نوع مقاله : مقاله پژوهشی
1 دانشجوی دکترای آب و سازههای هیدرولیکی، گروه عمران آب و محیطزیست، دانشکده مهندسی عمران، دانشگاه تبریز، تبریز، ایران
2 استاد، گروه عمران آب و محیطزیست، دانشکده مهندسی عمران، دانشگاه تبریز، تبریز، ایران
3 دانشیار، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه شهید مدنی آذربایجان، تبریز، ایران
عنوان مقاله [English]
In this paper, the uncertainty of artificial intelligence models for evaluting performance of the activated sludge unit of the Tabriz treatment plant is assessed. In this regard, daily data of pollution parameters, particularly Biochemical Oxygen Demand and Chemical Oxygen Demand, are utilized. All data were collected daily during the years (2015-2020) and the best parameters were selected using the correlation coefficient criterion. The TSSi, TDSi, VSSi, pHi parameters and also, BODe and CODe with a one-day delay were selected as model input and BODe and CODe were selected as model output. The calculations of uncertainties were performed in two models of Feed Forward Neural Network as point prediction and lower upper bound estimation method to provide the Prediction Interval. The LUBE method, unlike the classical methods of calculating PI, estimates PI without the need for data distribution information. In this method, the FFNN was trained with two outputs indicating the upper and lower limits of the prediction. PICP assessment and comparing it with μ values, caused γ values to equal zero that, in the continuation of the calculation process caused CWC extraction with the minimum possible amount and production of PI for computational data and observations with the possibility of controlling random changes in the activated sludge section. So, the convergence of the LUBE method has the ability to effectively control the uncertainty between the parameters of the biological section of activated sludge using PI. The time required to build PI is considerably short. Numerical results show approximately 99% success in calculations and coverage of modeling uncertainties. Providing an oscillating range of uncertainties can be a valuable aid in improving economic conditions as well as reducing activated sludge control time and better treatment plant monitoring. Despite the design criteria for BODe of 20 mg per liter, PI results show a supply of 12% of the design index. However, considering the supply of the remaining 88% in terms of quality standard for the use of effluents and returned water, according to the Deputy of Strategic Supervision, publication 535, at the rate of 31 mg per liter in the activated sludge sector, the proper performance of the treatment plant is demonstrated. The LUBE method is an efficient method, so by providing an optimized range of fluctuations for computational data, the smallest abnormal changes in the activated sludge section due to controlling the amount of food for the micro-organisms present in this section; also, the pollution indicators with the least computing time are also reported. In addition, due to the high cost of activated sludge in the wastewater treatment sector, from an economic point of view, it also helps reduce costs. According to the non-linear behavior of bacteria during the reduction of food, as well as the control of mortality caused by the reduction of food, it can be considered a very effective tool.