تخمین ارتفاع سطح LNAPL در آبخوانهای آلوده به نفت با استفاده از برنامه‌نویسی بیان ژن (GEP)، سیستم استنتاج فازی (ANFIS) و روش رگرسیون چند متغیره (MLR)
محورهای موضوعی :فاطمه ابراهیمی 1 * , محمد نخعي 2 , حميدرضا ناصري 3 , کمال خدایی 4
1 - دانشکده علوم زمین، دانشگاه خوارزمی، تهران
2 - دانشکده علوم زمین، دانشگاه خوارزمی، تهران
3 - دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران
4 - پژوهشکده علوم پایه کاربردی جهاد دانشگاهی
کلید واژه: نوسانات LNAPL , برنامه‌نویسی بیان ژن, سیستم استنتاج تطبیقی فازی, رگرسیون چند متغیره,
چکیده مقاله :
یکی از مهم‌ترین نگرانیها در آبخوانهای مجاور به تاسیسات نفتی، نشت LNAPL ها میباشد. بازیافت LNAPLها همواره مشکل و پرهزینه است. نخستین مرحله در برنامه‌ریزی چنین سیستمهایی، تعیین اهداف طراحی میباشد، اغلب بیشینهسازی برداشت آلاینده، و کمینه سازی هزینه به‌عنوان مهم‌ترین اهداف طراحی در نظر گرفته میشوند. شناسایی ضخامت LNAPL و نوسانات آن میتواند تعیین‌کننده روش بازیافت، بیشینهسازی برداشت و کاهش هزینه آن شود. در این مطالعه از سه روش برنامه‌نویسی بیان ژن ، سیستم استنتاج تطبیقی فازی ، و رگرسیون چند متغیره ، برای تخمین و پیش‌بینی ارتفاع سطح LNAPL استفاده شده است. متغیرهای ورودی شامل ارتفاع سطح آب زیرزمینی و نرخ تخلیه LNAPL و متغیر خروجی ارتفاع سطح LNAPL میباشد. نتایج اجرای سه مدل توسط پارامترهای آماری مورد تحلیل و بررسی قرار گرفت و مشخص شد که برنامه‌نویسی بیان ژن دارای نتایج بهتری میباشد و میتواند به‌طور موفقیت‌آمیزی در پیش‌بینی نوسانات سطح LNAPL در فرایندههای Recovery مورد استفاده قرار گیرد. همچنین توسط مدل GEP یک معادله برای پیش‌بینی سطح LNAPL ارائه شد که می‌توان آن را در سر چاه برای پیش‌بینی ارتفاع سطح LNAPL استفاده کرد.
One of the main concerns in the aquifers adjacent to oil facilities is the leakage of LNAPLs. Since remediation processes costly and time consuming, so the first step in these systems is determining design goals. Often the most important goal of these systems is to maximize pollutant removal and minimize the cost. Identifying the thickness of LNAPL and its fluctuations can determine the type of recovery method and thus can be effective on the amount of removal and the cost of the implementation. In this study, three methods of gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and multivariate linear regression (MLR) were used to estimate and predict the LNAPL level. Input variables are groundwater level elevation and discharge rate of LNAPL and the output variable is the LNAPL level elevation. The results of the three models were analyzed by statistical parameters and it was determined that GEP technique has better results and could be used successfully in predicting LNAPL level fluctuations in recovery processes. Also, the GEP model provides an equation for predicting the LNAPL level that can be used in the field to predict the elevation of the LNAPL level.
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