Development of Corrosion Prediction Approach for Natural Gas Pipelines: A Novel Deep Learning SVM Method

سال انتشار: 1404
محل انتشار: Iranian Journal of Chemistry and Chemical Engineering، دوره: 44، شماره: 2
کد COI اختصاصی: JR_IJCCE-44-2_019
زبان مقاله: انگلیسی
تعداد مشاهده: 67

نویسندگان

Faculty of Environment, University of Tehran, Tehran, IR. IRAN

چکیده

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 ۱۲۰ 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 ۰.۸۹, a Grey Relational Degree (GRD) of ۰.۸۰, a Root Means Square Error (RMSE) of ۰.۰۴۴, a root mean squared error of prediction (RMSEP) of ۰.۰۴۱, a coefficient of determination of ۰.۹۲۵, and a Mean Absolute Percentage Error (MAPE) of ۵.۷۳%. 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.

کلیدواژه ها

Support vector machines (SVMs), Particle Swarm Optimization (PSO), Corrosion, prediction, Gas Pipeline

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.