The importance of the treatment plant performance during rain to acute water pollution

1996 ◽  
Vol 34 (3-4) ◽  
Keyword(s):  
Water Pollution ◽  
Treatment Plant ◽  
Plant Performance

The importance of the treatment plant performance during rain to acute water pollution

1996 ◽  
Vol 34 (3-4) ◽  
pp. 1-8 ◽  
Author(s):  
W. Rauch ◽  
P. Harremoës
Keyword(s):  
Water Pollution ◽  
Deterministic Model ◽  
Water Quality Management ◽  
Treatment Plant ◽  
Drainage System ◽  
Oxygen Depletion ◽  
Plant Performance ◽  
Total System ◽  
Combined Sewer Overflows ◽  
The Difference

Rain causes not only pollution loads from combined sewer overflows but also from treatment plants. High hydraulic load conditions can affect the secondary clarifier performance, resulting in a massive loss of sludge from the plant. The consequence to the oxygen concentration in the recipient can be described by the same simplified mechanism, because the principle remains the same. The difference that has to be accounted for, is the different organic characteristics of the discharged water. The analysis of a hypothetical urban drainage system by means of a deterministic model reveals the importance of the treatment plant in this respect. Oxygen depletion in urban rivers is caused by intermittent discharges from both sewer system and wastewater treatment plant. Neglecting one of them in the evaluation of the environmental impact gives a wrong impression of total system behavior. Linear sensitivity analysis provides useful information for water quality management. The significant parameters in terms of acute water pollution are identified.

A Study of the Planning of Water Pollution Control for Second Songhua River in China

1991 ◽  
Vol 23 (1-3) ◽  
pp. 41-48
Author(s):  
Yin Jun
Keyword(s):  
Water Pollution ◽  
Mathematical Model ◽  
Pollution Control ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Initial Structure ◽  
Stable Model ◽  
Control Plan ◽  
Songhua River ◽  
The Second Songhua River

The paper takes the Second Songhua River as an object for research and selects Thomas's BOD-DO stable model as the initial structure on the basis of overall investigations and analyses on water pollution in every reach. In view of the characteristics of the river being located at the north, values k’1, k’2 and k’3 in dry season of winter were determined and calculated, and a series analyses have been made. The self-purification ability of the river and the total elimination amount of the main pollutants BOD5 were also calculated. In order to minimize the required cost, we distributed the cost to the main pollution sources, which are to be controlled. We firstly set a cost function of sewage treatment plant by series design and calculated the related cost parameters, then calculated two kinds of optimal distributing models of BOD5 elimination, which were a mathematical model of extreme value of conditions and a matrix mathematical model. Now they have been applied to the practical pollution control plan for the Second Songhua River.

Approach to the Reforms in Nightsoil Treatment Process Introduced Abroad

1991 ◽  
Vol 24 (5) ◽  
pp. 189-196 ◽  
Author(s):  
S. B. Guo ◽  
R. Z. Chen ◽  
G. Li ◽  
H. Y. Shoichi
Keyword(s):  
Treatment Cost ◽  
Treatment Process ◽  
Biological Process ◽  
Treatment Plant ◽  
Plant Performance ◽  
First Year ◽  
Original Design ◽  
Effluent Quality ◽  
Public Toilets

In 1987 Guangzhou Liede Nightsoil Treatment Plant started commissioning. The purpose of the plant is to dispose of 400 tons of nightsoi1 from city public toilets per day. In the first year of the commissioning a biological process was basically used according to the original design made by a Danish company. Practically it has been proved that the design is effective. The process can reduce BOD from 3800 mg/l to about 133 mg/l, or by approximately 96.5 percent. The performance of the sludge digester system is satisfactory. Because the primary investigation on characteristics of the nightsoil was insufficient there were some problems raised during the commissioning. So in the first year the effluent failed to achieve the desired quality. After the analysis of the plant performance some necessary reforms have been carried out. Now the effluent quality can stably meet the national discharge limits and the treatment cost decreases.

Use of chemical upgrading (CU) in Hungary and Slovakia

1994 ◽  
Vol 30 (5) ◽  
pp. 87-95 ◽  
Author(s):  
Susan E. Murcott ◽  
Donald R. F. Harleman
Keyword(s):  
Wastewater Treatment ◽  
Biological Treatment ◽  
Treatment Plant ◽  
Domestic Sewage ◽  
Metal Salts ◽  
Plant Performance ◽  
Eastern European ◽  
Low Doses ◽  
The Past ◽  
Treatment Technologies

In the past decade, the development of polymers and new chemical technologies has opened the way to using low doses of chemicals in wastewater treatment. “Chemical upgrading” (CU) is defined in this paper as an application of these chemical technologies to upgrade overloaded treatment systems (typically consisting of conventional primary plus biological treatment) in Central and Eastern European (CEE) countries. Although some of the chemical treatment technologies are proven ones in North America, Scandinavia, and Germany, a host of factors, for example, the variations in composition and degree of pollution, the type of technologies in use, the type and mix of industrial and domestic sewage, and the amount of surface water, had meant that the viability of using CU in CEE countries was unknown. This report describes the first jar tests of CU conducted during the summer of 1993. The experiments show CU's ability to improve wastewater treatment plant performance and to potentially assist in the significant problem of overloaded treatment plants. Increased removal of BOD, TSS, and P in the primary stage of treatment is obtained at overflow rates above 1.5 m/h, using reasonably priced, local sources of metal salts in concentrations of 25 to 50 mg/l without polymers.

