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The Innovation Energy > 2024 Vol. 1 > No. 4 > 100052
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Prediction of exhaust gas content of PTA oxidation units based on the improved parameter adaptive LSTM

  • 1.

    College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

  • 2.

    China Kunlun Contracting & Engineering CO., LTD, Beijing 100037, China

  • 3.

    Beijing Advanced Innovation Centerfor Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China

  • 4.

    Department of Automation, Tsinghua University, Beijing 100084, China

  • 5.

    Sustainable energy research Center (SERC), Dalarna University, Falun 79188, Sweden

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The Innovation Energy  1(4),  https://doi.org/10.59717/j.xinn-energy.2024.100052  |  Cite this article
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  • [1] Zhang, Y., Li, R., and Tsai, CL. (2010). Regularization Parameter Selections via Generalized Information Criterion. J. Am. Stat. Assoc. 105(489): 312−323. DOI: 10.1198/jasa.2009.tm08013.

    View in Article CrossRef Google Scholar

    [2] Vagropoulos, Stylianos I., et al. (2016). Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting. IEEE international energy conference. 1-6. DOI: 10.1109/ENERGYCON.2016.7514029.

    View in Article Google Scholar

    [3] Chen, C., Lu, N., Jiang, B., et al. (2021). A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance. IEEE/CAA J. Autom. Sinica. 8(2): 412−422. DOI: 10.1109/JAS.2021.1003835.

    View in Article CrossRef Google Scholar Scopus

    [4] Zuo, H., Yan, G., Lu, R., et al (2024) A Multi-Task Learning Based Runoff Forecasting Model for Multi-Scale Chaotic Hydrological Time Series. Water Res. Manag. 38(2): 481-503. DOI: 10.1007/s11269-023-03681-z.

    View in Article Google Scholar

    [5] Zahari, A., and Jafreezal J. (2015). A novel approach of hidden Markov model for time series forecasting. Proceedings of the 9th International Conference on Ubiquitous Information Manag. Commun. 91: 1–5. DOI: 10.1145/2701126.2701179.

    View in Article Google Scholar

    [6] Ye, Z., Wang, N., Zhou, J., et al. (2024). Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks. The Innovation. 5(2): 100562. DOI:https://doi.org/10.1016/j.xinn.2023.100562.

    View in Article Google Scholar

    [7] Han, Y., Li, Z., Hu, X., et al. (2024). Novel Long Short-Term Memory model based on the attention mechanism for the leakage detection of water supply processes. IEEE Trans. Syst. Man. Cybern. 54(5): 2786−2796. DOI: 10.1109/TSMC.2024.3350200.

    View in Article CrossRef Google Scholar

    [8] Cai, H., Liu, X., Sun, L., et al. (2024). Battery internal short circuit diagnosis based on vision transformer without real data. The Innovation Energy 1(3): 100041. DOI: 10.59717/j.xinn-energy.2024.100041.

    View in Article CrossRef Google Scholar

    [9] Wu, Z., Pan, S., Long G., et al. (2020). Connecting the dots: Multivariate time series forecasting with graph neural networks. Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 753–763. DOI:10.1145/3394486.3403118.

    View in Article Google Scholar

    [10] Oreshkin, B. N., Amini, A., Coyle, L., et al. (2021). FC-GAGA: Fully connected gated graph architecture for spatio-temporal traffic forecasting. Proceedings of the AAAI conference on artificial intelligence. 35(10): 9233−9241. DOI:arXiv:2007.15531. DOI: 10.1609/aaai.v35i10.17114.

    View in Article CrossRef Google Scholar

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  • Cite this article:

    Han Y., Huang J., Xu B., et al., (2024). Prediction of exhaust gas content of PTA oxidation units based on the improved parameter adaptive LSTM. The Innovation Energy 1(4): 100052. https://doi.org/10.59717/j.xinn-energy.2024.100052
    Han Y., Huang J., Xu B., et al., (2024). Prediction of exhaust gas content of PTA oxidation units based on the improved parameter adaptive LSTM. The Innovation Energy 1(4): 100052. https://doi.org/10.59717/j.xinn-energy.2024.100052

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