北京地區土壤腐蝕性關鍵參量與Q235鋼腐蝕速率預測模型研究

尹志彪; 王莎莎; 祝振洪; 谷少杰; 馬帥杰; 杜艷霞; 江社明

doi:10.13374/j.issn2095-9389.2022.09.13.002

北京地區土壤腐蝕性關鍵參量與Q235鋼腐蝕速率預測模型研究

doi: 10.13374/j.issn2095-9389.2022.09.13.002

1.
鋼鐵研究總院，北京 100081
2.
北京市燃氣集團有限責任公司，北京 100035
3.
北京科技大學新材料技術研究院，北京 100083

詳細信息

通訊作者:
E-mail：duyanxia@ustb.edu.cn

中圖分類號: TE988.2
計量
- 文章訪問數: 159
- HTML全文瀏覽量: 21
- PDF下載量: 27
- 被引次數: 0
出版歷程
- 收稿日期: 2022-09-13
- 網絡出版日期: 2023-02-07

Key parameters of soil corrosivity and a model for predicting the corrosion rate of Q235 steel in Beijing

1.
Central Iron & Steel Research Institute, Beijing 100081, China
2.
Beijing Gas Group Company Limited, Beijing 100035, China
3.
Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: E-mail: duyanxia@ustb.edu.cn

摘要

摘要: 在北京101處不同地理位置進行了土壤現場取樣，并在實驗室對土壤樣品的9項理化參數進行了測試，獲得了北京地區的土壤參數分布范圍. 通過機器學習分析得到了Q235鋼在北京地區土壤中腐蝕速率的關鍵影響因素為自腐蝕電位、土壤含水率以及土壤電阻率，基于隨機森林算法建立了Q235鋼在北京土壤中的腐蝕速率預測模型，預測值與實際值的平均絕對誤差小于5%. 為了進一步探究Q235鋼在北京土壤中的腐蝕速率與3項關鍵土壤參數之間的關系，利用已建立的腐蝕速率預測模型，以自腐蝕電位、土壤電阻率以及土壤含水率3項關鍵參數為輸入量，以Q235鋼腐蝕速率為輸出量進行預測分析，預測結果表明：當自腐蝕電位在?0.57 V(vs SCE)～?0.70 V(vs SCE)之間、含水率在13%～22%以及土壤電阻率在45 Ω·m～65 Ω·m之間時，碳鋼在土壤中的腐蝕速率較高，超過了0.1 mm·a^?1，該結果為低碳鋼在北京地區土壤中的腐蝕評估提供了相對簡單的方法.
- 北京土壤 /
- Q235鋼 /
- 腐蝕速率 /
- 關鍵參量 /
- 機器學習 /
- 腐蝕預測
Abstract: Soil samples were excavated from 101 geographical locations in Beijing and transported back to a laboratory. The samples were tested for nine physical and chemical parameters, and the distribution ranges of the soil parameters were obtained. The soil in Beijing is mainly loam, involving clay and sand, with the pH being mainly neutral or weakly alkaline; its chloride content is low. Additionally, the soil parameters that vary substantially are the moisture content, resistivity, self-corrosion potential, redox potential, and self-corrosion current density. Herein, because of the long period required, in addition to the difficulty of burying corrosion-inspection pieces in the field, weight-loss experiments were performed in seven locations. Moreover, the corrosion rates calculated using Faraday’s law and the weight-loss method were compared and verified for seven locations. The results revealed that the corrosion rate obtained using Faraday’s law is consistent with that obtained using the weight-loss method. Therefore, the corrosion-rate data obtained using Faraday’s law in the laboratory have a certain practical significance; such data can provide support for follow-up research and analysis. The characteristics of the soil parameters and the correlation among different such parameters were obtained using the machine learning random-forest algorithm and Pearson coefficient analysis. The results reveal the soil self-corrosion potential, water content, and resistivity to be the key factors affecting the Q235 steel corrosion rate for the Beijing soil. The corrosion–rate prediction model of Q235 steel for the Beijing soil was established based on the machine learning random-forest algorithm. An average absolute error of <5% (which is small) was found between the predicted and actual values of the corrosion rate. The prediction model can, therefore, better reflect the soil corrosivity in Beijing, which has a certain practical significance. To further explore the relationship between the Q235 steel corrosion rate for the Beijing soil and the three key soil parameters, the established prediction model was employed. Taking the soil self-corrosion potential, resistivity, and moisture content as the input, the Q235 steel corrosion rate was predicted as the output and was analyzed. The prediction results show that when the soil self-corrosion potential is between ?0.57 V(vs SCE) and ?0.70 V(vs SCE), the soil moisture content is between 13% and 22% and when the soil resistivity is between 45 and 65 Ω·m, the corrosion rate of carbon steel in the soil is higher than 0.1 mm·a^?1. This work provides a simple method for assessing the corrosion of low-carbon steel in Beijing.
- Beijing soil /
- Q235 steel /
- corrosion rate /
- key parameters /
- machine learning /
- corrosion prediction

