城市固廢焚燒過(guò)程爐溫與煙氣含氧量多目標魯棒預測模型
doi: 10.16383/j.aas.c230430
-
1.
北京工業(yè)大學(xué)信息學(xué)部 北京 100124
-
2.
數字社區教育部工程研究中心 北京 100124
-
3.
城市軌道交通北京實(shí)驗室 北京 100124
Multi-target Robust Prediction Model for Furnace Temperature and Flue Gas Oxygen Content in Municipal Solid Waste Incineration Process
-
1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124
-
2.
Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124
-
3.
Beijing Laboratory for Urban Mass Transit, Beijing 100124
-
摘要: 為實(shí)現城市固廢焚燒(Municipal solid waste incineration, MSWI)過(guò)程爐溫與煙氣含氧量的準確預測, 提出一種基于改進(jìn)隨機配置網(wǎng)絡(luò )的多目標魯棒建模方法(Multi-target robust modeling method based on improved stochastic configuration network, MRI-SCN). 首先, 設計了一種并行方式增量構建 SCN 隱含層, 通過(guò)信息疊加與跨越連接來(lái)增強隱含層映射多樣性, 并利用參數自適應變化的監督不等式分配隱含層參數; 其次, 使用$ \text{F} $范數與$ L_{2,1} $范數正則項建立矩陣彈性網(wǎng)對模型參數進(jìn)行稀疏約束, 以建模爐溫與煙氣含氧量間的相關(guān)性; 接著(zhù), 采用混合拉普拉斯分布作為每個(gè)目標建模誤差的先驗分布, 通過(guò)最大后驗估計重新評估 SCN 模型的輸出權值, 以增強其魯棒性; 最后, 利用城市固廢焚燒過(guò)程的歷史數據對所提建模方法的性能進(jìn)行測試. 實(shí)驗結果表明, 所提建模方法在預測精度與魯棒性方面具有優(yōu)勢.Abstract: To achieve accurate prediction of furnace temperature and flue gas oxygen content in municipal solid waste incineration (MSWI) process, a multi-target robust modeling method based on improved stochastic configuration network (MRI-SCN) is proposed. First, a parallel method is designed to incrementally build SCN hidden layers, which enhances the diversity of hidden layer mapping through information superposition and spanning connection, and assign hidden layer parameters using the supervised inequality with adaptive parameter changes. Second, a matrix elastic net is established by using F-norm and $ L_{2,1} $-norm regularization terms to sparsely constrain the model parameters to model the correlation between furnace temperature and flue gas oxygen content. Then, the mixture Laplace distribution is used as the prior distribution of each target modeling error, and the output weights of the SCN model are re-evaluated by maximum a posteriori estimation to enhance its robustness. Finally, the performance of the proposed modeling method is tested on the historical data of municipal solid waste incineration process. The experimental results show that the proposed modeling method has advantages in prediction accuracy and robustness.
-
圖 2 前饋神經(jīng)網(wǎng)絡(luò )隱含層構造方式
Fig. 2 The hidden layer construction methods of feedforward neural network
圖 3 不同范數約束在原始數據集上的實(shí)驗結果
Fig. 3 Results of experiments with different paradigm constraints on the original dataset
圖 5 爐溫與煙氣含氧量散點(diǎn)圖及預測誤差概率分布曲線(xiàn)$(\zeta$ = 20%)
Fig. 5 Scatterplot of furnace temperature, flue gas oxygen content and probability distribution curves of prediction error $(\zeta$ = 20%)
圖 6 爐溫與煙氣含氧量預測誤差曲線(xiàn)及模型輸出權值$(\zeta$ = 30%)
Fig. 6 Furnace temperature, flue gas oxygen content prediction error curves and model output weights $(\zeta$ = 30%)
表 1 MRI-SCN與不同類(lèi)型建模方法在原始數據集上的對比實(shí)驗結果
Table 1 Results of experiments comparing MRI-SCN with the different type of modeling methods on the original dataset
