مدلسازی نفوذپذیری سیستم بیوراکتورغشایی با استفاده از شبکه عصبی مصنوعی

نوع مقاله : ترویجی

نویسندگان

1 دانشجوی کارشناسی ارشد گروه مهندسی شیمی، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران.

2 پژوهشگاه صنعت نفت_ عضو هیات علمی

چکیده

مدلسازی برای سیستم های پیچیده ای همچون بیوراکتور غشایی به دلیل امکان اجرای آزمایشهای مجازی زیاد در زمان کوتاه ابزاری قدرتمند است، اگرچه نیازمند اعتبار تجربی و تبدیل فرایند به مدل ریاضی می باشد. در این پژوهش به مدلسازی فرایند فیلتراسیون توسط شبکه های عصبی با استفاده از نرم افزار MATLAB 8.1 (2013) پرداخته شده و از داده های تجربی یک سیستم بیوراکتور غشایی غوطه ور مجهز به غشاء کوبوتا جهت تصفیه فاضلاب شهری با غلظت مواد جامد محلول (MLSS) بالا استفاده شده است. 2/3 از داده های تجربی جهت ساخت شبکه، آموزش و ارزیابی شبکه استفاده گردید، سپس شبکه طراحی شده جهت تخمین نفوذپذیری 1/3 از داده ها و همچنین سیستم بیوراکتور غشایی مشابه دیگر مورد استفاده قرار گرفت.جهت آموزش شبکه الگوریتم trainlm اعمال شده است. مقدار ضریب تعیین (R^2) جهت پیش بینی نفوذپذیری برای 1/3از داده های سیستم اول 0/93 و در مورد سیستم مشابه 0/92 می باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Modeling of permeability of membrane bioreactor system using artificial neural network

نویسندگان [English]

  • Deniz Mohseni 1
  • Mahmood Hemmati 2
1 M.S Student of Department of Chemical Engineering, Islamic Azad University, Tehran, Iran.
2 RIPI-Academic Staff
چکیده [English]

Modeling for complex systems such as membrane bioreactor due to run virtual tests a lot in a short time is a powerful tool, however, requires experimental validation and conversion the process to mathematical model. In this study, the modeling of filtering process by using neural network software MATLAB 8.1 (2013) with experimental data from a submerged MBR system that is equipped with Kubota membranes for municipal wastewater treatment with high Mixed Liquor Suspended Solids (MLSS) concentration was investigated.
2/3 of empirical data were used for build, training and assessment the network, then the designed network was used to estimate the permeability of 1/3 of the data as well as other similar membrane bioreactor system. trainlm algorithm is applied for training. The value of Coefficient of determination (R^2) for predicting the permeability of 1/3 of datas of the first system is 0/93 and 0/92 for the same system.

کلیدواژه‌ها [English]

  • membrane bioreactor
  • filtration process
  • resistances in series
  • Neural network
  • Permeability
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