Research on Short-Term Prediction Method of Liquefied Gas Concentration based on Mixed Intelligence
DOI:
https://doi.org/10.21152/1750-9548.18.1.113Abstract
Short-term prediction of liquefied gas concentration is helpful to assisted analysis of storage tank operation status and trend, thereby reducing the risk of accidents. Within the limited space, affected by factors such as complexity, high dimension, strong correlation and weak regularity of storage tank operation data, the existing short-term prediction method of liquefied gas concentration is difficult to ensure the real-time performance and accuracy of prediction results. Therefore, we propose a short-term prediction method of liquefied gas concentration based on mixed intelligence. Firstly, we bring in an Extreme Change Function, and calculate the weighted set kurtosis value of the feature curve to realize feature dimension reduction. Secondly, the Convolutional Neural Network is used to mine the correlation between features and extract effective feature vectors. Meanwhile, we use Long Short-Term Memory Network to learn the change law of the data, so as to obtain the predicted value of liquefied gas concentration. Finally, our method is applied to a real scenario to demonstrate that the short-term prediction method of liquefied gas concentration achieves superior results in prediction accuracy, running speed and stability compared with other methods.
References
Bożena K, Aneta K, Robert P, et al. Research on the safety and security distance of above-ground liquefied gas storage tanks and dispensers[J]. International Journal of Environmental Research and Public Health,2022,19(2). https://doi.org/10.3390/ijerph19020839
Guo X. Study on Near-source Release and Dispersion for Hazardous Liquefied Gas and Assessments of Accident Consequences [D]. Tianjin University, 2021. https://doi.org/10.27356/d.cnki.gtjdu.2021.002372
Geng T, Ju T, Li B, et al. Prediction of the tropospheric NO2 column concentration and distribution using the time sequence-based versus influencing factor-based random forest regression model[J]. Sustainability,2023,15(3). https://doi.org/10.3390/su15032748
Wu C, He H, Song R, et al. A hybrid deep learning model for regional O3 and NO2 concentrations prediction based on spatiotemporal dependencies in air quality monitoring network[J]. Environmental Pollution (Barking, Essex :1987),2023,320. https://doi.org/10.1016/j.envpol.2023.121075
Sun Y. Analysis and prediction of CO2 emission calculation models in the steel industry[D]. Metallurgical Automation Research and Design Institute,2023. https://link.cnki.net/doi/10.27448/d.cnki.gyzyy.2023.000002
Lama A, Ran T, Bilal F, et al. Greenhouse gas emission prediction on road network using deep sequence learning[J]. Transportation Research Part D,2020,88. https://doi.org/10.1016/j.trd.2020.102593
Djeziri, A. M, Djedidi, et al. A temporal-based SVM approach for the detection and identification of pollutant gases in a gas mixture[J]. Applied Intelligence,2021,52(6). https://doi.org/10.1007/s10489-021-02761-0
Guoquan L,Zhichao J,Qi W. Analysis of Gas Leakage Early Warning System Based on Kalman Filter and Optimized BP Neural Network[J]. IEEE ACCESS,2020,8. https://doi.org/10.1109/ACCESS.2020.3026096
Mohamad-Javad M,Faramarz B,Min Z, et al. Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor[J]. Process Safety and Environmental Protection,2022,162. https://doi.org/10.1016/j.psep.2022.04.058
Duan H, Meng X, Tang J, et al. Prediction of NOx concentration using modular long short-term memory neural network for municipal solid waste incineration[J]. Chinese Journal of Chemical Engineering,2023,56(04):46-57. https://doi.org/10.1016/j.cjche.2022.06.028
Zhe Y,Chunlai Y,Xiaolei Y, et al. NOx concentration prediction in coal-fired power plant based on CNN-LSTM algorithm[J]. Frontiers in Energy Research,2023.
