|
|
|
| Combined Prediction Model of SO2 Concentration at Outlet of Absorber Based on VMD-IASO-ELM |
| JIN Xiu-zhang,LI Yang-feng,YAO Ning |
| School of Control and Computer Engineering, North China Electric Power University, Baoding, Hebei 071003, China |
|
|
|
|
Abstract In order to improve the emission control level of SO2 pollutants in thermal power plants, a combined prediction model of SO2 concentration at outlet of absorber was proposed based on VMD-IASO-ELM. Firstly, the mechanism and correlation analysis were used to determine the initial correlation variables of SO2 concentration at outlet of absorber, and variational mode decomposition (VMD) algorithm was used to decompose it. The low-frequency components with large mutual information between the decomposition results and the output were retained. Then, the simple structure was established based on fast-learning extreme learning machine (ELM), and the improved atomic search optimization (IASO) based on hybrid strategy was used to optimize network parameters and improve the prediction accuracy. Finally, the error correction term was deduced from fuzzy rules to correct the prediction results of ELM model. Using historical data for simulation modeling, the results show that the model has high prediction accuracy and learning ability and can accurately track the change trend of SO2 concentration at the outlet of absorber.
|
|
Received: 19 November 2021
Published: 18 April 2023
|
|
|
|
|
|
| ［6］ |
金秀章, 刘岳, 于静, 等. 基于变量选择和EMD-LSTM网络的出口SO2浓度预测 ［J］. 中国电机工程学报, 2021, 41(24): 8475-8483.
|
| ［1］ |
郦建国, 朱法华, 孙雪丽. 中国火电大气污染防治现状及挑战 ［C］//第十八届中国电除尘学术会议. 南京, 2019.
|
| ［3］ |
金秀章, 李京. 基于互信息PSO-LSSVM的SO2浓度预测 ［J］. 计量学报, 2021, 42(5): 675-680.
|
|
Jin X Z, Li J. Prediction of SO2 concentration based on mutual information PSO-LSSVM ［J］. Acta Metrologica Sinica, 2021, 42(5): 675-680.
|
| ［4］ |
苏祥鹏, 刁永发, 杨青杰. 基于RBF神经网络的双碱法脱硫塔SO2排放量预测方法 ［J］. 热力发电, 2017, 46(4): 58-63.
|
|
Ma S C, Lin C Y, Zhou Q, et al. Prediction model of FGD system based on deep neural network and its application ［J］. Chemical Industry And Engineering Progress, 2021, 40(3): 1689-1698.
|
|
Jin X Z, Liu Y, Yu J, et al. Prediction of outlet SO2 concentration based on variable selection and EMD-LSTM network ［J］. Proceedings of the CSEE, 2021, 41(24): 8475-8483.
|
| ［8］ |
Dragomiretskiy K, Zosso D. Variational mode decomposition ［J］. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
|
| ［12］ |
吴华锋, 陈信强, 毛奇凰, 等. 基于自然选择策略的蚁群算法求解TSP问题 ［J］. 通信学报, 2013, 34(4): 165-170.
|
| ［13］ |
汪逸晖, 高亮. 乌鸦搜索算法的改进及其在工程约束优化问题中的应用 ［J］. 计算机集成制造系统, 2021, 27(7): 1871-1883.
|
| ［17］ |
李丰泽, 马素霞. 基于动态论域的循环流化床锅炉燃烧系统的模糊自适应PID控制 ［J］. 动力工程学报, 2021, 41(3): 195-200.
|
| ［18］ |
樊凤杰,白洋,纪会芳. 基于EEMD-ICA的脑电去噪算法研究［J］. 计量学报, 2021, 42(3): 395-400.
|
| ［2］ |
李斌, 邓煜, 边禹铭, 等. 基于T-S模糊神经网络的湿法脱硫效率预测 ［J］. 热力发电, 2016, 45(6): 116-119.
|
|
Li B, Deng Y, Bian Y M, et al. Prediction of limestone-gypsum wet flue gas desulfurization efficiency baesd on T-S fuzzy neural network ［J］. Thermal Power Generation, 2016, 45(6): 116-119.
|
|
Su X P, Diao Y F, Yang Q J. Prediction of SO2 emission from dual-alkali desulfurization tower based on RBF neural network ［J］. Thermal Power Generation, 2017, 46(10): 58-63.
