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Analysis of energy saving and emission reduction of secondary fiber mill based on data mining

    Song Hu Affiliation
    ; Jigeng Li Affiliation
    ; Mengna Hong Affiliation
    ; Yi Man Affiliation

Abstract

Waste paper recycling is an important way to realize the environmental protection development of the papermaking industry. The quality of the pulp will affect the pulp sales of the secondary fiber paper mills. The waste paper pulp can be adjusted by controlling the pulping process working conditions, but the working conditions of the waste paper pulping process have too many parameters. And the parameters are coupled with each other, it is difficult to control. In order to find the best working conditions and improve the quality of the pulp, this study uses the association rules algorithm to optimize the parameters for the waste paper pulping process. These parameters are power of refiner, waste paper concentration of refiner, the volume of slurry that enters deinked process, deinking agent amount, deinking time, deinking temperature, bleaching agent amount, bleaching time, and bleaching temperature. The test results show that the qualified rate of the pulp produced under the improved working conditions is 92.56%, an increase of 6.93%, and the average electricity consumption per ton of pulp is reduced by 5.76 kWh/t. In addition to potential economic benefits, this method can reduce carbon emissions.

Keyword : environmental sustainability, wastewater management, waste management technologies

How to Cite
Hu, S., Li, J., Hong, M., & Man, Y. (2021). Analysis of energy saving and emission reduction of secondary fiber mill based on data mining. Journal of Environmental Engineering and Landscape Management, 29(2), 85-93. https://doi.org/10.3846/jeelm.2021.14219
Published in Issue
May 13, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Axegård, P. (2019). The effect of the transition from elemental chlorine bleaching to chlorine dioxide bleaching in the pulp industry on the formation of PCDD/Fs. Chemosphere, 236, 124386. https://doi.org/10.1016/j.chemosphere.2019.124386

Chakraborty, D., Shelvapulle, S., Reddy, K. R., Kulkarni, R. V., Puttaiahgowda, Y. M., Naveen, S., & Raghu, A. V. (2019). Integration of biological pre-treatment methods for increased resource replace resource with energy recovery from paper and pulp biosludge. Journal of Microbiological Methods, 160, 93–100. https://doi.org/10.1016/j.mimet.2019.03.015

China Paper Association (CPA). (2018). Almanac of China Paper Industry. China Light Industry Press (in Chinese).

Danielewicz, D., & Surma-Ślusarska, B. (2019). Miscanthus × giganteus stalks as a potential non-wood raw material for the pulp and paper industry. Influence of pulping and beating conditions on the fibre and paper properties. Industrial Crops and Products, 141, 111744. https://doi.org/10.1016/j.indcrop.2019.111744

Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record (ACM Special Interest Group on Management of Data), 29(2), 1–12. https://doi.org/10.1145/335191.335372

Hossein, M. A., Talaeipour, M., Hemmasi, A. H., Bazyar, B., & Mahdavi, S. (2015). Effects of sequencing enzyme application and refining on DIP properties produced from mixed office waste paper. BioResources, 10(3), 4768–4783. https://doi.org/10.15376/biores.10.3.4768-4783

Iglesias, C., Santos, A. J. A., Martínez, J., Pereira, H., & Anjos, O. (2017). Influence of heartwood on wood density and pulp properties explained by machine learning techniques. Forests, 8(1), 20. https://doi.org/10.3390/f8010020

Kaur, D., Bhardwaj, N. K., & Lohchab, R. K. (2019). Effect of incorporation of ozone prior to ECF bleaching on pulp, paper and effluent quality. Journal of Environmental Management, 236, 134–145. https://doi.org/10.1016/j.jenvman.2019.01.089

Kumar, V., Kumar, A., Chhabra, D., & Shukla, P. (2019). Improved biobleaching of mixed hardwood pulp and process optimization using novel GA-ANN and GA-ANFIS hybrid statistical tools. Bioresource Technology, 271, 274–282. https://doi.org/10.1016/j.biortech.2018.09.115

Li, J., Mei, M., Han, Y., Hong, M., & Man, Y. (2020). Life cycle cost assessment of recycled paper manufacture in China. Journal of Cleaner Production, 252, 119868. https://doi.org/10.1016/j.jclepro.2019.119868

Li, M., Zhou, P., Wang, H., & Chai, T. (2017). Nonlinear multiobjective MPC-based optimal operation of a high consistency refining system in papermaking. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(3), 1208–1215. https://doi.org/10.1109/TSMC.2017.2748722

