Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling

2021 ◽  
Author(s):  
Yongkui Yang ◽  
Kyong-Ryong Kim ◽  
Rongrong Kou ◽  
Yipei Li ◽  
Jun Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Network Modeling ◽  
Treatment Plant ◽  
Neural Network Modeling ◽  
Dynamic Neural Network ◽  
Effluent Quality

scholarly journals Persulfate mediated solar photo-Fenton aiming at wastewater treatment plant effluent improvement at neutral PH: emerging contaminant removal, disinfection, and elimination of antibiotic-resistant bacteria

2021 ◽  
Author(s):  
Maria Clara V. M. Starling ◽  
Elizângela P. Costa ◽  
Felipe A. Souza ◽  
Elayne C. Machado ◽  
Juliana Calábria de Araujo ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Resistant Bacteria ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
E Coli ◽  
Effluent Quality ◽  
Neutral Ph ◽  
Wastewater Treatment Plant Effluent

AbstractThis work investigated an innovative alternative to improve municipal wastewater treatment plant effluent (MWWTP effluent) quality aiming at the removal of contaminants of emerging concern (caffeine, carbendazim, and losartan potassium), and antibiotic-resistant bacteria (ARB), as well as disinfection (E. coli). Persulfate was used as an alternative oxidant in the solar photo-Fenton process (solar/Fe/S2O82−) due to its greater stability in the presence of matrix components. The efficiency of solar/Fe/S2O82− at neutral pH using intermittent iron additions is unprecedented in the literature. At first, solar/Fe/S2O82− was performed in a solar simulator (30 W m−2) leading to more than 60% removal of CECs, and the intermittent iron addition strategy was proved effective. Then, solar/Fe/S2O82− and solar/Fe/H2O2 were compared in semi-pilot scale in a raceway pond reactor (RPR) and a cost analysis was performed. Solar/Fe/S2O82− showed higher efficiencies of removal of target CECs (55%), E. coli (3 log units), and ARB (3 to 4 log units) within 1.9 kJ L−1 of accumulated irradiation compared to solar/Fe/H2O2 (CECs, 49%; E. coli, 2 log units; ARB, 1 to 3 log units in 2.5 kJ L−1). None of the treatments generated acute toxicity upon Allivibrio fischeri. Lower total cost was obtained using S2O82− (0.6 € m−3) compared to H2O2 (1.2 € m−3). Therefore, the iron intermittent addition aligned to the use of persulfate is suitable for MWWTP effluent quality improvement at neutral pH.

Research on Variable Metric Chaos Optimization Modeling for the Prediction of Wastewater Treatment Plants Performance

2012 ◽  
Vol 428 ◽  
pp. 169-175
Author(s):  
Guo Kai Fu ◽  
Yi Yue Hu ◽  
Zhi Zhang
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Treatment System ◽  
Variable Metric ◽  
Chaos Optimization ◽  
Actual Operation ◽  
The Stability

A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. For the multi-variable, uncertainty, non-linear characteristics of the wastewater treatment system, a variable metric chaos optimization neural network (VMCNW) prediction model is established standing on the actual operation data in the wasterwater treatment system. The model overcomes several disadvantages of the conventional BP neural network. Namely:slow convergence, low accuracy and difficulty in finding the global optimum.The results of model calculation show that the predicted value can better match measured value,played a effect of simulating and predicting and be able to optimize the operation status. The establishment of the predicting model provide a simple and practical way for the operation and management in wastewater treatment plant,and have good research and engineering practical value.

Decentralized Adaptive PI with Adaptive Interaction Algorithm of Wastewater Treatment Plant

2014 ◽  
Vol 67 (5) ◽  
Author(s):  
M. F. Rahmat ◽  
S. I. Samsudin ◽  
N. A. Wahab ◽  
Mashitah Che Razali ◽  
Muhammad Sani Gaya
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Wastewater Treatment Plant ◽  
Error Function ◽  
Treatment Plant ◽  
Weather Conditions ◽  
Pi Controller ◽  
Effluent Quality ◽  
Dynamic Performances ◽  
Adaptive Interaction

Wastewater treatment plant (WWTP) is highly known with the variation and uncertainty of the parameters, making them a challenge to be tuned and controlled. In this paper, an adaptive decentralized PI controller is developed for nonlinear activated sludge WWTP. The work is highlighted in auto-tuning the PI control parameters in satisfying straighten effluent quality and hence optimizing the nitrogen removal. The PI controller parameters are obtained by using simple updating algorithm developed based on adaptive interaction theory. The error function is minimized directly by approximate Frechet tuning algorithm without explicit estimation of the model. The effectiveness of the proposed controller is then validated by comparing the performance of activated sludge process to the benchmark PI under three different weather conditions with realistic variations in influent flow rate and composition. The algorithm is effectively applied in activated sludge system with improved dynamic performances in effluent quality index and energy consumed of Benchmark Simulation Model No.1.

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