數據集 BP RBF RVFL MLS-SVR MRI-SCN 春季 5.80; 4.57; 86.57 5.40; 3.74; 89.14 4.55; 3.59; 92.36 3.41; 2.38; 95.70 3.12; 2.34; 96.38 夏季 5.63; 4.33; 87.48 4.85; 3.75; 91.49 4.60; 3.63; 92.14 3.37; 2.52; 95.90 3.16; 2.26; 96.25 秋季 5.39; 4.27; 89.03 4.92; 3.73; 91.25 4.52; 3.55; 92.44 3.44; 2.69; 95.41 3.08; 2.31; 96.48 冬季 5.30; 4.19; 89.93 5.72; 4.42; 89.14 4.96; 3.91; 91.46 3.52; 2.53; 95.61 3.10; 2.33; 96.68 下載: 導出CSV表 2 MRI-SCN 與同類(lèi)型建模方法在原始數據集上的對比實(shí)驗結果
Table 2 Results of experiments comparing MRI-SCN with the same type of modeling methods on the original dataset
數據集 SCN MI-SCN MT-SCN MoGL-SCN MRI-SCN 春季 3.67; 2.86; 95.07 3.45; 2.64; 95.62 3.46; 2.67; 95.59 3.61; 2.68; 94.50 3.12; 2.34; 96.38 夏季 3.70; 2.80; 95.40 3.43; 2.55; 95.65 3.32; 2.57; 95.84 3.40; 2.67; 95.63 3.16; 2.26; 96.25 秋季 3.63; 2.73; 95.17 3.31; 2.56; 95.91 3.34; 2.61; 95.92 3.52; 2.76; 95.42 3.08; 2.31; 96.48 冬季 3.74; 2.94; 95.23 3.56; 2.73; 95.33 3.49; 2.81; 95.84 3.55; 2.31; 95.82 3.10; 2.33; 96.68 下載: 導出CSV表 3 四組噪聲數據集上的實(shí)驗結果
Table 3 Results of experiments on the four noisy datasets
數據集 $ \zeta $ SCN MI-SCN MT-SCN MoGL-SCN MRI-SCN 10% 6.62; 5.15; 83.95 5.80; 4.57; 87.67 5.59; 4.40; 88.55 3.86; 2.87; 94.43 3.65; 2.74; 95.07 15% 7.59; 5.97; 78.84 6.39; 5.07; 84.97 6.35; 5.05; 85.16 4.00; 2.95; 94.02 3.87; 2.89; 94.45 春季 20% 8.96; 7.11; 70.38 7.33; 5.80; 80.14 7.36; 5.88; 79.99 4.26; 3.11; 93.21 4.03; 3.02; 93.95 25% 10.38; 8.05; 60.18 8.26; 6.46; 74.83 8.58; 6.74; 72.80 4.48; 3.26; 92.53 4.34; 3.24; 92.95 30% 11.17; 8.71; 53.98 8.70; 6.86; 72.21 9.40; 7.41; 67.46 4.82; 3.50; 91.25 4.56; 3.38; 92.28 10% 6.31; 4.90; 85.43 5.77; 4.56; 87.74 5.50; 4.30; 88.88 4.00; 2.93; 93.81 3.76; 2.78; 94.66 15% 7.38; 5.71; 79.98 6.48; 5.13; 84.56 6.24; 4.89, 85.75 4.38; 3.14; 92.66 3.96; 2.92; 94.07 夏季 20% 8.91; 7.00; 70.13 7.45; 5.94; 79.13 7.49; 5.96; 78.73 4.38; 3.17; 92.55 4.22; 3.13; 93.10 25% 9.40; 7.48; 66.62 7.89; 6.34; 76.58 7.93; 6.35; 76.22 4.68; 3.33; 91.83 4.45; 3.27; 92.43 30% 10.39; 8.21; 59.58 8.61; 6.90; 72.39 8.85; 7.09; 70.72 5.08; 3.65; 89.94 4.65; 3.41; 91.83 10% 6.40; 5.04; 85.18 5.98; 4.79; 86.83 5.57; 4.43; 88.69 3.73; 2.75; 94.76 3.46; 2.60; 95.53 15% 7.33; 5.72; 80.40 6.33; 5.01; 85.27 6.10; 4.78; 86.37 3.80; 2.80; 94.63 3.61; 2.72; 95.11 秋季 20% 8.90; 6.85; 71.15 7.19; 5.66; 80.85 7.40; 5.74; 79.96 4.03; 2.95; 94.02 3.86; 2.88; 94.45 25% 9.82; 7.52; 65.08 7.62; 6.00; 78.77 8.00; 6.20; 76.81 4.11; 2.98; 93.71 3.96; 2.96; 94.16 30% 10.79; 8.25; 57.89 8.28; 6.44; 74.97 8.75; 6.73; 72.24 4.50; 3.22; 92.52 4.26; 3.18; 93.28 10% 6.86; 5.34; 83.29 6.55; 5.16; 84.72 6.22; 4.86; 86.29 4.10; 3.04; 94.20 3.93; 2.98; 94.56 15% 7.94; 6.20; 78.14 7.27; 5.76; 81.77 6.97; 5.50; 83.25 4.40; 3.21; 93.30 4.27; 3.18; 93.73 冬季 20% 9.40; 7.36; 69.16 8.05; 6.32; 77.44 7.91; 6.20; 78.23 4.55; 3.33; 92.83 4.37; 3.28; 93.32 25% 10.56; 8.18; 60.47 8.81; 6.94; 72.48 8.90; 6.97; 71.96 4.88; 3.56; 91.40 4.62; 3.45; 92.41 30% 11.26; 8.74; 55.94 9.50; 7.48; 68.70 9.65; 7.57; 67.57 5.02; 3.64; 91.25 4.83; 3.60; 91.72 下載: 導出CSV表 4 不同建模方法運行 30 次的時(shí)間對比
Table 4 Comparison of time for 30 runs of different modeling methods
方法 BP RBF SCN MI-SCN MT-SCN MoGL-SCN MRI-SCN 時(shí)間(s) 21.43 31.70 5.23 16.29 36.22 37.78 28.17 下載: 導出CSVA1 多目標魯棒預測模型輸入變量明細
