scholarly journals Emerging investigator series: automated single-nanoparticle quantification and classification: a holistic study of particles into and out of wastewater treatment plants in Switzerland

2021 ◽  
Author(s):  
Kamyar Mehrabi ◽  
Ralf Kaegi ◽  
Detlef Günther ◽  
Alexander Gundlach-Graham
Keyword(s):  
Wastewater Treatment ◽  
High Throughput ◽  
Single Particle ◽  
Wastewater Treatment Plant ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Metal Nanoparticle ◽  
Single Nanoparticle ◽  
Plant Samples

High-throughput single-particle ICP-TOFMS analyses are used to quantify and classify diverse metal nanoparticle types from wastewater treatment plant samples.

Analysis and balance of phosphorous removal from wastewater at urban wastewater treatment plant

2020 ◽  
Vol 15 (2) ◽  
pp. 142-151
Author(s):  
Peter Lukac ◽  
Lubos Jurik
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Wastewater Treatment Plants ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Phosphorus Recovery ◽  
Anaerobic Sludge ◽  
Biological Phosphorus Removal ◽  
The Mean

Abstract:Phosphorus is a major substance that is needed especially for agricultural production or for the industry. At the same time it is an important component of wastewater. At present, the waste management priority is recycling and this requirement is also transferred to wastewater treatment plants. Substances in wastewater can be recovered and utilized. In Europe (in Germany and Austria already legally binding), access to phosphorus-containing sewage treatment is changing. This paper dealt with the issue of phosphorus on the sewage treatment plant in Nitra. There are several industrial areas in Nitra where record major producers in phosphorus production in sewage. The new wastewater treatment plant is built as a mechanicalbiological wastewater treatment plant with simultaneous nitrification and denitrification, sludge regeneration, an anaerobic zone for biological phosphorus removal at the beginning of the process and chemical phosphorus precipitation. The sludge management is anaerobic sludge stabilization with heating and mechanical dewatering of stabilized sludge and gas management. The aim of the work was to document the phosphorus balance in all parts of the wastewater treatment plant - from the inflow of raw water to the outflow of purified water and the production of excess sludge. Balancing quantities in the wastewater treatment plant treatment processes provide information where efficient phosphorus recovery could be possible. The mean daily value of P tot is approximately 122.3 kg/day of these two sources. The mean daily value of P tot is approximately 122.3 kg/day of these two sources. There are also two outflows - drainage of cleaned water to the recipient - the river Nitra - 9.9 kg Ptot/day and Ptot content in sewage sludge - about 120.3 kg Ptot/day - total 130.2 kg Ptot/day.

Use of modelling for optimization and upgrade of a tropical wastewater treatment plant in a developing country

2007 ◽  
Vol 56 (7) ◽  
pp. 21-31 ◽  
Author(s):  
D. Brdjanovic ◽  
M. Mithaiwala ◽  
M.S. Moussa ◽  
G. Amy ◽  
M.C.M. van Loosdrecht
Keyword(s):  
Wastewater Treatment ◽  
Developing Country ◽  
Wastewater Treatment Plant ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Coupled Model ◽  
Reject Water ◽  
Complex Interactions ◽  
N Removal ◽  
Discharge Criteria

This paper presents results of a novel application of coupling the Activated Sludge Model No. 3 (ASM3) and the Anaerobic Digestion Model No.1 (ADM1) to assess a tropical wastewater treatment plant in a developing country (Surat, India). In general, the coupled model was very capable of predicting current plant operation. The model proved to be a useful tool in investigating various scenarios for optimising treatment performance under present conditions and examination of upgrade options to meet stricter and upcoming effluent discharge criteria regarding N removal. It appears that use of plant-wide modelling of wastewater treatment plants is a promising approach towards addressing often complex interactions within the plant itself. It can also create an enabling environment for the implementations of the novel side processes for treatment of nutrient-rich, side-streams (reject water) from sludge treatment.

