مهندسی شیمی ایران

مهندسی شیمی ایران

توسعۀ مدل جایگزین داده‌محور برای شبیه‌سازی و تحلیل سریع رفتار موجودی شبکه‌های انتقال گاز طبیعی در پیک مصرف

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی کارشناسی ارشد مهندسی شیمی، دانشگاه تربیت مدرس
2 استادیار مهندسی شیمی، دانشگاه تربیت مدرس
چکیده
در سال‌های اخیر، ایران به‌دلیل پیک مصرف گاز در روزهای سرد و نبود واردات یا ذخیرۀ کافی، با کمبود گاز طبیعی و خسارت‌های ناشی از آن مواجه‌بوده‌است که مدیریت موجودی خطوط لوله انتقال گاز را به راه‌کاری اساسی تبدیل‌کرده‌است. شبیه‌سازی رفتار دینامیکی این سامانه‌ها به‌دلیل پیچیدگی روابط اجزای مختلف، زمان‌بر و پرهزینه است. در این پژوهش، یک مدل جایگزین داده‌محور برپایۀ شبکۀ عصبی بازگشتی با ورودی خارج (NARX) برای تحلیل و پیش‌بینی رفتار شبکه‌های انتقال گاز در شرایط پیک ‌مصرف توسعه‌یافته‌است. داده‌های شبه ‌تجربی با شبیه‌سازی دینامیکی واحد شبه ‌واقعی در Aspen HYSYS تولید و مدل با الگوریتم لونبرگ- مارکوارت آموزش‌داده‌شد. برای ارزیابی عملکرد، مقادیر میانگین خطای مطلق (MAE)، میانگین مربعات خطا (MSE) و ضریب تعیین R2 روی داده‌های آموزش و آزمون حساب‌شد. نتایج نشان‌می‌دهد که مدل در داده‌های آزمون دارای MAE 0/01474، MSE برابربا 0/002229 و R2 برابربا 0/9968 است. مدل پیشنهادی حدود 1133 برابر سریع‌تر است و کاهش زمان اجراء معادل 99/91%را با حفظ دقت نشان‌می‌دهد. این ترکیبِ سرعت و دقت امکان انجام تحلیل‌های ‌بلادرنگ و پشتیبانی از تصمیم‌گیری در شرایط پیک‌ مصرف را فراهم و مدل پیشنهادی را به ابزاری کارامد برای پیش‌بینی و مدیریت پیک‌سایی شبکه‌های انتقال گاز طبیعی تبدیل‌می‌کند.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Development of a Data-Driven Surrogate Model for Rapid Simulation and Analysis of the Behavior of Line Pack in Gas Transmission Networks During Peak Consumption

نویسندگان English

A. H. Shiri 1
M. Fakhroleslam 2
1 MSc. Student of Chemical Engineering, Tarbiat Modares University
2 Assistant Professor of Chemical Engineering, Tarbiat Modares University
چکیده English

In recent years, Iran has faced natural gas shortages during the cold months due to peak demand on cold days and the lack of sufficient imports or storage, making inventory management of gas transmission pipelines a critical solution. Simulating the dynamic behavior of such systems is often time-consuming and costly due to the complexity of interactions among their components. In this study, a data-driven surrogate model based on a Nonlinear Autoregressive Neural Network with eXogenous Inputs (NARX) is developed to analyze and predict the behavior of natural gas transmission networks under peak demand conditions. The quasi-experimental data required for training the NARX model were generated through dynamic simulation of a quasi-real unit in Aspen HYSYS, and the model was trained using the Levenberg–Marquardt algorithm.To evaluate the model’s performance, the Mean Absolute Error (MAE), Mean Squared Error (MSE) and Coefficient of Determination (R²) were calculated on both training and test datasets. The results show that on the test data, the model achieved MAE = 0.01474, MSE = 0.002229, and R² = 0.9968. Compared to full dynamic simulation, the proposed surrogate model is approximately 1133 times faster, corresponding to a 99.911% reduction in computation time, while maintaining high accuracy. This unique combination of speed and precision enables real-time analysis and decision-support applications during peak-demand scenarios, positioning the proposed NARX-based model as an efficient and reliable tool for forecasting and peak-shaving management in natural gas transmission networks.

کلیدواژه‌ها English

Rapid Simulation
Recurrent Neural Network
Surrogate Model
Linepack
Machine Learning
Peak-Shaving
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