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.

scholarly journals Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1149
Author(s):  
Pedro Oliveira ◽  
Bruno Fernandes ◽  
Cesar Analide ◽  
Paulo Novais
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Wastewater Treatment Plants ◽  
Learning Approach ◽  
Sustainable Cities ◽  
Short Term ◽  
Long Short Term Memory ◽  
Gated Recurrent Units

A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh.

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

Quality improvement of biosolids by ferrate(VI) oxidation of offensive odour compounds

1996 ◽  
Vol 33 (3) ◽  
pp. 119-130 ◽  
Author(s):  
Allen C. Chao ◽  
Sergio J. de Luca ◽  
Carlos N. Idle
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Sewage Treatment ◽  
Potassium Ferrate ◽  
Sulphur Compounds ◽  
Environmental Recovery ◽  
Oxidizing Power ◽  
Aldehydes And Ketones ◽  
High Concentrations ◽  
By Products

Studies concerning the treatment, stabilization and final disposal of biosolids, one of the by-products of wastewater treatment, in environmental recovery, have been intensified by the sanitary and environmental effects of land disposal. The careful assessment of biosolid quality shows that, when appropriately managed, the environmental risks of their uses can be minimized by chemical stabilization, and biosolids could even be used as fertilizer and soil conditioner. A research study of biosolid stabilization was performed using lime as a standard process compared to potassium ferrate (VI). The chances of leaching and solubilization of metals were tested, simulating conditions for disposal in the environment. The sanitary effectiveness in terms of pathogens (bacteria, fungi and helminth eggs) were also evaluated. Experiments were performed on the lime and ferrate(VI) treatment of compounds such as ammonia, nitrate, soluble sulphides, and total sulphates, indicators of odouriferous offensive compounds which might occasionally prevent some uses of the solids, and the results are presented in this paper. Wastewater Treatment Plants emit offensive odours generated during the sewage treatment process, as well as during the treatment and the management of biosolids. This occurs in the drying beds and the spreading of biosolids on land, due to the high concentrations of sulphur compounds, nitrogen compounds, acids and organic compounds (aldehydes and ketones). The potassium ferrate(VI) utilized in the research is a powerful oxidizing agent throughout the pH scale, with the advantage of not generating by-products which will cause toxicity or mutagenicity (DE LUCA, 1981). The ion ferrate(VI) has greater oxidizing power than permanganate, e.g., it oxidizes reduced sulfur forms to sulphate, ammonia to nitrate, hypochlorite to chlorite and chlorite to chlorate(DE LUCA et al., 1992; CHAO et al., 1992). This paper shows that, as expected, the potassium ferrate (VI) treatment replaces several chemical products utilized for odour control of sludges, mainly aggressive odours caused by ammonia and sulphides, through the formation of precipitates with iron compounds. Ferrate (VI) has often been shown to destroy soluble sulphides, transforming them into sulphate. The generation of oxygen in the decomposition of ferrate(VI) increases its oxidizing power. Ferrate(VI) applied to sludges also has the double effect of transforming ammonia into nitrates, such that this product takes the place of sulphates, acting as an electron acceptor, thus preventing the development of further odours when biosolids are utilized.

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 Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3139 ◽  
Author(s):  
Félix Hernández-del-Olmo ◽  
Elena Gaudioso ