Artificial Neural Network Modelling of Sequencing Batch Reactor Performance

2012 ◽  
pp. 456-479 ◽  
Author(s):  
Eldon R. Rene ◽  
Sung Joo Kim ◽  
Dae Hee Lee ◽  
Woo Bong Je ◽  
Mirian Estefanía López ◽  
...  
Keyword(s):  
Neural Network ◽  
Sequencing Batch Reactor ◽  
Batch Reactor ◽  
Water Quality Management ◽  
Operating Time ◽  
Network Modelling ◽  
Cycle Times ◽  
Aeration Time ◽  
Solids Concentration ◽  
Artificial Neural

Sequencing batch reactor (SBR) is a versatile, eco-friendly, and cost-saving process for the biological treatment of nutrient-rich wastewater, at varying loading rates. The performance of a laboratory-scale SBR was monitored to ascertain the chemical oxygen demand (COD) and total nitrogen (T-N) removals under four different operating conditions, by varying the operating time for the nitrification/denitrification steps, i.e., the cycle times. A multi-layered neural network was developed using COD, T-N, carbon to nitrogen ratio (C/N), aeration time, and mixed liquor suspended solids concentration (MLSS) data. This chapter compares the neural simulation results to the experimental results and extracts information on the significant factors affecting SBR performance. The application of artificial neural networks to biological processes such as SBR is a relatively new technique in wastewater and water quality management, and the results presented herein indicate the promising start of the adoption of computational science in this domain of research.

scholarly journals Artificial Neural Network Modeling for Biological Removal of Organic Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch Reactor

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Pradyut Kundu ◽  
Anupam Debsarkar ◽  
Somnath Mukherjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sequencing Batch Reactor ◽  
Batch Reactor ◽  
Experimental Results ◽  
Neural Network Modeling ◽  
Data Sets ◽  
Slaughterhouse Wastewater ◽  
Ann Models ◽  
Artificial Neural

The present paper deals with treatment of slaughterhouse wastewater by conducting a laboratory scale sequencing batch reactor (SBR) with different input characterized samples, and the experimental results are explored for the formulation of feedforward backpropagation artificial neural network (ANN) to predict combined removal efficiency of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N). The reactor was operated under three different combinations of aerobic-anoxic sequence, namely, (4 + 4), (5 + 3), and (5 + 4) hour of total react period with influent COD and NH4+-N level of 2000 ± 100 mg/L and 120 ± 10 mg/L, respectively. ANN modeling was carried out using neural network tools, with Levenberg-Marquardt training algorithm. Various trials were examined for training of three types of ANN models (Models “A,” “B,” and “C”) using number of neurons in the hidden layer varying from 2 to 30. All together 29, data sets were used for each three types of model for which 15 data sets were used for training, 7 data sets for validation, and 7 data sets for testing. The experimental results were used for testing and validation of three types of ANN models. Three ANN models (Models “A,” “B,” and “C”) were trained and tested reasonably well to predict COD and NH4+-N removal efficiently with 3.33% experimental error.

scholarly journals Application of Artificial Neural Network to Improve Pleurotus sp. Cultivation Modelling

2019 ◽  
Vol 255 ◽  
pp. 02010
Author(s):  
Fakharudin Abdul Sahli ◽  
Zainol Norazwina ◽  
Dzulkefli Noor Athirah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mathematical Modelling ◽  
Nitrogen Concentration ◽  
Network Modelling ◽  
Connection Weight ◽  
Palm Fruit ◽  
Cultivation Process ◽  
Artificial Neural ◽  
Trained Network

Mathematical modelling for nitrogen concentration in mycelium (N) during Pleurotus sp. cultivation had successfully been produced using multiple linear regression. Two different substrates were used to cultivate the Pleurotus sp. which were empty palm fruit bunch (EFB) and sugarcane bagasse (SB). Both substrates were collected and prepared as the selected factors which were type of substrate (SB - A and EFB - B), size of substrates (0.5 cm and 2.5 cm), mass ratio of spawn to substrate (SP/SS) (1:10 and 1:14), temperature during spawn running (25°C and ambient) and pre-treatment of substrates (steam and non-steam). The response was nitrogen concentration in mycelium (N). This paper presents the application of artificial neural network to improve the modelling process. Artificial neural network is one of the machine learning method which use the cultivation process information and extract the pattern from the data. Neural network ability to learn pattern by changing the connection weight had produced a trained network which represent the Pleurotus sp. cultivation process. Next this trained network was validated using error measurement to determine the modelling accuracy. The results show that the artificial neural network modelling produced better results with higher accuracy and lower error when compared to the mathematical modelling.