HTML全文

圖 1 北京101處不同地點

Figure 1. 101 locations of Beijing that are considered in this study

下載: 全尺寸圖片幻燈片

圖 2 機器學習流程圖

Figure 2. Machine learning flow chart

下載: 全尺寸圖片幻燈片

圖 3 法拉第定律與失重法獲得腐蝕速率結果對比圖

Figure 3. Comparison of the corrosion rates using Faraday’s law and the weight-loss method

下載: 全尺寸圖片幻燈片

圖 4 土壤各理化參數的特征重要性

Figure 4. Importance of soil physical and chemical parameters

下載: 全尺寸圖片幻燈片

圖 5 土壤各理化參數間的相關性

Figure 5. Correlation among soil physical and chemical parameters

下載: 全尺寸圖片幻燈片

圖 6 不同地點腐蝕速率預測值與真實值對比圖

Figure 6. Comparison of predicted and true corrosion rates at different sites

下載: 全尺寸圖片幻燈片

圖 7 土壤各理化參數的均方根誤差

（0—土壤電阻率；1—氧化還原電位；2—土壤含水率；3—pH；4—自腐蝕電位；5—土壤含鹽量；6—氯離子含量）

Figure 7. Root mean square errors (RMSE) of soil physical and chemical parameters

(0—soil resistivity; 1—redox potential; 2—soil moisture content; 3—pH; 4—soil self-corrosion potential; 5—soil salt content; 6—chloride content)

下載: 全尺寸圖片幻燈片

圖 8 土壤電阻率、自腐蝕電位和腐蝕速率的關系圖

Figure 8. Relationship between the soil resistivity, self-corrosion potential, and corrosion rate

下載: 全尺寸圖片幻燈片

圖 9 土壤含水率、自腐蝕電位和腐蝕速率的關系圖

Figure 9. Relationship between soil moisture content, self-corrosion potential, and corrosion rate

下載: 全尺寸圖片幻燈片

表 1 Q235鋼成分表(質量分數)

Table 1. Q235–steel composition contents %

C	Si	Mn	P	S	Al	Fe
0.21	0.043	0.29	0.022	0.009	0.025	Bal

下載: 導出CSV

表 2 北京市土壤各理化參數的范圍

Table 2. Ranges of physical and chemical parameters of the Beijing soil

Soil texture	pH	Soil moisture content/%	Redox potential/ mV(vs SCE)	Soil resistivity/ (Ω·m)	Soil self-corrosion potential/V(vs SCE)	Chloride-ion content/%	Soil salt content/%	Soil self-corrosion current density/ (A·m^?2)
Loam soil	6.9–9.0	5–27	37.6–357.8	13.2–131.5	(?0.4)–(?0.79)	4.1 × 10^?4– 3.94 × 10^?3	0.03–0.245	1.72 × 10^?4–9.95 × 10^?2

下載: 導出CSV

表 3 預測誤差分析統計

Table 3. Prediction-error analysis statistics

RMSE	MAE	R²
0.0134	0.00026	0.9048

下載: 導出CSV

259luxu-164

參考文獻(21)

