Prediction of Optimal Sulfinol Concentration in Khangiran Gas Treating Unit via Adaptive Neuro-Fuzzy Inference System and Regularization Network

Document Type : Original Article

Authors

1 Chemical Engineering Department, Faculty of Engineering, Bojnord University, Bojnord, Iran

2 Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University Of Mashhad, Mashhad, Iran

20.1001.1.25885596.2016.1.1.5.7

Abstract

The concentration of H2S in the inlet acid gas is an important factor that sulfur plant designers must consider when deciding on the right technology or configuration to obtain high sulfur recovery efficiency. 
Using sterically-hindered solvents such as promoted tertiary amines and various configuration for gas treating unit are several alternatives for acid gas enrichment (AGE) to reduce the concentration of carbon dioxide and heavy aromatic hydrocarbons while enriching the H2S content of SRU feed stream. The present article uses combinations of Aspen-HYSYS software and two distinct networks (namely, Regularization network and adaptive neuro-fuzzy inference system) to compare the AGE capability of sulfinol-M (sulfolane + MDEA) solvent at optimal concentration to traditional MDEA solution when both of them are used in a conventional gas treating unit (GTU). The simulation outcomes demonstrate that the optimal concentration of Sulfinol-M aqueous solution (containing 37 wt% Sulfolane and 45 wt% MDEA) will completely eliminate toluene and ethylbenzene from the SRU feed stream while removing 80% of benzene entering the GTU process. Furthermore, mole fraction of H2S in the SRU feed stream increases the conventional 33.48 mole% to over 57mole%. Increased H2S selectivity of optimal Sulfinol-M aqueous solution will elevate the CO2 slippage through sweet gas stream at around 4.5mole% which is still below the permissible threshold. 

Keywords

Main Subjects

Article Title [فارسی]

پیش بینی غلظت بهینه سولفینول در واحد تصفیه گاز پالایشگاه خانگیران از طریق سیستم استنتاج تطبیقی عصبی- فازی و شبکه رگولاریزاسیون

Authors [فارسی]

  • علی گرمرودی اصیل 1
  • اکبر شاهسوند 2
  • مرتضی اسفندیاری 1

1 گروه مهندسی شیمی، دانشکده فنی مهندسی و علوم پایه، دانشگاه بجنورد، بجنورد، ایران

2 گروه مهندسی شیمی، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران

Abstract [فارسی]

غلظت سولفید هیدروژن در گاز اسیدی ورودی به واحد بازیافت گوگرد از جمله پارامترهای مهم و تاثیرگذار می باشد که باید طراحان آن واحدها در هنگام تصمیم گیری برای انتخاب فرآیند یا ساختار درست جهت بدست آوردن بیشترین بازده بازیافت گوگرد در نظر داشته باشند. استفاده از حلال های ممانعت فضایی شده مانند آمین های نوع سوم ارتقاء یافته و همچنین ساختارهای متفاوت برای واحد تصفیه گاز، از جمله گزینه های مختلف برای غنی سازی گاز اسیدی (AGE) که به منظور کاهش غلظت دی اکسید کربن و هیدروکربن های آروماتیکی سنگین و افزایش غلظت سولفید هیدروژن در جریان خوراک ورودی به واحد بازیافت گوگرد انجام می گیرد، خواهد بود. در مقاله حاضر با استفاده از تلفیق نرم افزار اسپن-هایسیس و دو شبکه مجزا (به نام های شبکه رگولاریزاسیون و سیستم استنباط فازی-­عصبی تطبیقی) نسبت به مقایسه توانایی غنی سازی گاز اسیدی حلال سولفینول-M (سولفولان+MDEA) در غلظت بهینه و حلال رایج MDEA، هنگامی که هر دوی آن ها به عنوان حلال واحد تصفیه گاز مورد استفاده قرار می گیرند، اقدام شده است. نتایج حاصل از شبیه سازی حاکی از آن بود که در غلظت بهینه حلال سولفینول-M (شامل 37% وزنی سولفولان و 45% وزنی MDEA) تمامی تولوئن و اتیل بنزن و همچنین 80% از بنزن ورودی به واحد تصفیه گاز، از خوراک ورودی به واحد بازیافت گوگرد حذف خواهند شد. علاوه بر این، کسر مولی سولفید هیدروژن در خوراک ورودی به واحد بازیافت گوگرد از مقدار فعلی 48/33% به بالای 57% افزایش پیدا خواهد کرد. افزایش انتخاب پذیری حلال سولفینول-M باعث افزایش کسر مولی دی اکسید کربن در جریان گاز شیرین به حدود 5/4% خواهد شد که کماکان زیر مقدار مجاز می باشد. 

Keywords [فارسی]

  • غنی سازی گاز اسیدی
  • بنزن- تولوئن-اتیل بنزن
  • شبکه رگولاریزاسیون
  • سیستم استنباط فازی‌عصبی تطبیقی
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