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Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine

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  • Simsek, Suleyman
  • Uslu, Samet
  • Simsek, Hatice

Abstract

Instead of many experimental studies made for the suitability of biodiesel for use in diesel engine, it has become easier to determine by fewer experiments with the development of computer applications. In this research, it was aimed to determine the optimum ratio of animal waste fat biodiesel (AWFBD) and the corresponding engine responses by using artificial neural network (ANN) and response surface methodology (RSM). In addition, a comparison was made with test results to evaluate the performance of ANN and RSM. According to the regression results obtained from RSM, absolute fraction of variance (R2) values greater than 0.95 emerged for all answers. Correlation coefficient (R) values obtained from ANN were found to be higher than 0.97. The developed ANN model was able to predict engine responses with mean absolute percentage error (MAPE) in the range of 3.787–10.730%. MAPE values for RSM were obtained between 2.004 and 11.461%. Combined desirability factor obtained from RSM was found as 0.72288% and optimum engine parameters were found as 22% AWFBD ratio and 1350-Watt engine load. In addition, according to the verification test between the optimum results and the prediction results, it was concluded that there is a good agreement with a maximum error rate of 3.863%.

Suggested Citation

  • Simsek, Suleyman & Uslu, Samet & Simsek, Hatice, 2022. "Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine," Energy, Elsevier, vol. 239(PD).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pd:s0360544221026384
    DOI: 10.1016/j.energy.2021.122389
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    5. Raj, Reetu & Tirkey, Jeewan Vachan & Jena, Priyaranjan & Prajapati, Lawalesh Kumar, 2024. "Comparative analysis of Gasifier-CI engine performance and emissions characteristics using diesel with producer gas derived from coal– briquette-coconut shell-mahua feedstock and its blends," Energy, Elsevier, vol. 293(C).
    6. 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.
    7. Uslu, Samet & Celik, Mehmet, 2023. "Response surface methodology-based optimization of the amount of cerium dioxide (CeO2) to increase the performance and reduce emissions of a diesel engine fueled by cerium dioxide/diesel blends," Energy, Elsevier, vol. 266(C).
    8. Channapattana, Shylesha V. & Campli, Srinidhi & Madhusudhan, A. & Notla, Srihari & Arkerimath, Rachayya & Tripathi, Mukesh Kumar, 2023. "Energy analysis of DI-CI engine with nickel oxide nanoparticle added azadirachta indica biofuel at different static injection timing based on exergy," Energy, Elsevier, vol. 267(C).
    9. El-Shafay, A.S. & Gad, M.S. & Ağbulut, Ümit & Attia, El-Awady, 2023. "Optimization of performance and emission outputs of a CI engine powered with waste fat biodiesel: A detailed RSM, fuzzy multi-objective and MCDM application," Energy, Elsevier, vol. 275(C).
    10. 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).

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