IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i2p788-d1571144.html
   My bibliography  Save this article

Significant Research on Sustainable Oxygenated Fuel for Compression Ignition Engines with Controlled Emissions and Optimum Performance Prediction Using Artificial Neural Network

Author

Listed:
  • Javed Syed

    (Department of Mechanical Engineering, King Khalid University, Abha 61421, Saudi Arabia)

Abstract

The present work compares the performance and emissions of a compression ignition (CI) engine using dual-mode LPG at varying flow rates and an oxygenated biodiesel mix (B20). The experimental investigation is carried out on LPG flow rates (0.1, 0.3, and 0.5 kg/h) and replacing the diesel with oxygenated B20, affecting engine performance and emissions under various load circumstances while maintaining engine speed. The study demonstrates the potential of the artificial neural network (ANN) in accurately forecasting the performance and emission characteristics of the engine across different operating conditions. The ANN model’s high accuracy in correlating experimental results with predicted outcomes underscores its potential as a dependable instrument for optimizing fuel parameters. The results show that LPG and oxygenated B20 balance engine performance and emissions, making CI engine functionality sustainable. A biodiesel blend containing diethyl ether (B20 + 2%DEE) exhibits slightly reduced brake thermal efficiency (BTE) at lower brake power (BP); however, it demonstrates advantages at higher BP, with diethyl ether contributing to improved ignition quality. The analysis indicates that the average NO x emissions for B20 + 2%DEE at flow rates of 0.1 kg/h, 0.3 kg/h, and 0.5 kg/h are 29.33%, 28.89%, 48.05%, and 37.48%, respectively. Consequently, selecting appropriate fuel and regulating the LPG flow rate is critical for enhancing thermal efficiency in a dual-fuel engine.

Suggested Citation

  • Javed Syed, 2025. "Significant Research on Sustainable Oxygenated Fuel for Compression Ignition Engines with Controlled Emissions and Optimum Performance Prediction Using Artificial Neural Network," Sustainability, MDPI, vol. 17(2), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:2:p:788-:d:1571144
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/2/788/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/2/788/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Mallesh B. Sanjeevannavar & Nagaraj R. Banapurmath & V. Dananjaya Kumar & Ashok M. Sajjan & Irfan Anjum Badruddin & Chandramouli Vadlamudi & Sanjay Krishnappa & Sarfaraz Kamangar & Rahmath Ulla Baig &, 2023. "Machine Learning Prediction and Optimization of Performance and Emissions Characteristics of IC Engine," Sustainability, MDPI, vol. 15(18), pages 1-30, September.
    3. Zandie, Mohammad & Ng, Hoon Kiat & Gan, Suyin & Muhamad Said, Mohd Farid & Cheng, Xinwei, 2023. "Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends," Energy, Elsevier, vol. 262(PA).
    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. Zhu, Yizi & He, Zhixia & Xuan, Tiemin & Shao, Zhuang, 2024. "An enhanced automated machine learning model for optimizing cycle-to-cycle variation in hydrogen-enriched methanol engines," Applied Energy, Elsevier, vol. 362(C).
    2. Li, Ji & Zhou, Quan & He, Xu & Chen, Wan & Xu, Hongming, 2023. "Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives," Energy, Elsevier, vol. 272(C).
    3. Podlasek, Szymon & Jankowski, Marcin & Bałazy, Patryk & Lalik, Krzysztof & Figaj, Rafał, 2024. "Application of ANN control algorithm for optimizing performance of a hybrid ORC power plant," Energy, Elsevier, vol. 306(C).
    4. Cesar de Lima Nogueira, Silvio & Och, Stephan Hennings & Moura, Luis Mauro & Domingues, Eric & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2023. "Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering," Energy, Elsevier, vol. 280(C).
    5. Aliakbari, Karim & Ebrahimi-Moghadam, Amir & Pahlavanzadeh, Mohammadsadegh & Moradi, Reza, 2023. "Performance characteristics and exhaust emissions of a single-cylinder diesel engine for different fuels: Experimental investigation and artificial intelligence network," Energy, Elsevier, vol. 284(C).

    More about this item

    Keywords

    oxygenated fuel additive; B20; LPG dual mode; ANN; performance and emissions;
    All these keywords.

    JEL classification:

    • B20 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - General

    Statistics

    Access and download statistics

    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:jsusta:v:17:y:2025:i:2:p:788-:d:1571144. 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.