scholarly journals Joint Environmental and Economical Analysis of Wastewater Treatment Plants Control Strategies: A Benchmark Scenario Analysis

2016 ◽  
Vol 8 (4) ◽  
pp. 360 ◽  
Author(s):  
Montse Meneses ◽  
Henry Concepción ◽  
Ramon Vilanova
Keyword(s):  
Wastewater Treatment ◽  
Scenario Analysis ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
Benchmark Scenario ◽  
Economical Analysis

scholarly journals Global Evaluation of Wastewater Treatment Plants Control Strategies Including CO 2 Emissions

2017 ◽  
Vol 50 (1) ◽  
pp. 12956-12961 ◽  
Author(s):  
Marian Barbu ◽  
Ramon Vilanova ◽  
Montse Meneses ◽  
Ignacio Santin
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
Global Evaluation

Benchmark for evaluating control strategies in wastewater treatment plants

1999 ◽  
Author(s):  
J. Alex ◽  
J. F. Beteau ◽  
J. B. Copp ◽  
C. Hellinga ◽  
U. Jeppsson ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Control Strategies

scholarly journals ANN-Based Soft Sensor to Predict Effluent Violations in Wastewater Treatment Plants

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1280 ◽  
Author(s):  
Ivan Pisa ◽  
Ignacio Santín ◽  
Jose Vicario ◽  
Antoni Morell ◽  
Ramon Vilanova
Keyword(s):  
Wastewater Treatment ◽  
Short Term Memory ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
False Positive Rate ◽  
Soft Sensor ◽  
Aquatic Life ◽  
Pollutant Concentrations ◽  
High Concentrations ◽  
Artificial Neural Network Ann

Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce water’s pollutant products, which are harmful to the environment at high concentrations. In addition, regulations are applied by administrations to limit pollutant concentrations in effluent. In this context, control strategies have been adopted by WWTPs to avoid violating these limits; however, some violations still occur. For that reason, this work proposes the deployment of an artificial neural network (ANN)-based soft sensor in which a Long-Short Term Memory (LSTM) network is used to generate predictions of nitrogen-derived components, specifically ammonium ( S N H ) and total nitrogen ( S N t o t ). S N t o t is a limiting nutrient and can therefore cause eutrophication, while nitrogen in the S N H form is toxic to aquatic life. These parameters are used by control strategies to allow actions to be taken in advance and only when violations are predicted. Since predictions complement control strategies, the evaluation of the ANN-based soft sensor was carried out using the Benchmark Simulation Model N.2. (BSM2) and three different control strategies (from low to high control complexity). Results show that our proposed method is able to predict nitrogen-derived products with good accuracy: the probability of detecting violations of BSM2’s limits is 86%–94%. Moreover, the prediction accuracy can be improved by calibrating the soft sensor; for example, perfect prediction of all future violations can be achieved at the expense of increasing the false positive rate.

Lessons learnt from evaluating full-scale ammonium feedback control in three large wastewater treatment plants

2014 ◽  
Vol 69 (7) ◽  
pp. 1573-1580 ◽  
Author(s):  
L. Åmand ◽  
C. Laurell ◽  
K. Stark-Fujii ◽  
A. Thunberg ◽  
B. Carlsson
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
Full Scale ◽  
Feedback Controllers ◽  
Energy Assessment ◽  
Different Types ◽  
Term Evaluation ◽  
Save Energy

Three large wastewater treatment plants in Sweden participate in a project evaluating different types of ammonium feedback controllers in full-scale operation. The goal is to improve process monitoring, maintain effluent water quality and save energy. The paper presents the outcome of the long-term evaluation of controllers. Based on the experiences gained from the full-scale implementations, a discussion is provided about energy assessment for the purpose of comparing control strategies. The most important conclusions are the importance of long-term experiments and the difficulty of comparing energy consumption based on air flow rate measurements.

scholarly journals Ecophysiology of different filamentous Alphaproteobacteria in industrial wastewater treatment plants

Microbiology ◽  
2006 ◽  
Vol 152 (10) ◽  
pp. 3003-3012 ◽  
Author(s):  
Caroline Kragelund ◽  
Yunhong Kong ◽  
Jaap van der Waarde ◽  
Karin Thelen ◽  
Dick Eikelboom ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Industrial Wastewater ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
Bacterial Species ◽  
Enzymic Activity ◽  
Specific Gene ◽  
Industrial Wastewater Treatment ◽  
Potential Control ◽  
Species Specific