Qin Y, Ouyang C, Fang P. Reservoir carbon dioxide flux prediction based on CNN-LSTM model and small sample data [J]. Journal of Chongqing Jiao tong University (Natural Science),2022,41(06):119-125.
Chao L,Ailin Z,Junhua X, et al. LSTM-Pearson Gas Concentration Prediction Model Feature Selection and Its Applications[J]. Energies,2023,16(5). https://doi.org/10.3390/en16052318
Chi D, Huang Q, Liu L, et al. Research on the Prediction Model of Dissolved Oxygen Content in Dished Lakes Based on PCA-MIC-LSTM [J]. Yangtze River,2022,53(06):54-60. https://doi.org/10.16232/j.cnki.1001-4179.2022.06.008
HyungSub K,Florent N,NamJin N, et al. Future Projection of CO2 Absorption and N2O Emissions of the South Korean Forests under Climate Change Scenarios: Toward Net-Zero CO2 Emissions by 2050 and Beyond[J]. Forests,2022,13(7). https://doi.org/10.3390/f13071076
A. L R, A. D E, J. D R, et al. Spectral Kurtosis Based Methodology for the Identification of Stationary Load Signatures in Electrical Signals from a Sustainable Building[J]. Energies,2022,15(7). https://doi.org/10.3390/en15072373
Wang L. Research on the Detection Method of Centrifugal Pump Wear Ring Rubbing Sound Signal Based on Spectral Kurtosis[D]. Zhejiang University,2023. https://doi.org/10.27461/d.cnki.gzjdx.2022.001565
Bing D,Yingjie P,Ning L, et al. Bearing Fault Diagnosis Based on Prime Mean Spectral Segmentation Kurtogram[J]. Journal of Physics: Conference Series,2023,2419(1). https://doi.org/10.1088/1742-6596/2419/1/012080
Gao R, Hu D, Shi W, et al. Fault Feature Enhancement of Rolling Bearing Acoustic Signals based on Maximum Correlation Kurtosis Deconvolution and Spectral Kurtosis[J]. Noise and Vibration Control,2022,42(02):102-107.
Liu J, Zhao X, Zhang W, et al. Prediction of NOx Concentration at SCR Inlet of Power Plant Boiler Based on CNN (1D)-LSTM Model[J]. Electronic Measurement Technology,2023,46(13):59-65. https://doi.org/10.19651/j.cnki.emt.2211530
Jie J, Ke'nan L, Fang'ai L. Prediction of SO2 Concentration Based on AR-LSTM Neural Network [J]. Neural Processing Letters,2022.
Ming F,Dan L,Siyan L. A deep learning-based direct forecasting of CO2 plume migration[J]. Geoenergy Science and Engineering,2023,221. https://doi.org/10.1016/j.geoen.2022.211363
Yuan Z, Chen W, Jiang Z, et al. Research Progress on Nonlinear Coupling Constitutive Relation of Rarefied Gas Flow [J]. Physics of Gases,2022,7(05):1-15. https://doi.org/10.19527/j.cnki.2096-1642.0931
Anshul S,Pardeep K,Kumar H V, et al. Hilbert transform and spectral kurtosis based approach in identifying the health state of retrofitted old steel truss bridge[J]. World Journal of Engineering,2022,19(4). https://doi.org/10.1108/WJE-08-2020-0375
Li X, Bai C, Shi S. Prediction Method of Dissolved Gas Concentration in Locomotive Transformer Oil Based on CNN-BiLSTM Model [J]. Journal of the China Railway Society,2022,44(05):42-48.
Surbhi K, Kumar S S. Machine learning-based time series models for effective CO2 emission prediction in India [J]. Environmental science and pollution research international,2022. https://doi.org/10.1007/s11356-022-21723-8
Published
How to Cite
Issue
Section
Copyright (c) 2024 Y Zhang, F Tian, KJ Zhang, T L, Y Liao, LY Zhang
This work is licensed under a Creative Commons Attribution 4.0 International License.