Impacts of separate rejection water treatment on the overall plant performance

2003 ◽  
Vol 48 (4) ◽  
pp. 139-146 ◽  
Author(s):  
B. Wett ◽  
J. Alex
Keyword(s):  
Water Treatment ◽  
Treatment Plant ◽  
Operating Experience ◽  
Plant Performance ◽  
High Tech ◽  
State Variables ◽  
Total N ◽  
Unit Process ◽  
Innovative Tool ◽  
The Impact

A separate rejection water treatment appears as a high-tech unit process which might be recommendable only for specific cases of an upgrading of an existing wastewater treatment plant. It is not the issue of this paper to consider a specific separate treatment process itself but to investigate the influence of such a process on the overall plant performance. A plant-wide model has been applied as an innovative tool to evaluate effects of the implemented sidestream strategy on the mainstream treatment. The model has been developed in the SIMBA environment and combines acknowledged mathematical descriptions of the activated sludge process (ASM1) and the anaerobic mesophilic digestion (Siegrist model). The model's calibration and validation was based on data from 5 years of operating experience of a full-scale rejection water treatment. The impact on the total N-elimination efficiency is demonstrated by detailed nitrogen mass flow schemes including the interactions between the wastewater and the sludge lane. Additionally limiting conditions due to dynamic N-return loads are displayed by the model's state variables.

scholarly journals Biological Nutrient Removal Model No. 2 (BNRM2): a general model for wastewater treatment plants

2013 ◽  
Vol 67 (7) ◽  
pp. 1481-1489 ◽  
Author(s):  
R. Barat ◽  
J. Serralta ◽  
M. V. Ruano ◽  
E. Jiménez ◽  
J. Ribes ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Nitrogen Removal ◽  
Nutrient Removal ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Operating Conditions ◽  
Plant Performance ◽  
Biological Nutrient Removal ◽  
Nitrite Oxidizing Bacteria ◽  
Removal Model

This paper presents the plant-wide model Biological Nutrient Removal Model No. 2 (BNRM2). Since nitrite was not considered in the BNRM1, and this previous model also failed to accurately simulate the anaerobic digestion because precipitation processes were not considered, an extension of BNRM1 has been developed. This extension comprises all the components and processes required to simulate nitrogen removal via nitrite and the formation of the solids most likely to precipitate in anaerobic digesters. The solids considered in BNRM2 are: struvite, amorphous calcium phosphate, hidroxyapatite, newberite, vivianite, strengite, variscite, and calcium carbonate. With regard to nitrogen removal via nitrite, apart from nitrite oxidizing bacteria two groups of ammonium oxidizing organisms (AOO) have been considered since different sets of kinetic parameters have been reported for the AOO present in activated sludge systems and SHARON (Single reactor system for High activity Ammonium Removal Over Nitrite) reactors. Due to the new processes considered, BNRM2 allows an accurate prediction of wastewater treatment plant performance in wider environmental and operating conditions.

Integrated Wastewater Treatment Plant Performance Evaluation Using Artificial Neural Networks

1999 ◽  
Vol 40 (7) ◽  
pp. 55-65 ◽  
Author(s):  
Mohamed F. Hamoda ◽  
Ibrahim A. Al-Ghusain ◽  
Ahmed H. Hassan
Keyword(s):  
Neural Networks ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Wastewater Treatment Plant ◽  
Municipal Wastewater ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Plant Performance ◽  
Municipal Wastewater Treatment ◽  
Artificial Neural

Proper operation of municipal wastewater treatment plants is important in producing an effluent which meets quality requirements of regulatory agencies and in minimizing detrimental effects on the environment. This paper examined plant dynamics and modeling techniques with emphasis placed on the digital computing technology of Artificial Neural Networks (ANN). A backpropagation model was developed to model the municipal wastewater treatment plant at Ardiya, Kuwait City, Kuwait. Results obtained prove that Neural Networks present a versatile tool in modeling full-scale operational wastewater treatment plants and provide an alternative methodology for predicting the performance of treatment plants. The overall suspended solids (TSS) and organic pollutants (BOD) removal efficiencies achieved at Ardiya plant over a period of 16 months were 94.6 and 97.3 percent, respectively. Plant performance was adequately predicted using the backpropagation ANN model. The correlation coefficients between the predicted and actual effluent data using the best model was 0.72 for TSS compared to 0.74 for BOD. The best ANN structure does not necessarily mean the most number of hidden layers.

Future Directions in Instrumentation, Control and Automation in the Water and Wastewater Industry

1993 ◽  
Vol 28 (11-12) ◽  
pp. 9-14 ◽  
Author(s):  
Troy D. Vassos
Keyword(s):  
Control Strategies ◽  
Treatment Plant ◽  
Dynamic Modelling ◽  
Simulation Software ◽  
Plant Performance ◽  
Limiting Factors ◽  
Time Data ◽  
Training Environment ◽  
Factors Affecting ◽  
Water And Wastewater

The need to optimize treatment plant performance and to meet increasingly stringent effluent criteria are two key factors affecting future development of instrumentation, control and automation (ICA) applications in the water and wastewater industry. Two case studies are presented which highlight the need for dynamic modelling and simulation software to assist operations staff in developing effective instrumentation control strategies, and to provide a training environment for the evaluation of such strategies. One of the limiting factors to date in realizing the potential benefits of ICA has been the inability to adequately interpret the large number of existing instrumentation inputs available at treatment facilities. The number of inputs can exceed the number of control loops by up to three orders of magnitude. The integration of dynamic modelling and expert system software is seen to facilitate the interpretation of real-time data, allowing both quantitative (instrumented) and qualitative (operator input) information to be integrated for process control. Improvements in sensor reliability and performance, and the development of biological monitoring sensors and control algorithms are also discussed.

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