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Multi-objective Design of Blending Fuel by Intelligent Optimization Algorithms

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Abstract

Fuel design is a complex multi-objective optimization problem in which facile and robust methods are urgently demanded. Herein, a complete workflow for designing a fuel blending scheme is presented, which is theoretically supported, efficient, and reliable. Based on the data distribution of the composition and properties of the blending fuels, a model of polynomial regression with appropriate hypothesis space was established. The parameters of the model were further optimized by different intelligence algorithms to achieve high-precision regression. Then, the design of a blending fuel was described as a multi-objective optimization problem, which was solved using a Nelder–Mead algorithm based on the concept of Pareto domination. Finally, the design of a target fuel was fully validated by experiments. This study provides new avenues for designing various blending fuels to meet the needs of next-generation engines.

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Acknowledgements

The authors appreciate the support from the National Key R&D Program of China (No. 2021YFC2103701), the National Natural Science Foundation of China (No. 22178248) and the Haihe Laboratory of Sustainable Chemical Transformations.

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Authors and Affiliations

  1. Key Laboratory for Green Chemical Technology of Ministry of Education, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China

    Ruichen Liu, Cong Li, Li Wang, Xiangwen Zhang & Guozhu Li

  2. Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China

    Li Wang, Xiangwen Zhang & Guozhu Li

  3. Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China

    Li Wang, Xiangwen Zhang & Guozhu Li

Authors
  1. Ruichen Liu
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  2. Cong Li
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  3. Li Wang
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  4. Xiangwen Zhang
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  5. Guozhu Li
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Correspondence to Guozhu Li.

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Liu, R., Li, C., Wang, L. et al. Multi-objective Design of Blending Fuel by Intelligent Optimization Algorithms. Trans. Tianjin Univ. (2024). https://doi.org/10.1007/s12209-024-00393-2

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