مدل‌سازی تبدیل گاز سنتز و بررسی درصد تبدیل هیدروژن و منوکسیدکربن توسط شبکه عصبی براساس آزمایشات تجربی در راکتور بستر ثابت

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی شهید نیکبخت، دانشگاه سیستان و بلوچستان، زاهدان، ایران

2 گروه مهندسی شیمی، دانشگاه صنعتی بیرجند، بیرجند، ایران

چکیده

در این پژوهش، براساس داده‌ها و آزمایشات تجربی به بررسی درصد تبدیل هیدروژن و منوکسیدکربن و طراحی مدل آنها براساس طراحی آزمایش و شبکه عصبی پرداخته شد. داده‌های آزمایشگاهی براساس پنج متغیر ورودیو براساس طراحی مکعب مرکزی تعیین گردید. این پنج متغیر موثر عبارتند از: دما، فشار راکتور، نسبت هیدروژن به منوکسید کربن در خوراک، فشار جزئی هیدروژن و منوکسید کربن در راکتور. شرایط عملیاتی راکتور، دما ( oC340-320)، فشار (bar gauge 8-2)، نسبت هیدروژن به منوکسیدکربن (2/2- 8/0)، فشار جزئی منوکسیدکربن (bar gauge 7/2-3/0) و فشار جزئی هیدروژن (bar gauge 5/2-3/0) می‌باشد. برای بررسی و به‌دست آوردن مدل درصد تبدیل‌ها، از دو روش پاسخ سطح و شبکه عصبی استفاده گردید. برای بررسی توانمندی هر دو روش، دو پارامتر مهم خطای آماری شامل مجذور میانگین خطا و انحراف نسبی میانگین مطلق محاسبه شد. نتایج به‌دست آمده از هر دو مدل پاسخ سطح و شبکه عصبی با نتایج تجربی مقایسه شد. مشاهده گردید که هر دو مدل تطابق خوبی با داده‌های تجربی دارند. برای محاسبه بیشینه درصد تبدیل برای هر دو مدل، شبکه عصبی با الگوریتم ژنتیک مدل شده و نقاط بیشینه هر دو مدل غیرخطی به‌دست آمد. در پایان، مدل‌های حاصله تحلیل شده و نقاط بیشینه مورد بررسی قرار گرفت. همچنین این مدل می‌تواند برای به‌دست آوردن محصولات انتخابی با ارزش افزوده بالا نیز به‌کار گرفته شود.
 

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigation of H2 and CO and Modeling of Syngas Conversion by Artificial Neural Network Based on Experimental Data in a Fixed-bed Reactor

نویسندگان [English]

  • Afshin Razmjooie 1
  • Hossein Atashi 1
  • Farhad Shahraki 1
  • Mehdi Shiva 2
1 Department of Chemical Engineering, University of Sistan and Baluchestan, Zahedan, Iran
2 Department of Chemical Engineering, Birjand Industrial University, Birjand, Iran
چکیده [English]

In this research, the application of design of experiment and artificial neural network on conversion of H2 and CO were studied based on the experimental data. The experimental data has been collected from five independent variables based on central composite design such as temperature and pressure of reactor, H2/CO feed ratio, and partial pressure of H2 and CO in reactor. The operating conditions are: T = 320-340°C, P = 2-8 barg, H2/CO = 0.8-2.2, PCO = 0.3-2.7 barg, and PH2 = 0.3-2.5 barg. To generate the conversion models, two methods consist of response surface methodology and artificial neural network were used. The capability and sensitivity of both models were evaluated by some statistical parameters including mean square error and absolute average relative deviation. The result of both models were compared with experimental data and show the best results. To evaluate the maximum conversion of (H2 and CO), a hybrid ANN/GA was performed to solve the nonlinear both models. Finally, all quadratic equations and maximum of both models were performed, and the results were concluded. Also, this method can be used to produce the valuable selective production.
 

کلیدواژه‌ها [English]

  • Fischer-tropsch Synthesis
  • Conversion of Syngas (or Synthesis Gas)
  • Response Surface Methodology
  • Artificial Neural Network
  • Genetic Algorithm

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