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Integrating process optimization with energy-efficiency scheduling to save energy for paper mills

Author

Listed:
  • Zeng, Zhiqiang
  • Hong, Mengna
  • Li, Jigeng
  • Man, Yi
  • Liu, Huanbin
  • Li, Zeeman
  • Zhang, Huanhuan

Abstract

With the surging energy price and environmental concerns, measures to improve energy efficiency have attracted increasing concerns of the manufacture sector, especially energy-intensive manufacturing industries such as tissue paper mills. Energy-efficiency scheduling, as a novel energy-efficient method, has attracted the attention of an increasing number of researchers in recent years. Drying process is the most energy-intensive production process in tissue paper mills, which has a great energy-saving potential. This paper aims to reduce the energy costs for the tissue paper mill, consisting of processing energy cost and set-up energy cost, through integrating drying process optimization with energy-efficient scheduling. First, the energy cost model and the scheduling model were built. Then, the energy cost of the drying process of every job in a given scheduling problem was optimized using particle swarm optimization (PSO). Afterwards, the energy cost was further optimized using energy-efficiency scheduling. In addition, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) was utilized to solve the energy-efficiency scheduling problem. Finally, several real scheduling problems from a real tissue paper mill were addressed using the proposed approach to demonstrate its effectiveness in energy saving. The experiment result showed that there is a great energy-saving potential in the drying process, accounting for up to 12.53% of the total energy consumption. Moreover, the maximum energy saving ratio of the proposed approach could reach 9.03%. On the whole, the proposed approach can provide a new energy-saving method for tissue paper mills or other manufacturing industries.

Suggested Citation

  • Zeng, Zhiqiang & Hong, Mengna & Li, Jigeng & Man, Yi & Liu, Huanbin & Li, Zeeman & Zhang, Huanhuan, 2018. "Integrating process optimization with energy-efficiency scheduling to save energy for paper mills," Applied Energy, Elsevier, vol. 225(C), pages 542-558.
    • Handle: RePEc:eee:appene:v:225:y:2018:i:c:p:542-558
    DOI: 10.1016/j.apenergy.2018.05.051
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    References listed on IDEAS

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    1. Herre, Lars & Tomasini, Federica & Paridari, Kaveh & Söder, Lennart & Nordström, Lars, 2020. "Simplified model of integrated paper mill for optimal bidding in energy and reserve markets," Applied Energy, Elsevier, vol. 279(C).
    2. Hannan, M.A. & Lipu, M.S. Hossain & Ker, Pin Jern & Begum, R.A. & Agelidis, Vasilios G. & Blaabjerg, F., 2019. "Power electronics contribution to renewable energy conversion addressing emission reduction: Applications, issues, and recommendations," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Bonilla-Campos, Iñigo & Nieto, Nerea & del Portillo-Valdes, Luis & Manzanedo, Jaio & Gaztañaga, Haizea, 2020. "Energy efficiency optimisation in industrial processes: Integral decision support tool," Energy, Elsevier, vol. 191(C).
    4. do Carmo, Pedro R.X. & do Monte, João Victor L. & Filho, Assis T. de Oliveira & Freitas, Eduardo & Tigre, Matheus F.F.S.L. & Sadok, Djamel & Kelner, Judith, 2023. "A data-driven model for the optimization of energy consumption of an industrial production boiler in a fiber plant," Energy, Elsevier, vol. 284(C).
    5. Nejad, Alireza Mahdavi, 2021. "A new drying approach deploying solid-solid phase change material: A numerical study," Energy, Elsevier, vol. 232(C).
    6. Zhou, Shengchao & Jin, Mingzhou & Du, Ni, 2020. "Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times," Energy, Elsevier, vol. 209(C).
    7. Saeed Solaymani, 2020. "A CO2 emissions assessment of the green economy in Iran," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(2), pages 390-407, April.
    8. Giuntini, Lorenzo & Lamioni, Rachele & Linari, Luca & Saccomano, Pietro & Mainardi, Davide & Tognotti, Leonardo & Galletti, Chiara, 2022. "Decarbonization of a tissue paper plant: Advanced numerical simulations to assess the replacement of fossil fuels with a biomass-derived syngas," Renewable Energy, Elsevier, vol. 198(C), pages 884-893.
    9. Ximei Li & Jianmin Gao & Yaning Zhang & Yu Zhang & Qian Du & Shaohua Wu & Yukun Qin, 2020. "Energy, Exergy and Economic Analyses of a Combined Heating and Power System with Turbine-Driving Fans and Pumps in Northeast China," Energies, MDPI, vol. 13(4), pages 1-22, February.
    10. Ding, Yan & Wang, Qiaochu & Kong, Xiangfei & Yang, Kun, 2019. "Multi-objective optimisation approach for campus energy plant operation based on building heating load scenarios," Applied Energy, Elsevier, vol. 250(C), pages 1600-1617.
    11. Li, Lei & Huang, Haihong & Zou, Xiang & Zhao, Fu & Li, Guishan & Liu, Zhifeng, 2021. "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, Elsevier, vol. 286(C).
    12. Panda, Debashish & Ramteke, Manojkumar, 2019. "Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 68-82.
    13. 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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