A1 Input variable details of multi-target robust prediction model
序號 變量名稱(chēng) 單位 1 進(jìn)料器左內側速度 % 2 進(jìn)料器左外側速度 % 3 進(jìn)料器右內側速度 % 4 進(jìn)料器右外側速度 % 5 干燥爐排左內側速度 % 6 干燥爐排左外側速度 % 7 干燥爐排右內側速度 % 8 干燥爐排右外側速度 % 9 干燥爐排左1空氣流量 $ {\rm {km^3N/h}} $ 10 干燥爐排右1空氣流量 $ {\rm {km^3N/h}} $ 11 干燥爐排左2空氣流量 $ {\rm {km^3N/h}} $ 12 干燥爐排右2空氣流量 $ {\rm {km^3N/h}} $ 13 燃燒爐排左1-1段空氣流量 $ {\rm {km^3N/h}} $ 14 燃燒爐排右1-1段空氣流量 $ {\rm {km^3N/h}} $ 15 燃燒爐排左1-2段空氣流量 $ {\rm {km^3N/h}} $ 16 燃燒爐排右1-2段空氣流量 $ {\rm {km^3N/h}} $ 17 燃燒爐排左2-1段空氣流量 $ {\rm {km^3N/h}} $ 18 燃燒爐排右2-1段空氣流量 $ {\rm {km^3N/h}} $ 19 燃燒爐排左2-2段空氣流量 $ {\rm {km^3N/h}} $ 20 燃燒爐排右2-2段空氣流量 $ {\rm {km^3N/h}} $ 21 燃燼爐排左空氣流量 $ {\rm {km^3N/h}} $ 22 燃燼爐排右空氣流量 $ {\rm {km^3N/h}} $ 23 二次風(fēng)量 $ {\rm {km^3N/h}} $ 24 一次風(fēng)機出口空氣壓力 kPa 25 一次空氣加熱器出口空氣溫度 ℃ 26 干燥爐排左內側溫度 ℃ 27 干燥爐排左外側溫度 ℃ 28 干燥爐排右內側溫度 ℃ 29 干燥爐排右外側溫度 ℃ 30 燃燒爐排1-1段左內側溫度 ℃ 31 燃燒爐排1-1段左外側溫度 ℃ 32 燃燒爐排1-1段右內側溫度 ℃ 33 燃燒爐排1-1段右外側溫度 ℃ 34 燃燒爐排1-2段左內側溫度 ℃ 35 燃燒爐排1-2段左外側溫度 ℃ 36 燃燒爐排1-2段右內側溫度 ℃ 37 燃燒爐排1-2段右外側溫度 ℃ 38 燃燒爐排2-1段左內側溫度 ℃ 39 燃燒爐排2-1段左外側溫度 ℃ 40 燃燒爐排2-1段右內側溫度 ℃ 41 燃燒爐排2-1段右外側溫度 ℃ 42 燃燒爐排2-2段左內側溫度 ℃ 43 燃燒爐排2-2段左外側溫度 ℃ 44 燃燒爐排2-2段右內側溫度 ℃ 45 燃燒爐排2-2段右外側溫度 ℃ 46 當前時(shí)刻的爐溫 ℃ 47 當前時(shí)刻的煙氣含氧量 % 下載: 導出CSV亚洲第一网址_国产国产人精品视频69_久久久久精品视频_国产精品第九页 -
[1] Li Y, Zhao X G, Li Y B, Li X. Waste incineration industry and development policies in China. Waste Management, 2015, 46(8): 234?241 [2] 湯健, 夏恒, 余文, 喬俊飛. 城市固廢焚燒過(guò)程智能優(yōu)化控制研究現狀與展望. 自動(dòng)化學(xué)報, 2023, 49(10): 2019?2059Tang Jian, Xia Heng, Yu Wen, Qiao Jun-Fei. Research status and prospects of intelligent optimization control for municipal solid waste incineration process. Acta Automatica Sinica, 2023, 49(10): 2019?2059 [3] 嚴愛(ài)軍, 胡開(kāi)成. 城市生活垃圾焚燒爐溫控制的多目標優(yōu)化設定方法. 控制理論與應用, 2023, 40(4): 693?701Yan Ai-Jun, Hu Kai-Cheng. Multi-objective optimization setting method for temperature control of municipal solid waste incinerator. Control Theory & Applications, 2023, 40(4): 693?701 [4] Sun R, Ismail T M, Ren X H, Abd El-Salam M. Numerical and experimental studies on effects of moisture content on combustion characteristics of simulated municipal solid wastes in a fixed bed. Waste Management, 2015, 39(5): 166?178 [5] Magnanelli E, Tran?s O L, Carlsson P, Mosby J, Becidan M. Dynamic modeling of municipal solid waste incineration. Energy, 2020, 299(10): Article No. 118426 [6] 蔣珂, 蔣朝輝, 謝永芳, 潘冬, 桂衛華. 基于動(dòng)態(tài)注意力深度遷移網(wǎng)絡(luò )的高爐鐵水硅含量在線(xiàn)預測方法. 自動(dòng)化學(xué)報, 2023, 49(5): 949?963Jiang Ke, Jiang Zhao-Hui, Xie Yong-Fang, Pan Dong, Gui Wei-Hua. Online prediction method for silic on content of molten iron in blast furnace based on dynamic attention deep transfer network. Acta Automatica Sinica, 2023, 49(5): 949?963 [7] Zhou X F, Zhai N J, Li S A, Shi H B. Time series prediction method of industrial process with limited data based on transfer learning. IEEE Transactions on Industrial Informatics, 2023, 19(5): 6872?6882 doi: 10.1109/TII.2022.3191980 [8] He H J, Meng X, Tang J, Qiao J F. A novel self-organizing TS fuzzy neural network for furnace temperature prediction in MSWI process. Neural Computing and Applications, 2022, 34(12): 9759?9776 [9] 郭海濤, 湯健, 丁海旭, 喬俊飛. 