, Mamdooh & Said, Zafar & Shaik, Saboor & Afzal, Asif, 2022. "Hybrid optimization and modelling of CI engine performance and emission characteristics of novel hybrid biodiesel blends," Renewable Energy, Elsevier, vol. 198(C), pages 549-567.
    8. Akinola David Ogunsola & Modiu O. Durowoju & Oyetola Ogunkunle & Opeyeolu T. Laseinde & S. M. Ashrafur Rahman & Islam Md Rizwanul Fattah, 2023. "Shea Butter Oil Biodiesel Synthesized Using Snail Shell Heterogeneous Catalyst: Performance and Environmental Impact Analysis in Diesel Engine Applications," Sustainability, MDPI, vol. 15(11), pages 1-16, May.
    9. Jacob, Ashwin & Ashok, B. & Usman, Kaisan Muhammad & Kulla, D.M., 2022. "Influence of post-injection parameters on the performance of continuous regeneration trap to mitigate greenhouse gas and particulate emissions from CI engine," Energy, Elsevier, vol. 248(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2809-:d:792168. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.