Multi-target Robust Prediction Model for Furnace Temperature and Flue Gas Oxygen Content in Municipal Solid Waste Incineration Process
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摘要: 為實現城市固廢焚燒(Municipal solid waste incineration, MSWI)過程爐溫與煙氣含氧量的準確預測, 提出一種基于改進隨機配置網絡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, 通過信息疊加與跨越連接來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對模型參數進行稀疏約束, 以建模爐溫與煙氣含氧量間的相關性; 接著, 采用混合拉普拉斯分布作為每個目標建模誤差的先驗分布, 通過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程的歷史數據對所提建模方法的性能進行測試. 實驗結果表明, 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} $-norm regularization terms to sparsely constrain the model parameters to model the correlation between furnace temperature and flue gas oxygen content. Then, the mixture Laplace distribution is used as the prior distribution of each target modeling error, and the output weights of the SCN model are re-evaluated by maximum a posteriori estimation to enhance its robustness. Finally, the performance of the proposed modeling method is tested on the historical data of municipal solid waste incineration process. The experimental results show that the proposed modeling method has advantages in prediction accuracy and robustness.
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圖 2 前饋神經網絡隱含層構造方式
Fig. 2 The hidden layer construction methods of feedforward neural network
圖 3 不同范數約束在原始數據集上的實驗結果
Fig. 3 Results of experiments with different paradigm constraints on the original dataset
圖 5 爐溫與煙氣含氧量散點圖及預測誤差概率分布曲線$(\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與不同類型建模方法在原始數據集上的對比實驗結果
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 
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表 2 MRI-SCN 與同類型建模方法在原始數據集上的對比實驗結果
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
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表 3 四組噪聲數據集上的實驗結果
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
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表 4 不同建模方法運行 30 次的時間對比
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 時間(s) 21.43 31.70 5.23 16.29 36.22 37.78 28.17
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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 單位 1 進料器左內側速度 % 2 進料器左外側速度 % 3 進料器右內側速度 % 4 進料器右外側速度 % 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 二次風量 $ {\rm {km^3N/h}} $ 24 一次風機出口空氣壓力 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 當前時刻的爐溫 ℃ 47 當前時刻的煙氣含氧量 %
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