基于混合數據增強的MSWI 過(guò)程燃燒狀態(tài)識別. 自動(dòng)化學(xué)報, 2024, 50(3): 560?575Guo Hai-Tao, Tang Jian, Ding Hai-Xu, Qiao Jun-Fei. Combustion states recognition method of mswi process based on mixed data enhancement. Acta Automatica Sinica, 2024, 50(3): 560?575 [10] Qiao J F, Sun J, Meng X. Event-triggered adaptive model predictive control of oxygen content for municipal solid waste incineration process. IEEE Transactions on Automation Science and Engineering, 2024, 21(1): 463?474 doi: 10.1109/TASE.2022.3227918 [11] Pao Y H, Park G H, Sobajic D J. Learning and generalization characteristics of random vector functional-link net. Neurocomputing, 1994, 6(2): 163?180 doi: 10.1016/0925-2312(94)90053-1 [12] Wang D H, Li M. Stochastic configuration networks: Fundamentals and algorithms. IEEE Transactions on Cybernetics, 2017, 47(10): 3346?3479 [13] Li K, Yang C C, Wang W, Qiao J F. An improved stochastic configuration network for concentration prediction in wastewater treatment process. Information Sciences, 2023, 622(4): 148?160 [14] Li X, He Y, Ding J, Luan F, Zhang D. Predicting hot-strip finish rolling thickness using stochastic configuration networks. Information Sciences, 2022, 611(9): 677?689 [15] Lu J, Ding J L. Construction of prediction intervals for carbon residual of crude oil based on deep stochastic configuration networks. Information Sciences, 2019, 486(6): 119?132 [16] 胡開(kāi)成, 嚴愛(ài)軍, 王殿輝. 城市固廢焚燒過(guò)程爐溫非線(xiàn)性模型預測控制 [Online], available: http://kns.cnki.net/kcms/detail/44.1240.TP.20230330.0900.006.html, 2024-03-22Hu Kai-Cheng, Yan Ai-Jun, Wang Dian-Hui. Nonlinear model predictive control of furnace temperature for a municipal solid waste incineration process [Online], available: http://kns.cnki.net/kcms/detail/44.1240.TP.20230330.0900.006.html, March 22, 2024 [17] Yan A J, Guo J C, Wang D H. Heterogeneous feature ensemble modeling with stochastic configuration networks for predicting furnace temperature of a municipal solid waste incineration process. Neural Computing and Applications, 2022, 34(18): 15807?15819 doi: 10.1007/s00521-022-07271-9 [18] Ding H X, Tang J, Qiao J F. MIMO modeling and multi-loop control based on neural network for municipal solid waste incineration. Control Engineering Practice, 2022, 127(10): Article No. 105280 [19] Borchani H, Varando G, Bielza C, Larra?aga P. A survey on multi-output regression. Wiley Interdisciplinary Reviews Data Mining & Knowledge Discovery, 2015, 5(5): 216?233 [20] Kili?arslan S, K?zkurt C, Ba? S, Elen A. Detection and classification of pneumonia using novel superior exponential (SupEx) activation function in convolutional neural networks. Expert Systems With Applications, 2023, 217(5): Article No. 119503 [21] Gao Z, Yu W, Yan J. Neuro adaptive fault-tolerant control embedded with diversified activating functions with application to auto-driving vehicles under fading actuation. IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2023.3248100 [22] Li F J, Qiao J F, Han H G, Yang C L. A self-organizing cascade neural network with random weights for nonlinear system modeling. Applied Soft