[1]	Wan Y Y, Wu M, Xu D J, et al. Failure characteristics of gas pipelines and corrosion-induced microorganisms of soils in Beijing. Corros Prot Petrochem Ind, 2021, 38(2): 1 萬云洋, 武旻, 徐得甲, 等. 北京市燃氣管材失效與土壤腐蝕性微生物特征研究. 石油化工腐蝕與防護, 2021, 38(2):1
[2]	Hirata R, Yonemoto W, Ooi A, et al. Influence of soil particle size, covering thickness, and pH on soil corrosion of carbon steel. ISIJ Int, 2020, 60(11): 2533 doi: 10.2355/isijinternational.ISIJINT-2020-261
[3]	Cole I S, Marney D. The science of pipe corrosion: A review of the literature on the corrosion of ferrous metals in soils. Corros Sci, 2012, 56: 5 doi: 10.1016/j.corsci.2011.12.001
[4]	Hirata R, Ooi A, Tada E J, et al. Influence of the degree of saturation on carbon steel corrosion in soil. Corros Sci, 2021, 189: 109568 doi: 10.1016/j.corsci.2021.109568
[5]	Wang W H, Shen S M, Yu X C. Review on research methods of soil corrosion for buried pipeline steels. J Nanjing Univ Technol (Nat Sci Ed), 2008, 30(4): 105 王文和, 沈士明, 於孝春. 埋地管道鋼土壤腐蝕研究方法進展. 南京工業大學學報(自然科學版), 2008, 30(4):105
[6]	Mi X X, Tang A T, Zhu Y C, et al. Research progress of machine learning in material science. Mater Rep, 2021, 35(15): 15115 米曉希, 湯愛濤, 朱雨晨, 等. 機器學習技術在材料科學領域中的應用進展. 材料導報, 2021, 35(15):15115
[7]	Guo Z Y. Study on Corrosive Mechanism and Zoning Evaluation of Steel under Soil Environment in Shanxi Province [Dissertation]. Taiyuan: Taiyuan University of Technology, 2020 郭志遠. 山西省土壤環境對鋼的腐蝕性分區評價及其機理研究[學位論文]. 太原:太原理工大學, 2020
[8]	Liang P, Du C W, Yu J, et al. Analysis of factors on soil corrosion for buried Q235 steel in ku’erle region. Corros Sci Prot Technol, 2010, 22(2): 146 梁平, 杜翠薇, 余杰, 等. Q235鋼在庫爾勒地區土壤腐蝕性的影響因素分析. 腐蝕科學與防護技術, 2010, 22(2):146
[9]	Li L, Li X G, Xing S B, et al. Research on soil corrosion rate prediction of carbon steel in typical Chinese cities based on BP artificial neural network. Corros Sci Prot Technol, 2013, 25(5): 372 李麗, 李曉剛, 邢士波, 等. BP人工神經網絡對國內典型地區碳鋼土壤腐蝕的預測研究. 腐蝕科學與防護技術, 2013, 25(5):372
[10]	Liu J. Research on Soil Corrosion of 20# Steel Buried Gas Pipelines in Chongqing [Dissertation]. Chongqing: Chongqing University, 2013 劉靜. 重慶市20#鋼質燃氣埋地管道的土壤腐蝕研究[學位論文]. 重慶:重慶大學, 2013
[11]	Luo X H. Evaluation of soil corrosion grade based on IAHP and fuzzy grey theory. Mater Prot, 2019, 52(7): 54 羅小虎. 基于IAHP和模糊灰色理論的土壤腐蝕等級評價. 材料保護, 2019, 52(7):54
[12]	Qin X X. Research on Soil Corrosion and Protection of Buried Pipelines [Dissertation]. Beijing: China University of Petroleum, 2009. 秦曉霞. 埋地管道土壤腐蝕性與防護研究[學位論文]. 北京:中國石油大學, 2009
[13]	Ding L, Rangaraju P, Poursaee A. Application of generalized regression neural network method for corrosion modeling of steel embedded in soil. Soils Found, 2019, 59(2): 474 doi: 10.1016/j.sandf.2018.12.016
[14]	Li Y A, Luo Z S. Soil corrosion depth prediction of buried pipelines based on KPCA–ICS–ELM algorithm. China Saf Sci J, 2020, 30(12): 100 李易安, 駱正山. 基于KPCA–ICS–ELM算法的埋地管線土壤腐蝕深度預測. 中國安全科學學報, 2020, 30(12):100
[15]	Qu L S, Li X G, Du C W, et al. Corrosion rate prediction model of carbon steel in regional soil based on BP artificial neural network. J Univ Sci Technol Beijing, 2009, 31(12): 1569 曲良山, 李曉剛, 杜翠薇, 等. 運用BP人工神經網絡方法構建碳鋼區域土壤腐蝕預測模型. 北京科技大學學報, 2009, 31(12):1569
[16]	General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization Administration of the People’s Republic of China. GB/T 19285— 2014 Inspection of Corrosion Protection for Buried Steel Pipelines. Beijing: Standards Press of China, 2014 中華人民共和國國家質量監督檢驗檢疫總局, 中國國家標準化管理委員會. GB/T 19285—2014 埋地鋼質管道腐蝕防護工程檢驗. 北京:中國標準出版社, 2014
[17]	Wei P F, Lu Z Z, Song J W. Variable importance analysis: A comprehensive review. Reliab Eng Syst Saf, 2015, 142: 399 doi: 10.1016/j.ress.2015.05.018
[18]	Zhi Y J. Data-Driven Prediction Model for Small Sample Data of Metal Materials Atmospheric Corrosion [Dissertation]. Beijing: University of Science and Technology Beijing, 2019 支元杰. 大氣環境下小樣本金屬材料腐蝕的數據驅動預測模型[學位論文]. 北京:北京科技大學, 2019
[19]	Chen Y Y, Zheng W Z, Li W B, et al. Large group activity security risk assessment and risk early warning based on random forest algorithm. Pattern Recognit Lett, 2021, 144: 1 doi: 10.1016/j.patrec.2021.01.008
[20]	Saokaew S, Permsuwan U, Chaiyakunapruk N, et al. Effectiveness of pharmacist-participated warfarin therapy management: A systematic review and meta-analysis. J Thromb Haemost, 2010, 8(11): 2418 doi: 10.1111/j.1538-7836.2010.04051.x
[21]	Laura A, Silvia M, Zuzana M, et al. Systematic review of safeness and therapeutic efficacy of cannabis in patients with multiple sclerosis, neuropathic pain, and in oncological patients treated with chemotherapy. Epidemiol Prev, 2017, 41(5-6): 279