The ecophysiology of five filamentous species affiliated to the Alphaproteobacteria was investigated in industrial activated sludge systems. The five species, ‘Candidatus Alysiosphaera europaea’, ‘Candidatus Monilibacter batavus’, ‘Candidatus Alysiomicrobium bavaricum’, ‘Candidatus Sphaeronema italicum’ and Meganema perideroedes, are very abundant in industrial wastewater treatment plants and are often involved in bulking incidents. The morphology of these filamentous bacterial species resembled Eikelboom's Nostocoida limicola, or Type 021N, and could only be correctly identified by using fluorescence in situ hybridization (FISH), applying species-specific gene probes. Two physiological groupings of the five species were found using microautoradiography combined with FISH. Group 1 (‘Ca. Monilibacter batavus' and ‘Ca. Sphaeronema italicum’) utilized many short-chained fatty acids (acetate, pyruvate and propionate), whereas Group 2 (‘Ca. Alysiosphaera europaea’, ‘Ca. Alysiomicrobium bavaricum’ and Meganema perideroedes) could also exploit several sugars, amino acids and ethanol. All species had polyhydroxyalkanoate granules present and several of the species had a very large storage capacity. No activity was found under strict anaerobic conditions, while uptake of substrate was observed in the presence of nitrate or nitrite as potential electron acceptor. However, for all species a reduced number of substrates could be consumed under these conditions compared to aerobic conditions. Only a little exo-enzymic activity was found and nearly all species had a hydrophobic cell surface. Based on knowledge of the ecophysiological potential, control strategies are suggested.

scholarly journals Support vector machines for environmental informatics : application to biological nitrogen removal in wastewater treatment plants

2021 ◽  
Author(s):  
Yinghui Yang
Keyword(s):  
Wastewater Treatment ◽  
Nitrogen Removal ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
Weather Conditions ◽  
Support Vector ◽  
Biological Nitrogen Removal ◽  
Complex Nature ◽  
Generalized Regression Neural Network ◽  
Biological Nitrogen

In order to meet the more stringent environmental regulations, the adaptive and optimal control strategies should be investigated for the biological nitrogen removal (BNR) processes in wastewater treatment plants. Because of the complex nature of the microbial metabolism involved, the conventional mechanistic models for nitrogen removal are difficult to formulate and the existing ones are still uncertain to some extent. Alternatively, the machine learning methods have been investigated as black-box modelling techniques. A new approach, Support Vector Machine (SVM) was proposed to be used to model the biological nitrogen removal processes in this thesis. Specifically, LS-SVM, a simplified formulation of SVM, was applied to predict the concentration of nitrate & nitrite (NO). The simulation results indicate that the proposed method has better generalization performance in comparison with generalized regression neural network, especially under weather conditions that are quite different from the training weather conditions.

scholarly journals Evaluation of control strategies using an oxidation ditch benchmark

2002 ◽  
Vol 45 (4-5) ◽  
pp. 151-158 ◽  
Author(s):  
A. Abusam ◽  
K.J. Keesman ◽  
H. Spanjers ◽  
G. van Straten ◽  
K. Meinema
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
Treatment Plant ◽  
Electrical Energy ◽  
Performance Criteria ◽  
Oxidation Ditch ◽  
Flow Splitting ◽  
And Performance ◽  
Specific Oxidation

This paper presents validation and implementation results of a benchmark developed for a specific full-scale oxidation ditch wastewater treatment plant. A benchmark is a standard simulation procedure that can be used as a tool in evaluating various control strategies proposed for wastewater treatment plants. It is based on model and performance criteria development. Testing of this benchmark, by comparing benchmark predictions to real measurements of the electrical energy consumptions and amounts of disposed sludge for a specific oxidation ditch WWTP, has shown that it can (reasonably) be used for evaluating the performance of this WWTP. Subsequently, the validated benchmark was then used in evaluating some basic and advanced control strategies. Some of the interesting results obtained are the following: (i) influent flow splitting ratio, between the first and the fourth aerated compartments of the ditch, has no significant effect on the TN concentrations in the effluent, and (ii) for evaluation of long-term control strategies, future benchmarks need to be able to assess settlers' performance.

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