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Discrimination method of biomass slagging tendency based on particle swarm optimization deep neural network (DNN)

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  • Bi, Yingxin
  • Chen, Chunxiang
  • Huang, Xiaodong
  • Wang, Haokun
  • Wei, Guangsheng

Abstract

The slagging problem that occurs during the combustion of biomass fuels is difficult to deal with, affecting the safe operation of the boiler and reducing the combustion efficiency. Predicting the slagging tendency of biomass combustion can guide the selection of fuel, reduce the experimental cost and improve the production efficiency of the boiler. In this paper, 114 kinds of biomass are selected as the data set. Through the visualization of the overall distribution of the data set, it is found that the data distribution has certain rules, but the whole overlap and cross distribution. The quantitative relationship between slagging degree classification and each element was obtained by Spearman correlation analysis. The higher the content of Ca, Mg and P elements, the smaller the slagging degree, while the more Si, Cl and Ash content, the more serious slagging. Taking 13 kinds of chemical elements and biomass ash content (%) as input and slagging type as output, and the network models of Deep Neural Network, Recurrent Neural Network and Long Short Term Memory Neural Network were built by python 3.6 in TensorFlow virtual environment. The four indexes of Accuracy, Precision, Recall and F1_score were used to evaluate the model. The accuracy of Deep Neural Network model was highest, which was 0.8260869565217391, 0.827380523809524, 0.8425925925926, 0.8322021116138764, respectively. The particle swarm optimization algorithm is used to optimize the highest precision DNN model, and the optimization accuracy is improved to 0.9130434. The optimized model was used to predict the slagging types of other 571 biomass, and the results provide guidance for the actual operation of biomass fuel boilers, thus saving experimental time and reducing experimental costs. At the same time, this study provides a new method for predicting the slagging trend of biofuels.

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  • Bi, Yingxin & Chen, Chunxiang & Huang, Xiaodong & Wang, Haokun & Wei, Guangsheng, 2023. "Discrimination method of biomass slagging tendency based on particle swarm optimization deep neural network (DNN)," Energy, Elsevier, vol. 262(PA).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222022502
    DOI: 10.1016/j.energy.2022.125368
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    1. Saidur, R. & Abdelaziz, E.A. & Demirbas, A. & Hossain, M.S. & Mekhilef, S., 2011. "A review on biomass as a fuel for boilers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(5), pages 2262-2289, June.
    2. Noushabadi, Abolfazl Sajadi & Dashti, Amir & Ahmadijokani, Farhad & Hu, Jinguang & Mohammadi, Amir H., 2021. "Estimation of higher heating values (HHVs) of biomass fuels based on ultimate analysis using machine learning techniques and improved equation," Renewable Energy, Elsevier, vol. 179(C), pages 550-562.
    3. Ifaei, Pouya & Farid, Alireza & Yoo, ChangKyoo, 2018. "An optimal renewable energy management strategy with and without hydropower using a factor weighted multi-criteria decision making analysis and nation-wide big data - Case study in Iran," Energy, Elsevier, vol. 158(C), pages 357-372.
    4. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    5. Azadeh, A. & Babazadeh, R. & Asadzadeh, S.M., 2013. "Optimum estimation and forecasting of renewable energy consumption by artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 605-612.
    6. Míguez, José Luis & Porteiro, Jacobo & Behrendt, Frank & Blanco, Diana & Patiño, David & Dieguez-Alonso, Alba, 2021. "Review of the use of additives to mitigate operational problems associated with the combustion of biomass with high content in ash-forming species," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    7. Melts, Indrek & Ivask, Mari & Geetha, Mohan & Takeuchi, Kazuhiko & Heinsoo, Katrin, 2019. "Combining bioenergy and nature conservation: An example in wetlands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 293-302.
    8. Sakiewicz, Piotr & Piotrowski, Krzysztof & Kalisz, Sylwester, 2020. "Neural network prediction of parameters of biomass ashes, reused within the circular economy frame," Renewable Energy, Elsevier, vol. 162(C), pages 743-753.
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