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Energy Modeling of a Refiner in Thermo-Mechanical Pulping Process Using ANFIS Method

Author

Listed:
  • Behnam Talebjedi

    (Department of Mechanical Engineering, School of Engineering, Aalto University, 14400 Espoo, Finland)

  • Ali Khosravi

    (Department of Mechanical Engineering, School of Engineering, Aalto University, 14400 Espoo, Finland)

  • Timo Laukkanen

    (Department of Mechanical Engineering, School of Engineering, Aalto University, 14400 Espoo, Finland)

  • Henrik Holmberg

    (Department of Mechanical Engineering, School of Engineering, Aalto University, 14400 Espoo, Finland)

  • Esa Vakkilainen

    (Department of Energy, Lappeenranta University of Technology, 95992 Lappeenranta, Finland)

  • Sanna Syri

    (Department of Mechanical Engineering, School of Engineering, Aalto University, 14400 Espoo, Finland)

Abstract

In the pulping industry, thermo-mechanical pulping (TMP) as a subdivision of the refiner-based mechanical pulping is one of the most energy-intensive processes where the core of the process is attributed to the refining process. In this study, to simulate the refining unit of the TMP process under different operational states, the idea of machine learning algorithms is employed. Complicated processes and prediction problems could be simulated and solved by utilizing artificial intelligence methods inspired by the pattern of brain learning. In this research, six evolutionary optimization algorithms are employed to be joined with the adaptive neuro-fuzzy inference system (ANFIS) to increase the refining simulation accuracy. The applied optimization algorithms are particle swarm optimization algorithm (PSO), differential evolution (DE), biogeography-based optimization algorithm (BBO), genetic algorithm (GA), ant colony (ACO), and teaching learning-based optimization algorithm (TLBO). The simulation predictor variables are site ambient temperature, refining dilution water, refining plate gap, and chip transfer screw speed, while the model outputs are refining motor load and generated steam. Findings confirm the superiority of the PSO algorithm concerning model performance comparing to the other evolutionary algorithms for optimizing ANFIS method parameters, which are utilized for simulating a refiner unit in the TMP process.

Suggested Citation

  • Behnam Talebjedi & Ali Khosravi & Timo Laukkanen & Henrik Holmberg & Esa Vakkilainen & Sanna Syri, 2020. "Energy Modeling of a Refiner in Thermo-Mechanical Pulping Process Using ANFIS Method," Energies, MDPI, vol. 13(19), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5113-:d:422541
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    References listed on IDEAS

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    Cited by:

    1. Behnam Talebjedi & Timo Laukkanen & Henrik Holmberg & Esa Vakkilainen & Sanna Syri, 2021. "Energy Efficiency Analysis of the Refining Unit in Thermo-Mechanical Pulp Mill," Energies, MDPI, vol. 14(6), pages 1-18, March.
    2. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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