Model construction and application for effluent prediction in wastewater treatment plant: Data processing method optimization and process parameters integration

Mathematical statistical method (MSM) is a very important wastewater treatment plant data processing analysis tool. In this study, MSM had been used to evaluate the efficiency, stability and reliability of the low-strengthen complex wastewater treatment system. Results showed that while LSCWWTs upgrading in the future, each influent parameter could be set as the follows: COD = 738 mg/L, BOD = 300 mg/L, SS = 454 mg/L, TN = 64.3 mg/L, NH3-N = 65.2 mg/L and TP = 7.65 mg/L. For effluent of LSCWWTs, the stabilities of COD, BOD, SS and TN were all A, TP was B, and NH3-N was D. For effluent COD, TN and NH3-N reached the first class discharge standard of A, the reliability probabilities were 83.89%, 80.23% and 99.43%, respectively. And the reliability probabilities of effluent WQPs reached the first class discharge standard of B were all more than 98%.

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Improving the quality of wastewater treatment plant monitoring by adopting proper sampling strategies and data processing criteria

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.150724 ◽

2021 ◽

pp. 150724

Author(s):

Giorgio Bertanza ◽

Riccardo Boiocchi ◽

Roberta Pedrazzani

Keyword(s):

Wastewater Treatment ◽

Data Processing ◽

Wastewater Treatment Plant ◽

Treatment Plant ◽

Sampling Strategies ◽

Plant Monitoring ◽

Proper Sampling

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Infiltration Percolation in France: 10 Years Experience

Water Science & Technology ◽

10.2166/wst.1993.0208 ◽

1993 ◽

Vol 28 (10) ◽

pp. 73-81 ◽

Cited By ~ 12

Author(s):

F. Brissaud ◽

J. Lesavre

Keyword(s):

Wastewater Treatment ◽

Theoretical Approach ◽

Wastewater Treatment Plant ◽

Pilot Plant ◽

Wide Spectrum ◽

Treatment Plant ◽

Quality Standards ◽

Plant Design ◽

Plant Data ◽

Short Circuits

A survey was carried out during the late '80s over 7 infiltration percolation plants, serving populations ranging between 400 and 1700. With sand depths, hydraulic loads, influent COD and NTK concentrations respectively ranging from 0.6 to 0.2 m, 0.07 to 0.77m/day, 820 to 75 and 70 to 10 mg/l, and with different operating schedules, this set of plants displays a wide spectrum of infiltration percolation in use. When plants are suitably designed, sized and operated, primary effluents oxidation is very effective and current EEC quality standards for wastewater treatment plant effluents are matched. Disinfection is poor, below the level expected from laboratory and pilot plant data. This is due to non-uniform spreading of the influents on the infiltration areas and exceedingly short circuits and short water detention times in the sand beds. Based on a theoretical approach and on data obtained from these and many other plants, a sizing methodology is provided. Recommended improvements in the spreading technology, as well as in the plant design and management, should lead to more reliable oxidation and disinfection performance

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Forecasting Influent-Effluent Wastewater Treatment Plant Using Time Series Analysis and Artificial Neural Network Techniques

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1063 ◽

2007 ◽

Vol 2 (3) ◽

Cited By ~ 1

Author(s):

Sameer Al-Asheh ◽

Farouq Sabri Mjalli ◽

Hassan E. Alfadala

Keyword(s):

Neural Networks ◽

Time Series ◽

Wastewater Treatment ◽

Time Series Analysis ◽

Quality Indicators ◽

Wastewater Treatment Plant ◽

Treatment Plant ◽

Series Analysis ◽

Plant Data ◽

Artificial Neural

We consider the problem of predicting the future behavior of wastewater treatment plant quality indicators by creating prediction models using historical plant data. One of the main aims of this work is to be able to predict plant operational situations in advance so that corrective actions can be taken in time. Sets of historical plant data, such as BOD, COD and TSS were collected for a local wastewater treatment plant in Doha, the capital of the State of Qatar. These variables characterize the performance of any wastewater treatment plant and can be considered as quality indicators of the plant performance. Data were collected over a period of 4 years for the influent and effluent streams of the station. The plant influent and effluent predictions were performed using different techniques. These include time-series analysis, where the ARIMA (Autoregressive Integrated Moving Average) model was implemented in this case, and two Artificial Neural Networks (ANN) algorithms, namely Adaptive Linear Neuron networks (ADALINE) and Multi-layer Feedforward (ML-FF) neural networks. The predictions from the three techniques were presented and compared. The ML-FF model predictions proved to be more reliable than that of the equivalent ARIMA predictions followed by the ADALINE predictions, particularly for the finial effluent stream variables.

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Application of Baffled-Submerged Bio-Film Process in the Treatment of Sewage Wastewater in a Small Town

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.356-360.1401 ◽

2011 ◽

Vol 356-360 ◽

pp. 1401-1405

Author(s):

Ya Feng Li ◽

Xiao Ning Zhang ◽

Xin Wang

Keyword(s):

Wastewater Treatment ◽

Process Parameters ◽

Wastewater Treatment Plant ◽

Municipal Wastewater ◽

Primary Standard ◽

Treatment Plant ◽

Small Towns ◽

Municipal Wastewater Treatment ◽

Flow Process ◽

Broad Application

The process parameters and running effect of baffled-submerged bio-film process applied in the treatment of sewage wastewater were introduced, aiming to improve the broad application of baffled-submerged bio-film process in the wastewater treatment in small towns. An engineering case was taken as an example to describe the process flow, process parameters and sizes of the structures. The character of the designing was explained along with the discussion of the running effect. The running effect shows that sewage wastewater treated by baffled-submerged bio-film process can meet the requirements of Grand B of Primary standard of Discharge standard of pollutants for municipal wastewater treatment plant (GB18918).

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