scholarly journals Reduction of organic and biological pollutants from affluents of the Ancón wastewater treatment plant using microanobubbles of air and graphene [Reducción de contaminantes orgánicos y biológicos de afluentes de la planta de tratamiento de aguas residuales de Ancón utilizando micronanoburbujas de aire y grafeno]

2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Rudy Roxana Ayala Daza ◽  
Palmir Ponte Viera ◽  
Jhonny Valverde Flores
Keyword(s):  
Wastewater Treatment ◽  
Chemical Oxygen Demand ◽  
Biochemical Oxygen Demand ◽  
Wastewater Treatment Plant ◽  
Treatment Time ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Initial Concentration ◽  
Experimental Part ◽  
Thermotolerant Coliforms

The objective of this research was to reduce the organic and biological load of tributaries of the Ancón Wastewater Treatment Plant using microanobubbles of air and graphene. A preliminary sample of the affluent (3L) was taken, which had an initial concentration of Biochemical Oxygen Demand (BOD5) of 410 mg/L, Chemical Oxygen Demand (COD) of 483 mg/L, Thermotolerant Coliforms of 44,000 NMP/100mL and turbidity of 63.33 NTU. The experimental part was carried out with 03 samples of 20 liters with 03 repetitions with a treatment time of 20, 40 and 60 minutes applying air nanobubbles and 6, 12 and 18 grams of graphene respectively. The results of the treated samples were: 87 mg/L representing 78.8% reduction in Biochemical Oxygen Demand (BOD5), 114 mg/L representing 76.4% reduction in Chemical Oxygen Demand (COD), 2,900 NMP/100mL that represents 93.41% reduction of Thermotolerant Coliforms and 12.4 NTU that represents 80.11% reduction of turbidity.

Metro-environmental data approach for the prediction of chemical oxygen demand in new Nicosia wastewater treatment plant

2021 ◽  
Vol 221 ◽  
pp. 31-40
Author(s):  
A.S. Mubarak ◽  
Parvaneh Esmaili ◽  
Z.S. Ameen ◽  
R.A. Abdulkadir ◽  
M.S. Gaya ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Chemical Oxygen Demand ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Environmental Data

scholarly journals Physico-chemical investigation of wastewater from the Sebdou-Tlemcen textile complex North-West Algeria

2021 ◽  
Vol 6 (2) ◽  
pp. 361-370
Author(s):  
Asma Khelassi- Sefaoui ◽  
Abderrahmane Khechekhouche ◽  
Manel Zaoui-Djelloul Daouadji ◽  
Hamza Idrici
Keyword(s):  
Wastewater Treatment ◽  
Chemical Oxygen Demand ◽  
Water Temperature ◽  
Biochemical Oxygen Demand ◽  
Suspended Solids ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
North West ◽  
Physico Chemical ◽  
West Algeria

Wastewater treatment is a process used in several countries, particularly in Algeria. A study on Earth for one month was carried out at the sewage plant of the Sebdou textile complex, Tlemcen, north-west of Algeria. Regular samples gave average values at the outlet such that the water temperature is 22 ° C, the ph 7.43, the biochemical oxygen demand BOD5 is 36.5 mg / l, the chemical oxygen demand COD vary between 100 and 200 mg / l at the exit of the WWTP mg / l and finally suspended solids SS is of the order of 36.2 mg / l. All these values conform with the standards and therefore the treatment plant operates within Algerian standards.

Factors Affecting Biological Nitrogen Removal Efficiency in a Large Wastewater Treatment Plant

1991 ◽  
Vol 24 (7) ◽  
pp. 121-131 ◽  
Author(s):  
Elzbieta Plaza ◽  
Jan Bosander ◽  
Jozef Trela
Keyword(s):  
Wastewater Treatment ◽  
Chemical Oxygen Demand ◽  
Nitrogen Removal ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Biological Nitrogen Removal ◽  
Careful Study ◽  
Anoxic Zone ◽  
Biological Nitrogen

The pre-denitrification method, with internal carbon source for biological nitrogen removal, has been studied in full-scale experiments at a large wastewater treatment plant (flow 130,000 m3/d). Factors controlling nitrogen removal, such as fraction of anoxic zone and organic material content in wastewater are discussed. A flexible system with fine bubble membrane disc diffusers made it possible to change the ratio between the volumes for nitrification and denitrification. The denitrification process was limited by lack of organic carbon in the wastewater and increasing the fraction of anoxic zone did not improve the efficiency of the system. With the help of on-line analysers for total nitrogen and chemical oxygen demand, the relationship between the denitrification efficiency and the carbon/nitrogen ratio has been given careful study. The average value for chemical oxygen demand after primary sedimentation was only 130 mg/l and the value for the COD/N ratio was found to be 6.3. The denitrification rate was usually in the range of 1.0 and 2.0 mg NO3-N/g MLVSS h.

Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant

2019 ◽  
Vol 30 (3) ◽  
pp. 593-608 ◽  
Author(s):  
Naceureddine Bekkari ◽  
Aziez Zeddouri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Chemical Oxygen Demand ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Activation Functions ◽  
Content Type ◽  
Hidden Layer

Purpose Modeling Wastewater Treatment Plant (WWTP) constitutes an important tool for controlling the operation of the process and for predicting its performance with substantial influent fluctuations. The purpose of this paper is to apply an artificial neural network (ANN) approach with a feed-forward back-propagation in order to predict the ten-month performance of Touggourt WWTP in terms of effluent Chemical Oxygen Demand (CODeff). Design/methodology/approach The influent variables such as (pHinf), temperature (TEinf), suspended solid (SSinf), Kjeldahl Nitrogen (KNinf), biochemical oxygen demand (BODinf) and chemical oxygen demand (CODinf) were used as input variables of neural networks. To determine the appropriate architecture of the neural network models, several steps of training were conducted, namely the validation and testing of the models by varying the number of neurons and activation functions in the hidden layer, the activation function in output layer as well as the learning algorithms. Findings The better results were achieved with an architecture network [6-50-1], hyperbolic tangent sigmoid activation functions at hidden layer, linear activation functions at output layer and a Levenberg – Marquardt method as learning algorithm. The results showed that the ANN model could predict the experimental results with high correlation coefficient 0.89, 0.96 and 0.87 during learning, validation and testing phases, respectively. The overall results indicated that the ANN modeling approach can provide an effective tool for simulating, controlling and predicting the performance of WWTP. Originality/value This work is the first of its kind in this region due to the remarkable development in terms of population and agricultural activity in the region, which drove to the increase of water pollutants, so it is necessary to use the modern technologies to modeling and controlling of WWTP.

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