IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i4p768-d1585737.html

Artificial Intelligence and Nature-Inspired Techniques on Optimal Biodiesel Production: A Review—Recent Trends

Author

Listed:
  • Christos Kyriklidis

    (Chemical Engineering Department, University of Western Macedonia, Kila, GR-50100 Kozani, Greece)

  • Aikaterini Koutouvou

    (Informatics Department, University of Western Macedonia, Fourka, GR-52100 Kastoria, Greece)

  • Konstantinos Moustakas

    (School of Chemical Engineering, National Technical University of Athens, Zografou Campus, GR-15773 Athens, Greece)

  • Vayos Karayannis

    (Chemical Engineering Department, University of Western Macedonia, Kila, GR-50100 Kozani, Greece)

  • Constantinos Tsanaktsidis

    (Chemical Engineering Department, University of Western Macedonia, Kila, GR-50100 Kozani, Greece)

Abstract

Humanity has consumed large amounts of energy in recent decades. Energy requirements increase continuously, and fossil fuel overuse pollutes the environment extremely. The researchers turned their attention to alternative solutions, such as renewable sources of fuels, which reduce negative emissions. At the same time, biodiesel is produced from environmentally friendly raw materials and is a competitive fuel with acceptable properties. The scientific community investigates new approaches to further improve the physicochemical properties of biodiesel in more economical ways. Artificial intelligence and nature-inspired techniques are particularly capable of searching for optimal fuels in complex optimization fields. The current study concerns a recent review of biodiesel production approaches based on evolutionary computation methods. These methods lead to innovative biodiesel development, costing less with lower sulfur content. Except for the economic profits, the reduction of environmental emissions in praxis confirms biodiesel appropriateness for more consumption than fossil blends. The algorithms’ accuracy and effectiveness were evaluated in various case studies and detailed results were offered in every publication. The optimal fuels are produced in laboratories and tested in common engines too. In the literature, there exists a gap in relation to the financial and environmental aspects of biodiesel fuel production, which should also be investigated.

Suggested Citation

  • Christos Kyriklidis & Aikaterini Koutouvou & Konstantinos Moustakas & Vayos Karayannis & Constantinos Tsanaktsidis, 2025. "Artificial Intelligence and Nature-Inspired Techniques on Optimal Biodiesel Production: A Review—Recent Trends," Energies, MDPI, vol. 18(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:768-:d:1585737
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/4/768/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/4/768/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Xiao-Man & Zeng, Ya-Nan & Wang, Yu-Ran & Wang, Fu-Ping & Wang, Yi-Tong & Li, Jun-Guo & Ji, Rui & Kang, Le-Le & Yu, Qing & Liu, Tian-Ji & Fang, Zhen, 2023. "A novel strategy for efficient biodiesel production: Optimization, prediction, and mechanism," Renewable Energy, Elsevier, vol. 207(C), pages 385-397.
    2. Suvarna, Manu & Jahirul, Mohammad Islam & Aaron-Yeap, Wai Hung & Augustine, Cheryl Valencia & Umesh, Anushri & Rasul, Mohammad Golam & Günay, Mehmet Erdem & Yildirim, Ramazan & Janaun, Jidon, 2022. "Predicting biodiesel properties and its optimal fatty acid profile via explainable machine learning," Renewable Energy, Elsevier, vol. 189(C), pages 245-258.
    3. Can, Özer & Baklacioglu, Tolga & Özturk, Erkan & Turan, Onder, 2022. "Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel," Energy, Elsevier, vol. 247(C).
    4. Corral-Bobadilla, Marina & Lostado-Lorza, Rubén & Sabando-Fraile, Celia & Íñiguez-Macedo, Saúl, 2024. "An artificial intelligence approach to model and optimize biodiesel production from waste cooking oil using life cycle assessment and market dynamics analysis," Energy, Elsevier, vol. 307(C).
    5. Jisieike, Chiazor Faustina & Ishola, Niyi Babatunde & Latinwo, Lekan M. & Betiku, Eriola, 2023. "Crude rubber seed oil esterification using a solid catalyst: Optimization by hybrid adaptive neuro-fuzzy inference system and response surface methodology," Energy, Elsevier, vol. 263(PB).
    6. Soltani, Soroush & Roodbar Shojaei, Taha & Khanian, Nasrin & Shean Yaw Choong, Thomas & Asim, Nilofar & Zhao, Yue, 2022. "Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst," Renewable Energy, Elsevier, vol. 187(C), pages 760-773.
    7. Impha Yalagudige Dharmegowda & Lakshmidevamma Madarakallu Muniyappa & Parameshwara Siddalingaiah & Ajith Bintravalli Suresh & Manjunath Patel Gowdru Chandrashekarappa & Chander Prakash, 2022. "MgO Nano-Catalyzed Biodiesel Production from Waste Coconut Oil and Fish Oil Using Response Surface Methodology and Grasshopper Optimization," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lopes da Silva, Diego Samuel & Brandão de Araújo, Antonio Carlos, 2025. "Continuous reactor metamodel: Optimization of biodiesel reaction systems using machine learning techniques," Renewable Energy, Elsevier, vol. 253(C).

