پیش‌بینی غلظت آمونیوم و مواد آلی فاضلاب دفنگاه زباله با استفاده از شبکه عصبی مصنوعی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناس ارشد محیط زیست، عضو هیئت علمی پژوهشکده محیط زیست جهاد دانشگاهی، رشت

2 دانش‌آموخته کارشناسی ارشد هوش مصنوعی، دانشکده مهندسی کامپیوتر، دانشگاه شیراز

3 دانشیار گروه آب و محیط زیست، دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران

چکیده

در این تحقیق به‌منظور پیش‌بینی میزان غلظت مواد آلی و آمونیوم موجود در فاضلاب دفنگاه زباله با استفاده از شبکه عصبی مصنوعی، دو سیستم در آزمایشگاه مدل شد. برای آموزش و تست مدل شبکه عصبی، از نتایج آزمایشگاهی به‌دست آمده استفاده شد. سیستم 1، فقط شامل راکتور حاوی زباله تازه بود. در این سیستم فاضلاب پس از تولید، بر روی زباله تازه بازگردانده می‌‌شد. سیستم 2، شامل راکتور حاوی زباله تازه و راکتوری حاوی زباله خوب تجزیه شده بود. در این سیستم، فاضلاب پس از خروج از زباله تازه بر روی راکتور حاوی زباله خوب تجزیه شده، تخلیه و سپس بر روی زباله تازه بازگردانده می‌شد. نتایج آزمایشگاهی نشان داد که در سیستم 1، انباشتگی مواد آلی و NH4+-N رخ داد، اما حذف مواد آلی و NH4+-N در سیستم 2 به‌خوبی صورت گرفت به‌طوری که در طول مدت آزمایش، میانگین راندمان حذف مواد آلی در سیستم 2، معادل 85 درصد و میانگین راندمان حذف NH4+-N معادل 34 درصد بود. همچنین پیش‌بینی میزان غلظت مواد آلی و NH4+-N به‌وسیله شبکه عصبی مصنوعی، با توجه به شاخصهای آماری با کارایی بالایی صورت گرفت.
 

کلیدواژه‌ها


عنوان مقاله [English]

Prediction of COD and NH4+-N Concentrations in Leachate from Lab-scale Landfill Bioreactors Using Artificial Neural Networks

نویسندگان [English]

  • Mohamad Javad Zoqi 1
  • Taktom Zoqi 2
  • Mohsen Saeedi 3
1 M.Sc. of Environmental Eng., Faculty Member of Environmental Research Institute of Jahad Daneshgahi, Rasht
2 M.Sc. of Artificial Intelligence, Dept. of Computer Engineering, Shiraz University
3 Assoc. Prof., Dept. of Hydraulic and Environmental Eng., School of Civil Eng., Iran Uni. of Science and Tech., Tehran
چکیده [English]

In this study, we present an Artificial Neural Network (ANN) model for predicting COD and NH4+-N concentrations in landfill leachate from lab-scale landfill bioreactors. For this purpose, two different lab-scale systems were modeled. for neural network’s data obtained. In the first system, the leachate from a fresh-waste reactor was drained to a recirculation tank and recycled every two days. In the second, the leachate from a fresh waste landfill reactor was fed through a well-decomposed refuse landfill reactor, while the leachate from a well-decomposed refuse landfill reactor was simultaneously recycled to a fresh waste landfill reactor. The results indicate that leachate NH4+-N and COD concentrations accumulated to a high level in the first system, while. NH4+-N and COD removals were successfully carried out in the second. Also, average removal efficiencies in the second system reached 85% and 34% for COD and NH4+-N, respectively. Finally, the ANN’s results exhibited the success of the model as witnessed by the excellent agreement obtained between measured and predicted values.

کلیدواژه‌ها [English]

  • Anaerobic Process
  • Artificial Neural Network
  • Organic Content
  • NH4+-N
  • leachate
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