آنالیز داده های تصفیه خانه فاضلاب جهت بررسی کیفیت پساب خروجی با استفاده از نتایج آزمایشگاهی و پیش بینی براساس مدل های هوش مصنوعی (مطالعه موردی: تصفیه خانه فاضلاب تهران)

نوع مقاله : علمی - پژوهشی

نویسندگان

1 کارشناسی ارشد محیط ‌زیست، دانشکده مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران

2 دانشیار گروه محیط زیست، دانشکده مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران.

چکیده

استفاده از پساب تصفیه‌خانه‌های فاضلاب شهری جهت آبیاری اراضی کشاورزی از جمله مسائل مهم و اساسی در زمینه استفاده مجدد از پساب تصفیه‌خانه‌ها به‌ شمار می‌رود. در سال‌های اخیر استفاده از شبکه هوش مصنوعی جهت مدل‌سازی فرایند تصفیه فاضلاب مورد توجه پژوهشگران قرار گرفته ‌است. از این رو، در این پژوهش از مدل‌های‌ شبکه عصبی مصنوعی(ANN)، منطق فازی(FL) و سیستم استنتاج عصبی فازی تطبیقی(ANFIS) برای پیش‌بینی کیفیت پساب خروجی تصفیه‌خانه فاضلاب، استفاده شده ‌است. در ابتدا سه سناریو انتخاب گردید و ورودی آن‌ها، با روش تحلیل مؤلفه اصلی(PCA) کاهش یافت و در نهایت مدل‌سازی یک‌بار با روش PCA و بار دیگر بدون استفاده از این روش انجام شد و نتایج مدل‌ها با هم مقایسه گردید. ارزیابی نتایج پیش‌بینی‌ها با استفاده از شاخص‌های آماری نشان داد که مدل ANFIS با میانگین کاهش13.92 درصدی خطا نسبت به مدل FL و کاهش 8.22 درصدی نسبت به مدل ANN از دقت بالاتری برخوردار بوده و دقیق‌تر عمل کرده است که این روند با و بدون PCA معتبر بوده است. همچنین، با محاسبه درصد بازده حذف آلاینده‌ها در خروجی تصفیه‌خانه مشخص شد حداکثر بهره‌وری حذف در تصفیه‌خانه مربوط به آلاینده TSS بوده و معادل 96.68 درصد است. سایر آلاینده‌ها نیز مقادیری نزدیک به TSS داشتند. نتایج بدست ‌آمده در این پژوهش نشان می‌دهد که استفاده از مدل‌های شبکه هوش مصنوعی، برای پیش‌بینی کیفیت پساب خروجی تصفیه‌خانه‌های فاضلاب شهری امکان‌پذیر بوده و روشی ساده، دقیق، کارآمد و قابل اطمینان به حساب می‌آیند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Analysis of Sewage Treatment Plant’s Data to evaluate Quality of Effluent using Experimental Results and Prediction based on Artificial Intelligence Models (Case Study: Tehran Wastewater Treatment Plant)

نویسندگان [English]

  • Haniyeh Malek 1
  • Majid Ehteshami 2
1 Master of Environment Engineering, Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran.
2 Associate Professor, Department of Environment, Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran.
چکیده [English]

The use of wastewater outflowing from the municipal wastewater treatment plants for irrigation of agricultural lands is one of the important and fundamental issues for reuse of the plants’ effluent. In recent years, the artificial neural networks (ANN) have received considerable attention for modeling the sewage treatment process. Accordingly, the ANN models, fuzzy logic (FL) and adaptive neuro fuzzy inference system (ANFIS) have been utilized in this paper to predict quality of the effluent running out of the treatment plant. For this purpose, first, three scenarios were chosen and their inputs were reduced using the principal component analysis (PCA) method. Finally, the process of modeling was conducted with and without this method and then, the results were compared. Evaluating the results obtained from the predictions using the statistical indicators revealed that the ANFIS model with mean error reduction value of 13.92% compared to that of the FL model and a reduction value of 8.22% in contrast to the ANN model, benefits from a higher accuracy and this trend has been held true with and without PCA. Moreover, calculating the percentage of pollutant removal efficiency indicated that the maximum removal efficiency is obtained when total suspended solids (TSS) are removed which is equal to 96.68%. It bears to mention that the rest of the pollutants had values approximately equal to that of TSS. Based on the results, as a simple, accurate, efficient and reliable approach, the ANN models can be applied to predict quality of the effluents.

کلیدواژه‌ها [English]

  • Sewage treatment Plant
  • Effluent
  • Artificial Neural Network (ANN)
  • Multivariate Statistical Analysis Method
  • Statistical Indicators
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