به کارگیری روش های آماری برای افزایش دقت مدلسازی تصفیه خانه های فاضلاب صنعتی با استفاده از شبکه های عصبی مصنوعی

نوع مقاله: مقاله علمی

نویسندگان

1 تهران، دانشگاه شهید بهشتی، دانشکدة آب و محیط زیست، گروه مهندسی منابع آب

2 تهران، دانشگاه تهران، دانشکدة محیط زیست، گروه مهندسی محیط زیست

3 تهران، دانشگاه شهید بهشتی، دانشکدة آب و محیط زیست، گروه مهندسی محیط زیست

چکیده

به منظور کنترل بهتر و کارآمد عملکرد تصفیه خانه های فاضلاب صنعتی، می توان از ابزاری ریاضی برمبنای اطلاعات ثبت شدة برخی از پارامترهای اساسی پساب، طی دوره ای از بهره برداری تصفیه خانه استفاده کرد. در این پژوهش، برای اولین بار در کشور از شبکة عصبی چندلایة پیش خور با یک لایة پنهان و روش توقف آموزش، به منظور بررسی مشخصات پساب خروجی واحدهای تصفیه خانه استفاده شده است. همچنین، از روش تحلیل عاملی برای اصلاح و ارتقای عملکرد مدل های ترکیبی ایجادشده از طریق شبکة عصبی و تکنیک تحلیل مؤلفه های اصلی استفاده شده است. این تحلیل یکی از روش های آماری است که برای تجزیة اطلاعات موجود در مجموعة داده ها و تعیین تأثیرگذارترین متغیرها در هنگام زیادبودن تعداد متغیرهای مورد بررسی و ناشناخته بودن روابط بین آن ها استفاده می شود. به منظور ارزیابی عملکرد مدل ها از شاخص های مجذور میانگین مربع خطاها (RMSE)، میانگین مطلق خطا (MAE) و ضریب همبستگی پیرسون (R) استفاده شده است. مقادیر R به دست آمده از مدل ها که در بازة 0/8 تا 0/94 قرار دارند، نشان دهندة دقت مناسب آن در برآورد مشخصات کیفی فاضلاب است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Application of Statistical Techniques In Order To Improve Neural Modeling of Industrial Waste Water Treatment Plants

نویسندگان [English]

  • Mir Hasan Seyedseraji 1
  • Hamed Hasanlou 2
  • Maryam Pazoki 2
  • Hossein Nayeb 3
1 Department of Environmental Engineering, Faculty of Water and Environmental Engineering, University of Shahid Beheshti, P.O. Box 1719- 17765 Tehran, Iran
2 Department of Environmental Engineering, Faculty of Environment, University of Tehran, P.O. Box 14155-6135 Tehran, Iran
3 Department of Environmental Engineering, Faculty of Water and Environmental Engineering, University of Shahid Beheshti, P.O. Box 1719- 17765 Tehran, Iran
چکیده [English]

With regard to environmental issues, proper operation of wastewater treatment plants is of particular importance that in the case of inappropriate utilization, they will cause serious problems. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant (WWTP), powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. For the first time in Iran, the multilayer perceptron feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Principal Component Analysis (PCA) technique was applied to improve performance of generated models of neural networks. Also, factor analysis method was used to determine the effective parameters that improve the models accuracy and efficiency. Mean Square Error (MSE), Rout Mean Square Error (RMSE) and correlation coefficient (R) were used for performance evaluation of the models. Correlation coefficients (R) was between 0.8 to 0.94 that showed good accuracy of the models in estimating qualitative profile of wastewater.

کلیدواژه‌ها [English]

  • Industrial Wastewater Treatment Plant
  • Artificial Neural Network
  • PCA
  • factor analysis
  • Fajr petrochemical
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