Computing, 2016, 42(5): 184?193 [23] Luo H Y, Han G L, Wu X T, Liu P X, Yang H, Zhang X. Cascaded hourglass feature fusing network for saliency detection. Neurocomputing, 2021, 428(3): 206?217 [24] Li J P, Hua C C, Qian J L, Guan X P. Low-rank based multi-input multi-output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace. Fuzzy Sets and System, 2021, 421(9): 178?192 [25] Arashloo S R, Kittler J. Multi-target regression via non-linear output structure learning. Neurocomputing, 2022, 492(7): 572?580 [26] Tak N, ?nan D. Type-1 fuzzy forecasting functions with elastic net regularization. Expert Systems With Applications, 2022, 199(8): Article No. 116916 [27] Nie F P, Huang H, Cai X, Ding C. Efficient and robust feature selection via joint L2, 1-norms minimization. In: Proceedings of Advances in Neural Information Processing Systems. Vancouver, Canada: MIT Press, 2010. 1813?1821 [28] Xiang S M, Nie F P, Meng G F, Pan C H, Zhang C S. Discriminative least squares regression for multiclass classification and feature selection. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(11): 1738?1754 doi: 10.1109/TNNLS.2012.2212721 [29] Li Y H, Hu L, Gao W F. Multi-label feature selection via robust flexible sparse regularization. Pattern Recognition, 2023, 134(2): Article No. 109074 [30] Lv S H, Zhao H Q, Zhou L J. Robust proportionate normalized least mean M-estimate algorithm for block-sparse system identification. IEEE Transactions on Circuits and Systems-II Express Briefs, 2022, 69(1): 234?238 doi: 10.1109/TCSII.2021.3082425 [31] Wang Q, He X, Jiang X, Li X L. Robust bi-stochastic graph regularized matrix factorization for data clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(1): 390?403 [32] Yang X Y, Mu Y F, Cao K, Lv M Z, Peng B, Zhang Y, et al. Robust kernel recursive adaptive filtering algorithms based on M-estimate. Signal Processing, 2023, 207(6): Article No. 108952 [33] Duong N C, Speyer J L, Idan M. Laplace estimation for scalar linear systems. Automatica, 2022, 144(10): Article No. 110301 [34] Liang Z Z, Zhang L. L1-norm discriminant analysis via Bhattacharyya error bounds under Laplace distributions. Pattern Recognition, 2023, 141(9): Article No. 109609 [35] Lu J, Ding J L. Mixed-distribution based robust stochastic configuration networks for prediction interval construction. IEEE Transactions on Industrial Informatics, 2020, 16(8): 5099?5109 doi: 10.1109/TII.2019.2954351 [36] Song W X, Yao W X, Xing Y R. Robust mixture regression model fitting by Laplace distribution. Computational Statistics and Data Analysis, 2014, 71(3): 128?137 [37] Phillips R F. Least absolute deviations estimation via the EM algorithm. Statistics and Computing, 2002, 12(3): 281?285 doi: 10.1023/A:1020759012226 [38] Xu S, An X, Qiao X D, Zhu L J, Li L. Multi-output least-squares support vector regression machines. Pattern Recognition Letters, 2013, 34(9): 1078?1084 doi: 10.1016/j.patrec.2013.01.015 [39] Wang Q J, Hong Q Q, Wu S, Dai W. Multi-target stochastic configuration network and applications. IEEE Transactions on Artificial Intelligence, 2023, 4(2): 338?348 doi: 10.1109/TAI.2022.3162570