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.

scholarly journals Antibiotic Resistance Genes Dynamics at the Different Stages of the Biological Process in a Full-Scale Wastewater Treatment Plant

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 650 ◽  
Author(s):  
Ioanna Zerva ◽  
Ioanna Alexandropoulou ◽  
Maria Panopoulou ◽  
Paraschos Melidis ◽  
Spyridon Ntougias
Keyword(s):  
Antibiotic Resistance ◽  
Wastewater Treatment ◽  
Resistance Genes ◽  
Wastewater Treatment Plant ◽  
Biological Process ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Antibiotic Resistance Genes ◽  
Additional Treatment ◽  
Full Scale

Wastewater treatment plants (WWTPs) highly contribute to the transmission of antibiotic resistance genes (ARGs) in the environment. In this work, the diversity of ermF, ermB, sul1 and int1-enconding genes was examined in the influent, the mixed liquor and the effluent of a full-scale WWTP. Based on the clones analyzed, similar genotypes were recorded at all process stages. However, distinct genotypes of int1 were responsible for the expression of sul1 and ermF genes in Gammaproteobacteria and Bacteroidetes, respectively. Due to the detection of similar ARGs profiles throughout the biological process, it is concluded that additional treatment is needed for their retention.

Research on Variable Metric Chaos Optimization Modeling for the Prediction of Wastewater Treatment Plants Performance

2012 ◽  
Vol 428 ◽  
pp. 169-175
Author(s):  
Guo Kai Fu ◽  
Yi Yue Hu ◽  
Zhi Zhang
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Treatment System ◽  
Variable Metric ◽  
Chaos Optimization ◽  
Actual Operation ◽  
The Stability

A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. For the multi-variable, uncertainty, non-linear characteristics of the wastewater treatment system, a variable metric chaos optimization neural network (VMCNW) prediction model is established standing on the actual operation data in the wasterwater treatment system. The model overcomes several disadvantages of the conventional BP neural network. Namely:slow convergence, low accuracy and difficulty in finding the global optimum.The results of model calculation show that the predicted value can better match measured value,played a effect of simulating and predicting and be able to optimize the operation status. The establishment of the predicting model provide a simple and practical way for the operation and management in wastewater treatment plant,and have good research and engineering practical value.

Assessment of land occupation of municipal wastewater treatment plants in China

2018 ◽  
Vol 4 (12) ◽  
pp. 1988-1996 ◽  
Author(s):  
Yan He ◽  
Yishuang Zhu ◽  
Jinghan Chen ◽  
Minsheng Huang ◽  
Guohua Wang ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Municipal Wastewater ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Land Resources ◽  
Municipal Wastewater Treatment ◽  
Land Occupation ◽  
Municipal Wastewater Treatment Plants ◽  
Management Issues

The tense deficiency of available land resources is becoming one of the bottlenecks in dealing with wastewater treatment plant (WWTP) management issues.

scholarly journals Estimation of greenhouse gas (GHG) emissions from natural lagoon wastewater treatment plant: Case of Ain Taoujdate-Morocco

2020 ◽  
Vol 150 ◽  
pp. 01012
Author(s):  
Yassine Bahi ◽  
Ahmed Akhssas ◽  
Mohamed Khamar ◽  
Lahcen Bahi ◽  
Hanane Souidi
Keyword(s):  
Wastewater Treatment ◽  
Greenhouse Gas ◽  
Wastewater Treatment Plant ◽  
Wastewater Treatment Plants ◽  
Ghg Emissions ◽  
Treatment Plant ◽  
Empirical Method ◽  
Monthly Basis ◽  
Total Contribution ◽  
Simple Modeling

The process of removing organic components from wastewater as BOD5 through wastewater treatment plants has been proven to be a significant source of greenhouse gas emissions, mainly methane CH4, carbon dioxide CO2 and nitrous oxide N2O. The reduction of these emissions has attracted more interest given their major contribution to global warming. This study was able to identify and estimate the amount of methane and CO2 emissions on a monthly basis by a simple modeling approach and an empirical method (IPCC) for N2O emissions, in the case of Ain-Taoujdate wastewater treatment plant, throughout the years 2013, 2018 and 2019. The results showed that anaerobic ponds were the main source of on-site emissions with 66% of total contribution and 33% for facultative ponds, followed by the energy consumption of the pumping station as off-site GHG emissions.

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