骆正山,欧阳长风,王小完,张新生.盐穴储气库注采管柱内腐蚀速率预测模型研究[J].表面技术,2022,51(6):283-290.
LUO Zheng-shan,OUYANG Chang-feng,WANG Xiao-wan,ZHANG Xin-sheng.Research on Prediction Model of Internal Corrosion Rate in Injection and Production String of Salt Cavern Gas Storage[J].Surface Technology,2022,51(6):283-290 |
| 盐穴储气库注采管柱内腐蚀速率预测模型研究 |
| Research on Prediction Model of Internal Corrosion Rate in Injection and Production String of Salt Cavern Gas Storage |
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| DOI：10.16490/j.cnki.issn.1001-3660.2022.06.026 |
| 中文关键词: 盐穴储气库 注采管柱 腐蚀速率预测 主成分分析法(KPCA) 改进灰狼优化(IGWO) 极限学习机(ELM) |
| 英文关键词:salt cavern gas storage injection and production string corrosion rate prediction principal component analysis (KPCA) improved gray wolf optimization (IGWO) extreme learning machine (ELM) |
| 基金项目:国家自然科学基金(41877527);陕西省社科基金(2018S34) |
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| Author | Institution |
| LUO Zheng-shan | School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China |
| OUYANG Chang-feng | School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China |
| WANG Xiao-wan | School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China |
| ZHANG Xin-sheng | School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China |
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| 中文摘要: |
| 目的 提升盐穴储气库注采管柱的内腐蚀速率预测精度，以保障盐穴储气库的设施健康和运行安全。方法 建立基于小波核主成分分析(KPCA)和改进灰狼算法(IGWO)优化的极限学习机(ELM)腐蚀速率预测模型。以某盐穴储气库注采管柱为例。首先选取10种腐蚀影响因素，建立盐穴储气库注采管柱的内腐蚀指标体系;其次通过小波KPCA提取影响注采管柱内腐蚀的关键特征，后利用IGWO对ELM模型参数 和 进行迭代寻优，进而建立IGWO–ELM盐穴储气库注采管柱内腐蚀速率预测模型;最后在MATLAB中进行仿真计算，将IGWO–ELM模型与ELM、PSO–ELM、SSA–ELM模型进行预测误差对比。结果 经小波KPCA特征提取后得到包含98.61%原信息的3项主成分，IGWO–ELM模型的预测结果与实际值吻合度高，其均方根误差为0.008 8，平均绝对百分比误差为0.260 9%，决定系数(R2)高达0.992 5，比其他3个对比模型的性能更优。结论 小波KPCA特征提取能力优良，IGWO–ELM模型能够有效预测盐穴储气库注采管柱的内腐蚀速率，为盐穴储气库注采管柱的腐蚀研究提供了新的思路与方法。 |
| 英文摘要: |
| The injection and production string of the salt cavern gas storage has been in a complex underground environment for a long time, making it susceptible to a variety of corrosion factors. This work aims to improve the prediction accuracy of the corrosion rate in the injection and production string of the salt cavern gas storage, thereby ensuring the health and operational safety of these facilities. To accomplish the above objectives, the solution proposed is to establish an internal corrosion rate prediction model based on wavelet kernel principal component analysis (KPCA) and an extreme learning machine (ELM) after improved gray wolf optimization (IGWO). First of all, in the actual operation data of the injection and production string of the salt cavern gas storage, 10 indicators with larger corrosion factors are selected, such as:partial pressure of carbon dioxide, hydrogen sulfide partial pressure, inner wall surface temperature, etc. Subsequently, the internal corrosion index system of the injection and production string of the salt cavern gas storage was established. Secondly, the wavelet KPCA is used to extract the key features that affect the internal corrosion rate of the injection and production string, and then IGWO is used to iteratively optimize the input weight matrix and hidden layer threshold of the ELM model, and stop the loop until the termination condition is met. Furthermore, a prediction model of corrosion rate in the injection and production string of IGWO-ELM salt cavern gas storage is established. Finally, numerical simulation and simulation calculation are carried out in MATLAB software, and the prediction errors of the IGWO-ELM model are compared with the three models of ELM, PSO-ELM and SSA-ELM respectively. The research results show that the wavelet KPCA effectively extracts the three principal components that contain 98.61% of the original information in the corrosion data of the injection-production pipe string of the salt cavern gas storage. Applying the reconstructed corrosion data to the ELM, PSO-ELM, SSA-ELM, and IGWO-ELM models, their average relative errors are 9.404 8%, 5.061 5%, 1.573 7%, and 0.707 3%. The prediction results of the IGWO-ELM model are in good agreement with the actual values. The root mean square error of the constructed IGWO-ELM model is 0.008 8, the average absolute percentage error is 0.260 9%, and the coefficient of determination (R2) is as high as 0.992 5. Its prediction result is better than the other three comparison models. The kernel principal component analysis with the introduction of wavelet kernel function has an excellent ability to extract corrosion characteristics of the injection and production string of the salt cavern gas storage. Within the applicable range of certain working conditions, the established IGWO-ELM model can effectively predict the internal corrosion rate of the injection and production string of the salt cavern gas storage. It not only provides a reference basis for the integrity evaluation and risk warning of the injection and production system of the salt cavern gas storage, but also provides new ideas and methods for the corrosion study of the injection and production string of the salt cavern gas storage. |
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