基于正交信號校正和穩健回歸的帶鋼酸洗濃度預測模型

何飛; 王保健; 黎敏; 趙廣滿

doi:10.13374/j.issn1001-053x.2013.02.011

基于正交信號校正和穩健回歸的帶鋼酸洗濃度預測模型

doi: 10.13374/j.issn1001-053x.2013.02.011

何飛^1,2,
王保健²,
黎敏^1,2, ,,
趙廣滿³

1. 北京科技大學國家板帶生產先進裝備工程技術研究中心, 北京 100083;
2. 北京科技大學機械工程學院, 北京 100083;
3. 鞍鋼股份有限公司冷軋廠, 鞍山 114030

基金項目:

"十二五"國家科技支撐計劃資助項目(2012BAF04B02)

國家自然科學基金資助項目(51004013,51204018)

中央高校基本科研業務費專項(FHF-TP-12-167A.FRF-AS-09-008B)

高等學校博土學科點專項科研基金資助項目(20110006110027)

詳細信息

通訊作者:
黎敏,E-mail:limin@ustb.edu.cn

中圖分類號: TG335.12
計量
- 文章訪問數: 190
- HTML全文瀏覽量: 44
- PDF下載量: 5
- 被引次數: 0
出版歷程
- 收稿日期: 2012-10-08

Acid concentration prediction model of steel pickling process based on orthogonal signal correction and robust regression

HE Fei^1,2,
WANG Bao-jian²,
LI Min^{1,2
, ,},
ZHAO Guang-man³

1. National Engineering Research Center of Flat Rolling Equipniont, University of Science and Technology Beijing, Beijing 100083, China;
2. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China;
3. Cold Rolling Plant, Angang Steel Company Limited, Anshan 114030, China

摘要

摘要: 為了實時獲得冷軋帶鋼酸洗溶液的濃度值,便于進行酸濃度控制,采用軟測量方法實時預測酸濃度.由于酸濃度建模數據中無關成分和特異點會影響模型精度,利用正交信號校正和穩健回歸相結合的方法來建立酸濃度預測模型首先利用正交信號校正對建模數據進行預處理,去除自變量中與因變量無關的成分;然后采用基于迭代加權最小二乘的穩健回歸算法進行建模,降低特異點對模型的影響;最后將預測結果和多元線性回歸、傳統穩健回歸方法和正交信號校正多元線性回歸進行比較.實驗結果表明:采用正交信號校正-穩健回歸方法后,模型預測能力得到提高,與多元線性回歸結果相比,亞鐵離子質量濃度和氫離子質量濃度的相對預測誤差分別從1.82%降低到1.17%、從5.87%降低到4.73%.本文提出的方法具有更好的模型預測精度,可以滿足工業應用要求.
- 冷軋 /
- 帶鋼 /
- 酸洗 /
- 濃度 /
- 預測 /
- 數學模型
Abstract: In order to get and control acid concentration values in cold-rolled strip steel pickling, a soft measurement method was proposed for real-time predicting the acid concentration. Because of the influence of irrelevant components and outliers in acid concentration data on the accuracy of the acid concentration prediction model, orthog-onal signal correction (OSC) and iterative weighted least squares (IRLS) regression were combined to build the model. Firstly, orthogonal signal correction was used to remove irrelevant components which have nothing to do with tile mea-sured variables. Then robust regression based on the iteratively reweighted least squares algorithm was applied in the model to reduce the influence of outliers. Finally, the prediction results were compared with multiple linear regression (MLR), IRLS, and OSC-MLR. It is found that OSC-IRLS has the best prediction accuracy. In comparison with MLR, the relative error of OSC-IRLS decrease from 1.82% to 1.17% in predicting the concentration of ferrous ions and from 5.87% to 4.73% in predicting the concentration of hydrogen ions. The proposed method has a better model prediction accuracy to meet the requirements of industrial applications.
- cold rolling /
- strip steel /
- pickling /
- concentration /
- prediction /
- mathematical models