Liu, Y., Shen, W., Man, Y., Liu, Z., & Seferlis, P. (2019). Optimal scheduling ratio of recycling waste paper with NSGAII based on deinked-pulp properties prediction. Computers and Industrial Engineering, 132, 74–83. https://doi.org/10.1016/j.cie.2019.04.021

Man, Y., Han, Y., Li, J., & Hong, M. (2019a). Review of energy consumption research for papermaking industry based on life cycle analysis. Chinese Journal of Chemical Engineering, 27(7), 1543–1553. https://doi.org/10.1016/j.cjche.2018.08.017

Man, Y., Han, Y., Li, J., Hong, M., & Zheng, W. (2019b). Life cycle energy consumption analysis and green manufacture evolution for the papermaking industry in China. Green Chemistry, 21(5), 1011–1020. https://doi.org/10.1039/C8GC03604G

Man, Y., Han, Y., Wang, Y., Li, J., Chen, L., Qian, Y., & Hong, M. (2018). Woods to goods: Water consumption analysis for papermaking industry in China. Journal of Cleaner Production, 195, 1377–1388. https://doi.org/10.1016/j.jclepro.2018.06.026

Mandeep, Kumar Gupta, G., & Shukla, P. (2020). Insights into the resources generation from pulp and paper industry wastes: Challenges, perspectives and innovations. Bioresource Technology, 297, 122496. https://doi.org/10.1016/j.biortech.2019.122496

Nwaoha, C., & Tontiwachwuthikul, P. (2019). Carbon dioxide capture from pulp mill using 2-amino-2-methyl-1-propanol and monoethanolamine blend: Techno-economic assessment of advanced process configuration. Applied Energy, 250, 1202–1216. https://doi.org/10.1016/j.apenergy.2019.05.097

Okwonna, O. (2013). The effect of pulping concentration treatment on the properties of microcrystalline cellulose powder obtained from waste paper. Carbohydrate Polymers, 98(1), 721–725. https://doi.org/10.1016/j.carbpol.2013.06.039

Saini, S., Chutani, P., Kumar, P., & Sharma, K. K. (2020). Development of an eco-friendly deinking process for the production of bioethanol using diverse hazardous paper wastes. Renewable Energy, 146, 2362–2373. https://doi.org/10.1016/j.renene.2019.08.087

Shabbir, I., & Mirzaeian, M. (2017). Carbon emissions reduction potentials in pulp and paper mills by applying cogeneration technologies. Energy Procedia, 112, 142–149. https://doi.org/10.1016/j.egypro.2017.03.1075

Sharma, N., Bhardwaj, N. K., & Singh, R. B. P. (2020). Environmental issues of pulp bleaching and prospects of peracetic acid pulp bleaching: A review. Journal of Cleaner Production, 256, 120338. https://doi.org/10.1016/j.jclepro.2020.120338

Suuberg, E. M., Peters, W. A., & Howard, J. B. (1978). Product composition and kinetics of lignite pyrolysis. Industrial and Engineering Chemistry Process Design and Development, 17(1), 37–46. https://doi.org/10.1021/i260065a008

Tsatsis, D. E., Valta, K. A., Vlyssides, A. G., & Economides, D. G. (2019). Assessment of the impact of toner composition, printing processes and pulping conditions on the deinking of office waste paper. Journal of Environmental Chemical Engineering, 7(4), 103258. https://doi.org/10.1016/j.jece.2019.103258

Vashisth, S., Bennington, C. P. J., Grace, J. R., & Kerekes, R. J. (2011). Column Flotation Deinking: State-of-the-art and opportunities. Resources, Conservation and Recycling, 55(12), 1154–1177. https://doi.org/10.1016/j.resconrec.2011.06.013

Veluchamy, C., & Kalamdhad, A. S. (2017). Enhancement of hydrolysis of lignocellulose waste pulp and paper mill sludge through different heating processes on thermal pretreatment. Journal of Cleaner Production, 168, 219–226. https://doi.org/10.1016/j.jclepro.2017.09.040

Wang, L., Meng, J., Xu, P., & Peng, K. (2018). Mining temporal association rules with frequent itemsets tree. Applied Soft Computing Journal, 62, 817–829. https://doi.org/10.1016/j.asoc.2017.09.013

Yamaguchi, K. (2009). An event study on the concealment of the blending ratio of waste paper. Waste Management, 29(5), 1491–1494. https://doi.org/10.1016/j.wasman.2008.11.029

Zhou, P., Wang, H., Li, M., Zhao, Z., & Chai, T. (2016). Datadriven ALS-SVR-ARMA2K modelling with AMPSO parameter optimisation for a high consistency refining system in papermaking. IET Control Theory and Applications, 10(14), 1620–1629. https://doi.org/10.1049/iet-cta.2015.0850