پیش‌بینی کارایی تعلیق شکن‌های تجاری جداکننده آب شور از امولسیون نفت خام به کمک ماشین بردار پشتیبان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی شیمی، دانشگاه صنعتی امیرکبیر (پلی تکنیک تهران)، تهران، ایرانن

2 دانشکده مهندسی شیمی، دانشگاه صنعتی امیرکبیر (پلی تکنیک تهران)، تهران، ایران

3 دانشکده مهندسی نساجی، دانشگاه صنعتی امیرکبیر (پلی تکنیک تهران)، تهران، ایران

چکیده

امولسیون آب شور در نفت خام موجب کاهش ارزش نفت خام، مسمومیت کاتالیست‌ها در صنایع پایین دستی و خوردگی تجهیزات در بخش‌های مختلف انتقال، پالایش و نگهداری نفت خام می‌شود. متداول‌ترین روش صنعتی جداسازی آب شور از نفت خام، استفاده از ترکیبات شیمیایی می باشد.تامین ترکیبات شیمیایی مورد نیاز هزینه قابل توجهی دارد. به علاوه عملکرد تعلیق شکن‌های شیمیایی وابستگی بسیار زیادی به نوع نفت خام دارد. پیچیدگی موجود در ساختار نفت خام باعث می‌شود مدل سازی عملکرد تعلیق شکن‌ها دشوار باشد. به منظور کاهش تعداد پارامترهای مهم نفت خام از نسبت آسفالتین به مجموع رزین و آروماتیک به عنوان مولفه نفت خام استفاده شد. با توجه به پیچیدگی مدل مدنظر، از ماشین بردار پشتیبان به منظور پیش‌بینی عملکرد تعلیق شکن‌های تجاری استفاده شد. مهم‌ترین چالش در ماشین‌های بردار پشتیبان تنظیم فراپارامترها می باشد. در این مطالعه برای تنظیم فراپارامترها از معیار ریسک در پیش بینی راندمان های بالاتر از 85% و افزایش ضریب همبستگی استفاده شد. به منظور جمع‌آوری داده های مدل‌سازی، چهار نمونه نفت خام به همراه دو نمونه تعلیق شکن تجاری رایج در واحدهای بهره‌برداری میادین جنوب غرب کشورتهیه و با استفاده از روش بطری، عملکرد تعلیق شکن تجاری در شرایط عملیاتی مختلف بررسی شد. عملکرد الگوریتم پیشنهادی برای تنظیم فراپارامترها با الگوریتم بهینه‌سازی بیزین مورد مقایسه قرارگرفت. نتایج نشان می‌دهد تنظیم فراپارامترهای ماشین بردار پشتیبان با معیار ریسک کمک می‌کند یک مدل با دقت بهتر برای پیش‌بینی عملکرد تعلیق شکن‌های تجاری طراحی شود. با توجه به اهمیت مدل‌سازی عملکرد مواد تعلیق شکن در صنعت نفت، اعتبار سنجی مدل با یک نمونه امولسیون جدید مورد بررسی قرار گرفت. نتایج نشان می‌دهد ماشین بردار پشتیبان می‌تواند مدلی کارامد برای پیش‌بینی تعلیق شکن‌های تجاری ارائه کند. 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Predicting the Performance of Commercial Demulsifiers for Separating Brine Water from Crude Oil Emulsion using Support Vector Machine

نویسندگان [English]

  • Hassan Talebi 1
  • Mehrdad Mozaffarian 2
  • Bahram Dabir 2
  • Nima Esmaeilian 3
1 Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
2 Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
3 Department of Textile Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
چکیده [English]

The emulsion of salt water in crude oil causes reduction of crude oil quality, catalyst poisoning in downstream industries, and equipment corrosion in various parts of crude oil transportation, refining and storage systems. The most common industrial method for separating saline water from crude oil is the application of chemical compounds. Procuring the required chemical compounds will cost a lot. In addition, the performance of chemical demulsifiers is highly dependent on the type and structure of crude oil. The complexity of crude oil’s structure makes it difficult to develop performance models for demulsifiers. In order to reduce the number of important parameters of crude oil in modeling, the ratio of asphaltene to the total of resin and aromatic was used as the discriminating factor of crude oil emulsion process. Considering the complexity of the considered model, a support vector machine was used to predict the performance of commercial demulsifiers. The most important challenge in support vector machines is tuning hyperparameters. To tune the hyperparameters in this study, the risk criterion was used in predicting efficiencies higher than 85% and increasing the correlation coefficient. In order to collect modeling data, four samples of crude oil along with two samples of common commercial demulsifiers were prepared in the Production units of the southwestern fields of the country, and the performance of commercial demulsifiers at different operating conditions was checked using the bottle test method. The performance of the proposed algorithm for tuning hyperparameters was compared with the Bayesian optimization algorithm. The results show that adjusting the support vector machine hyperparameter with the risk criterion helps to design a model with better accuracy for predicting the performance of commercial demulsifiers. Considering the importance of modeling the performance of demulsifiers in petroleum industry, the evaluation of the model was investigated with a new emulsion sample. The results show that support vector machine can provide an efficient model for predicting the performance of commercial demulsifiers.

کلیدواژه‌ها [English]

  • Demulsification
  • Chemical Demulsifier
  • Support Vector Machine (SVM)
  • Emulsion Stability Index
  • Artificial Intelligence

مراجع

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پژوهش نفت
دوره 34، 1403-1 - شماره پیاپی 134
فروردین و اردیبهشت 1403
صفحه 3-23

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