|
| ［7］ |
董兴辉, 马晓双, 程友星, 等. 风电机组轴承健康劣化趋势建模与仿真 ［J］. 系统仿真学报, 2019, 31(1): 151-157.
|
|
Dong X H, Ma X S, Cheng Y X, et al. Modeling and simulation of health degradation trend for wind turbine bearing ［J］. Journal of System Simulation, 2019, 31(1): 151-157.
|
| ［11］ |
吴忠强, 王国勇, 谢宗奎, 等. 基于IALO算法的蓄电池参数辨识 ［J］. 计量学报, 2021, 42(9): 1206-1213.
|
|
Wang Y H, Gao L. Improvement of crow search algorithm and its application in engineering constrained optimization problems ［J］. Computer Integrated Manufacturing Systems, 2021, 27(7): 1871-1883.
|
| ［14］ |
董东林, 陈昱吟, 倪林根, 等. 基于WOA-ELM算法的矿井突水水源快速判别模型 ［J］. 煤炭学报, 2021, 46(3): 984-993.
|
| ［16］ |
李钊, 秦增光, 刘兆军, 等. 基于全变分辅助的相敏光时域反射技术 ［J］. 光学学报, 2021, 41(17): 32-39.
|
|
Fan F J,Bai Y,Ji H F. Denoising Method of EEG Signal Based on EEMD-ICA［J］. Acta Metrologica Sinica, 2021, 42(3): 395-400.
|
| ［19］ |
闫浩思,赵文杰. 基于MIC和MPA-KELM的脱硫出口SO2浓度预测［J］. 计量学报, 2023, 44(2): 271-278.
|
| ［5］ |
马双忱, 林宸雨, 周权, 等. 基于深度神经网络的脱硫系统预测模型及应用 ［J］. 化工进展, 2021, 40(3): 1689-1698.
|
|
Wu Z Q, Wang G Y, Xie Z K, et al. Parameter identification of battery basedon IALO algorithm ［J］. Acta Metrologica Sinica, 2021, 42(9): 1206-1213.
|
|
Wu H F, Chen X Q, Mao Q F, et al. Improved ant colony algorithm based on natural selection strategy for solving TSP problem ［J］. Journal on Communications, 2013, 34(4): 165-170.
|
|
Li K, Qin Z G, Liu Z J, et al. Phase-sensitive optical time domain reflectometry assisted by total variation techniques ［J］. Acta Optica Sinica, 2021, 42(17): 32-39.
|
|
Li F Z, Ma S X. Fuzzy adaptive PID control of circulating fluidized bed boiler combustion system based on dynamic universe ［J］. Journal of Chinese Society of Power Engineering, 2021, 41(3): 195-200.
|
| ［9］ |
Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: A new learning scheme of feed-forward neural networks ［C］// IEEE. IEEE International Joint Conference on Neural Network. 2004.
|
| ［10］ |
Zhao W G, Wang L Y, Zhang Z X. A novel atom search optimization for dispersion coefficient estimation in groundwater ［J］. Future Generation Computer Systems, 2019, 91: 601-610.
|
|
Dong D L, Chen Y Y, Ni L G, et al. Fast discriminant model of mine water inrush source based on WOA-ELM algorithm ［J］. Journal of China Coal Society, 2021, 46(3): 984-993.
|
| ［15］ |
刘岳, 于静, 金秀章. 基于特征优化和改进长短期记忆网络的NOx质量浓度预测 ［J］. 热力发电, 2021, 50(7): 162-169.
|
|
Liu Y, Yu J, Jin X Z. NOx mass concentration prediction based on feature optimization and improved LSTM network ［J］. Thermal Power Generation, 2021, 50(7): 162-169.
|
|
Yan H S,Zhao W J. Prediction of SO2 Concentration at Desulfurization Outlet Based on MIC and MPA-KELM［J］. Acta Metrologica Sinica, 2021, 44(2): 271-278.
|
| ［20］ |
Shi Y, Eberhart R. Empirical study of particle swarm optimization ［C］//International Conference on Evolutionary Computation. Washington, USA, 1999.
|
|
|
|
2023, Vol. 44 
