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Iranian Journal of Chemistry and Chemical Engineering، جلد ۴۴، شماره ۲، صفحات ۵۷۲-۵۸۲
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عنوان انگلیسی Development of Corrosion Prediction Approach for Natural Gas Pipelines: A Novel Deep Learning SVM Method
چکیده انگلیسی مقاله Managing corrosion in oil and gas pipelines poses significant challenges due to the complex nature of corrosion processes, including their initiation, progression, and stabilization. This research introduces an advanced hybrid prediction model, EMD–IPSO–SVM, designed to forecast internal corrosion in natural gas pipelines through a four-step process: data preprocessing, optimization, prediction, and evaluation. The model is validated using a dataset of 120 samples from natural gas pipelines in southwestern Iran. The EMD algorithm is employed to reduce noise and highlight key features of the corrosion data, while stratified sampling ensures accurate and unbiased separation of training and test datasets. An enhanced particle swarm optimization method is used to fine-tune the parameters of the support vector regression model. The model’s performance is assessed comprehensively, showing impressive results with a Prediction Effectiveness (PE) value of 0.89, a Grey Relational Degree (GRD) of 0.80, a Root Means Square Error (RMSE) of 0.044, a root mean squared error of prediction (RMSEP) of 0.041, a coefficient of determination of 0.925, and a Mean Absolute Percentage Error (MAPE) of 5.73%. These metrics indicate that the hybrid model outperforms current state-of-the-art models, offering enhanced prediction accuracy. This approach not only improves corrosion control but also supports the digital transformation efforts within the corrosion management industry.
کلیدواژه‌های انگلیسی مقاله Support vector machines (SVMs),Particle Swarm Optimization (PSO),Corrosion,prediction,Gas Pipeline
نویسندگان مقاله Zahra Naserzadeh |
Faculty of Environment, University of Tehran, Tehran, IR. IRAN

Ahmad Nohegar |
Faculty of Environment, University of Tehran, Tehran, IR. IRAN

نشانی اینترنتی https://ijcce.ac.ir/article_718327_de1ce304cdcf8ad3d35c5b7014c3c332.pdf
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