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              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

              胡開(kāi)成 嚴愛(ài)軍 湯健

              胡開(kāi)成, 嚴愛(ài)軍, 湯健. 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. 自動(dòng)化學(xué)報, 2024, 50(5): 1001?1014 doi: 10.16383/j.aas.c230430
              引用本文: 胡開(kāi)成, 嚴愛(ài)軍, 湯健. 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. 自動(dòng)化學(xué)報, 2024, 50(5): 1001?1014 doi: 10.16383/j.aas.c230430
              Hu Kai-Cheng, Yan Ai-Jun, Tang Jian. Multi-target robust prediction model for furnace temperature and flue gas oxygen content in municipal solid waste incineration process. Acta Automatica Sinica, 2024, 50(5): 1001?1014 doi: 10.16383/j.aas.c230430
              Citation: Hu Kai-Cheng, Yan Ai-Jun, Tang Jian. Multi-target robust prediction model for furnace temperature and flue gas oxygen content in municipal solid waste incineration process. Acta Automatica Sinica, 2024, 50(5): 1001?1014 doi: 10.16383/j.aas.c230430

              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
              基金項目: 國家自然科學(xué)基金 (62373017, 62073006), 北京市自然科學(xué)基金 (4212032) 資助
              詳細信息
                作者簡(jiǎn)介:

                胡開(kāi)成:北京工業(yè)大學(xué)信息學(xué)部博士研究生. 主要研究方向為復雜過(guò)程建模與智能優(yōu)化控制. E-mail: hukaicheng@emails.bjut.edu.cn

                嚴愛(ài)軍:北京工業(yè)大學(xué)信息學(xué)部教授. 主要研究方向為復雜過(guò)程建模與智能優(yōu)化控制. 本文通信作者. E-mail: yanaijun@bjut.edu.cn

                湯?。罕本┕I(yè)大學(xué)信息學(xué)部教授. 主要研究方向為小樣本數據建模, 城市固廢處理過(guò)程智能控制. E-mail: freeflytang@bjut.edu.cn

              Multi-target Robust Prediction Model for Furnace Temperature and Flue Gas Oxygen Content in Municipal Solid Waste Incineration Process

              Funds: Supported by National Natural Science Foundation of China (62373017, 62073006) and Beijing Natural Science Foundation of China (4212032)
              More Information
                Author Bio:

                HU Kai-Cheng Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology. His research interest covers complex process modeling and intelligent optimization control

                YAN Ai-Jun Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers complex process modeling and intelligent optimization control. Corresponding author of this paper

                TANG Jian Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers small sample data modeling and intelligent control of municipal solid waste treatment process

              • 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.
              • 圖  1  MSWI 工藝流程

                Fig.  1  MSWI process flow

                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  4  不同異常值比例下的aRMSE變化曲線(xiàn)

                Fig.  4  aRMSE change curves with different outlier percentages

                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CSV

                A1  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
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                        • 收稿日期:  2023-07-13
                        • 網(wǎng)絡(luò )出版日期:  2024-03-19
                        • 刊出日期:  2024-05-29

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