Odour emission estimates and control strategies using models and sampling for East Bay Municipal Utility District's collection sewage system and wastewater treatment plant

2000 ◽  
Vol 41 (6) ◽  
pp. 65-71
Author(s):  
J. R. Witherspoon ◽  
A. Sidhu ◽  
J. Castleberry ◽  
L. Coleman ◽  
K. Reynolds ◽  
...  

For several years, public complaints regarding odours generated by East Bay Municipal Utility District's (EBMUD's) wastewater treatment plant and sewage collection system (SCS) have been increasing. In response, an Odor Control Master Plan was completed to develop near- and long-term odour abatement strategies for their wastewater system. The plan's strategies include using an advisory committee to assist in setting odour threshold levels, prioritizingodour sources, issuing an odour-status newsletter, and reviewing odour control options. The objective is to provide an odour-free community environment at least 99 percent of the year. This paper provides innovative approaches to estimate odour emissions and identify viable odour control options for SCSs through complete wastewater treatment. This paper also presents a CH2M HILL SCS odour model comparison to a comprehensive EBMUD sewage system corrosion study, illustrating that areas having high predicted odours also have high corrosion rates.

2001 ◽  
Vol 43 (11) ◽  
pp. 189-196 ◽  
Author(s):  
M. Bongards

One of the main problems in operating a wastewater treatment plant is the purification of the excess water from dewatering and pressing of sludge. Because of a high load of organic material and of nitrogen it has to be buffered and treated together with the inflowing wastewater. Different control strategies are discussed. A combination of neural network for predicting outflow values one hour in advance and fuzzy controller for dosing the sludge water are presented. This design allows the construction of a highly non-linear predictive controller adapted to the behaviour of the controlled system with a relatively simple and easy to optimise fuzzy controller. Measurement results of its operation on a municipal wastewater treatment plant of 60,000 inhabitant equivalents are presented and discussed. In several months of operation the system has proved very reliable and robust tool for improving the system's efficiency.


2017 ◽  
Vol 33 (1) ◽  
pp. 57-63 ◽  
Author(s):  
Piotr M. Bugajski ◽  
Grzegorz Kaczor ◽  
Krzysztof Chmielowski

AbstractThe paper analyzes the effect of precipitation water that inflowing to sanitary sewage system as accidental water on the changes in the total amount of treated sewage. The effects of accidental water supply on the total amount of sewage inflowing to treatment plant were analyzed based on mean daily amounts from the investigated periods and mean daily amounts from incidental supplies. The study was conducted in the years 2010–2015. Six characteristic research periods were identified (one per each calendar year), when the amount of sewage in the sanitary sewage system was greater than during dry weather. The analysis of changes in the amount of sewage supplied to the sewerage system in the six investigated periods revealed that the accidental water constituted from 26.8% to 48.4% of total sewage inflowing to the wastewater treatment plant (WWTP). In exceptional situations, during intense rains, the share of precipitation water in the sewerage system would increase up to 75%. Then, the rainwater inflowing the sewerage system caused hydraulic overloading of the WWTP by exceeding its maximum design supply.


2008 ◽  
Vol 42 (17) ◽  
pp. 4485-4497 ◽  
Author(s):  
Xavier Flores-Alsina ◽  
Ignasi Rodríguez-Roda ◽  
Gürkan Sin ◽  
Krist V. Gernaey

1996 ◽  
Vol 33 (2) ◽  
pp. 199-208 ◽  
Author(s):  
W. Bauwens ◽  
P. Vanrolleghem ◽  
M. Smeets

The paper considers the efficiency of alternative sewer and wastewater treatment plant management schemes with respect to the effluents to the receiving waters. The input time series for the flows and concentrations at the CSO structures and at the treatment plant intake are obtained through a continuous sewer simulation model. The wastewater treatment plant model is based on a structured dynamic model describing COD removal and final settling. Special emphasis is put on the sludge inventory of the plant since this is considered to be the main problem area under storm conditions. The methodology is illustrated on the combined sewer network of Brussels. Scenarios without and with CSO control measures in the sewer are considered. At the treatment plant, the simulation study evaluates the effect of potential control strategies such as ratio control of the RAS, step feed and retention of first flush in a storm tank.


2001 ◽  
Vol 44 (2-3) ◽  
pp. 145-154 ◽  
Author(s):  
D. Demey ◽  
B. Vanderhaegen ◽  
H. Vanhooren ◽  
J. Liessens ◽  
L. Van Eyck ◽  
...  

In this paper, the practical implementation and validation of advanced control strategies, designed using model based techniques, at an industrial wastewater treatment plant is demonstrated. The plant under study is treating the wastewater of a large pharmaceutical production facility. The process characteristics of the wastewater treatment were quantified by means of tracer tests, intensive measurement campaigns and the use of on-line sensors. In parallel, a dynamical model of the complete wastewater plant was developed according to the specific kinetic characteristics of the sludge and the highly varying composition of the industrial wastewater. Based on real-time data and dynamic models, control strategies for the equalisation system, the polymer dosing and phosphorus addition were established. The control strategies are being integrated in the existing SCADA system combining traditional PLC technology with robust PC based control calculations. The use of intelligent control in wastewater treatment offers a wide spectrum of possibilities to upgrade existing plants, to increase the capacity of the plant and to eliminate peaks. This can result in a more stable and secure overall performance and, finally, in cost savings. The use of on-line sensors has a potential not only for monitoring concentrations, but also for manipulating flows and concentrations. This way the performance of the plant can be secured.


2019 ◽  
Vol 52 (1) ◽  
pp. 257-262
Author(s):  
Laurentiu Luca ◽  
Ramon Vilanova ◽  
George Adrian Ifrim ◽  
Emil Ceanga ◽  
Sergiu Caraman ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6315
Author(s):  
Ivan Pisa ◽  
Antoni Morell ◽  
Ramón Vilanova ◽  
Jose Lopez Vicario

In the last decade, industrial environments have been experiencing a change in their control processes. It is more frequent that control strategies adopt Artificial Neural Networks (ANNs) to support control operations, or even as the main control structure. Thus, control structures can be directly obtained from input and output measurements without requiring a huge knowledge of the processes under control. However, ANNs have to be designed, implemented, and trained, which can become complex and time-demanding processes. This can be alleviated by means of Transfer Learning (TL) methodologies, where the knowledge obtained from a unique ANN is transferred to the remaining nets reducing the ANN design time. From the control viewpoint, the first ANN can be easily obtained and then transferred to the remaining control loops. In this manuscript, the application of TL methodologies to design and implement the control loops of a Wastewater Treatment Plant (WWTP) is analysed. Results show that the adoption of this TL-based methodology allows the development of new control loops without requiring a huge knowledge of the processes under control. Besides, a wide improvement in terms of the control performance with respect to conventional control structures is also obtained. For instance, results have shown that less oscillations in the tracking of desired set-points are produced by achieving improvements in the Integrated Absolute Error and Integrated Square Error which go from 40.17% to 94.29% and from 34.27% to 99.71%, respectively.


Sign in / Sign up

Export Citation Format

Share Document