ارائه مدلی برای پیشبینی ریسک حوادث نشت از خطوط لوله انتقال نفت بر اساس تلفیق روشهای کنت مولبایر و شبکههای عصبی مصنوعی پیش بینی ریسک
ارتقای ایمنی و پیشگیری از مصدومیت ها,
دوره 13 شماره 2 (1404),
11 تیر 2026
,
صفحه 6-19
https://doi.org/10.22037/iipm.v13i2.52128
چکیده
سابقه و هدف: تحقیق حاضر با هدف ارائه مدلی برای پیشبینی ریسک حوادث نشت از 2 خط لوله 42 و 48 اینچ در جنوب غرب ایران، بر اساس تلفیق روشهای کنت مولبایر و شبکههای عصبی مصنوعی در سال 2023 صورت گرفته است.
روش کار: در این تحقیق، با استفاده از ۵ شاخص روش کنتمولبایر (شامل خسارت شخص ثالث، خوردگی، طراحی، عملکرد نادرست و شدت اثر نشت) به عنوان متغیرهای ورودی، شبکههای پرسپترون چندلایه (MLP) و تابع پایه شعاعی (RBF) با هدف تخمین سطوح ریسک وقوع حوادث در ۱۰ ناحیه مختلف برای خطوط لوله، توسعه داده شدند. شبکههای مصنوعی از یک لایه ورودی، لایههای پنهان و یک لایه خروجی تشکیل شدند. در نهایت بر اساس شاخصهای MSE و R، نتایج مورد مقایسه قرار گرفت.
یافتهها: نتایج تحقیق نشان داد که سطح ریسک در منطقه 5 (محدوده زیر رودخانه جراحی)، با عدد اولویت 2.7684 برای خط لوله 42 اینچ و 2.8475 برای خط لوله 48 اینچ، دارای بدترین وضعیت در روش کنت مولبایر بوده است. سطوح ریسک مربوط به خط 48 اینچ، بیش از خط 42 اینچ بوده است. نتایج توسعه شبکه عصبی مصنوعی نشان داد مقدار MSE برای دو شبکه ANN و RBF به ترتیب 0.00015891 و 0.0002917 و مقدار شاخص R به ترتیب 0.7767 و 0.7227 بود که قابل قبولی را برای دادههای مستخرج از قضاوتهای کیفی نشان داد. شبکه MLP کارایی بهتری نسبت به شبکه RBF داشته است.
نتیجهگیری: با در نظر گرفتن مناطق مسکونی و جمعیت ساکن در مناطق محدوده خط انتقال نفت، اکوسیستمهای حساس مانند دریاچه سد مخزنی، رودخانه جراحی، جنگل کم تراکم منطقهای و مناطق شهری، خطوط انتقال نفت، پتانسیل قابل توجهی در بروز حوادث دارند.
- ارزیابی ریسک
- خطوط لوله
- شبکه RBF
- شبکه MLP
- شبکههای عصبی مصنوعی
- آلودگی نفتی
ارجاع به مقاله
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