    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. Jaiswal, Krishna Kumar & Dutta, Swapnamoy & Banerjee, Ishita & Jaiswal, Km Smriti & Renuka, Nirmal & Ratha, Sachitra Kumar & Jaiswal, Amit K., 2024. "Valorization of fish processing industry waste for biodiesel production: Opportunities, challenges, and technological perspectives," Renewable Energy, Elsevier, vol. 220(C).
    2. Christopher Wissuchek & Patrick Zschech, 2025. "Prescriptive analytics systems revised: a systematic literature review from an information systems perspective," Information Systems and e-Business Management, Springer, vol. 23(2), pages 279-353, June.
    3. Chaudhary, Anu & Rathour, Ranju Kumari & Solanki, Preeti & Mehta Kakkar, Preeti & Pathania, Shruti & Walia, Abhishek & Baadhe, Rama Raju & Bhatia, Ravi Kant, 2025. "Recent technological advancements in biomass conversion to biofuels and bioenergy for circular economy roadmap," Renewable Energy, Elsevier, vol. 244(C).
    4. Zhu, Zhiheng & Sui, Meng & Duan, Yaozong & Zhang, Huicong & Li, Fashe & Wang, Hua, 2025. "An experimental and ReaxFF-MD simulation study on effects of ferrocene on combustion characteristics of biodiesel-diesel blends," Energy, Elsevier, vol. 329(C).
    5. Luo, Juan & Ma, Rui & Lin, Junhao & Sun, Shichang & Gong, Guojin & Sun, Jiaman & Chen, Yi & Ma, Ning, 2023. "Review of microwave pyrolysis of sludge to produce high quality biogas: Multi-perspectives process optimization and critical issues proposal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    6. Sun, Ping & Zhang, Jufang & Dong, Wei & Li, Decheng & Yu, Xiumin, 2023. "Prediction of oxygen-enriched combustion and emission performance on a spark ignition engine using artificial neural networks," Applied Energy, Elsevier, vol. 348(C).
    7. Arévalo, Paul & Cano, Antonio & Jurado, Francisco, 2024. "Large-scale integration of renewable energies by 2050 through demand prediction with ANFIS, Ecuador case study," Energy, Elsevier, vol. 286(C).
    8. 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).
    9. Muxin Nian & Rui Dong & Weihuang Zhong & Yunhua Zhang & Diming Lou, 2025. "Multi-Objective Optimization Study on Capture Performance of Diesel Particulate Filter Based on the GRA-MLR-WOA Hybrid Method," Sustainability, MDPI, vol. 17(19), pages 1-21, September.
    10. Gkousis, Spiros & Vasilaki, Vasileia & Katsou, Evina, 2026. "Machine learning and large language models for life cycle inventory compilation: Current situation and future developments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 228(C).
    11. Jisieike, Chiazor Faustina & Betiku, Eriola & Ojumu, Tunde V. & Latinwo, Lekan M., 2025. "Rubber seed oil biodiesel synthesis via ethanolysis using a blend of four biogenic residues as a sustainable heterogeneous catalyst," Renewable Energy, Elsevier, vol. 252(C).
    12. Wang, Huaiyu & Ji, Changwei & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan, 2022. "Towards a comprehensive optimization of the intake characteristics for side ported Wankel rotary engines by coupling machine learning with genetic algorithm," Energy, Elsevier, vol. 261(PB).
    13. Jisieike, Chiazor Faustina & Ishola, Niyi Babatunde & Latinwo, Lekan M. & Betiku, Eriola, 2023. "Crude rubber seed oil esterification using a solid catalyst: Optimization by hybrid adaptive neuro-fuzzy inference system and response surface methodology," Energy, Elsevier, vol. 263(PB).
    14. Tang, Yan & Jiang, Yizhuo & Hu, Yang & Cheng, Yunpei, 2026. "Carbon emission reduction potential analysis of biodiesel production from microalgae and waste cooking oil under different SSPs scenarios in China-based on GRA-CNN-BiLSTM modeling," Renewable Energy, Elsevier, vol. 258(C).
    15. Karatuğ, Çağlar & Tadros, Mina & Ventura, Manuel & Soares, C. Guedes, 2024. "Decision support system for ship energy efficiency management based on an optimization model," Energy, Elsevier, vol. 292(C).
    16. 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).
    17. Chen, Mingfei & Zhou, Kaile & Liu, Dong, 2024. "Machine learning based technique for outlier detection and result prediction in combustion diagnostics," Energy, Elsevier, vol. 290(C).
    18. Duan, Xiaoling & Yan, Su & Li, Yun & Fang, Si & Tie, Xinlong & Wang, Hongyan & Liu, Jiang & Zou, Chong & Wang, Tielin & Feng, Weiliang, 2025. "Preparation of chromium-doped ZIFs-8/ZnCo-LDH composite as a acid-base bifunctional catalyst for biodiesel production," Renewable Energy, Elsevier, vol. 238(C).
    19. Huang, Congzhi & Li, Zhuoyong, 2023. "Data-driven modeling of ultra-supercritical unit coordinated control system by improved transformer network," Energy, Elsevier, vol. 266(C).
    20. 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

    ;
    ;
    ;
    ;
    ;

    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:jeners:v:18:y:2025:i:4:p:768-:d:1585737. 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.