Use pH and ORP as fuzzy control parameters of denitrification in SBR process

2002 ◽  
Vol 46 (4-5) ◽  
pp. 131-137 ◽  
Author(s):  
Y.Z. Peng ◽  
J.F. Gao ◽  
S.Y. Wang ◽  
M.H. Sui
Keyword(s):  
Fuzzy Control ◽  
Sequencing Batch Reactor ◽  
Fuzzy Controller ◽  
Batch Reactor ◽  
Limiting Factor ◽  
Control Parameters ◽  
Carbon Addition ◽  
Brewery Wastewater ◽  
Aeration Time ◽  
On Line

In order to achieve fuzzy control of denitrification in a Sequencing Batch Reactor (SBR) brewery wastewater was used as the substrate. The effects of brewery wastewater, sodium acetate, methanol and endogenous carbon source on the relationships between pH, ORP and denitrification were investigated. Also different quantities of brewery wastewater were examined. All the results indicated that the nitrate apex and nitrate knee occurred in the pH and ORP profiles at the end of denitrification. And when carbon was the limiting factor, through comparing the different increasing rate of pH whether the carbon was enough or not could be known, and when the carbon should be added again could be decided. On the basis of this, the fuzzy controller for denitrification in SBR was constructed, and the on-line fuzzy control experiments comparing three methods of carbon addition were carried out. The results showed that continuous carbon addition at a low rate might be the best method, it could not only give higher denitrification rate but also reduce the re-aeration time as much as possible. It appears promising to use pH and ORP as fuzzy control parameters to control the denitrification time and the addition of carbon.

scholarly journals Study of COD Removal Rate on a Sequencing Batch Reactor (SBR) Treating Tapioca Wastewater

2019 ◽  
Vol 38 (3) ◽  
pp. 243
Author(s):  
Happy Mulyani ◽  
Gregorius Prima Indra Budianto ◽  
Margono Margono ◽  
Mujtahid Kaavessina
Keyword(s):  
Wastewater Treatment ◽  
Sequencing Batch Reactor ◽  
Removal Rate ◽  
Batch Reactor ◽  
Cod Removal ◽  
Operation Condition ◽  
Effluent Quality ◽  
Aeration Time ◽  
Standard Quality ◽  
Cod Removal Rate

Industrial wastewater treatment using Sequencing Batch Reactor (SBR) can improve effluent quality at lower cost than that obtained by other biological treatment methods. Further optimization is still required to enhance effluent quality until it meets standard quality and to reduce the operating cost of treatment of high strength organic wastewater. The purpose of this research was to determine the effect of pretreatment (pH adjustment and prechlorination) and aeration time on effluent quality and COD removal rate in tapioca wastewater treatment using SBR. Pretreatment was done by (1) adjustment of tapioca wastewater pH to control (4.92), 7, and 8, and (2) tapioca wastewater prechlorination at pH 8 during hour using calcium hypochlorite in variation dosages 0, 2, 4, 6 mg/L Cl2, SBR operation was conducted according to following steps: (1) Filling of pre-treated wastewater into a bioreactor during 1 hour, and (2) aeration of the mixture of tapioca wastewater and activated sludge during 8 hours. Effluent sample was collected at every 2-hours aeration for COD analysis. COD removal rate mathematical formula was got by first deriving the best fit function between aeration time and COD. Optimum aeration time resulting in no COD removal rate. The value of COD effluent and its removal rate in optimum aeration time was used to determine the recommended of operation condition of pretreatment. Research result shows that chosen pH operation condition is pH 8. Prechorination can make effluent quality which meets standard quality and highest COD removal rate. The chosen Cl2 dosage is 6 mg/L.

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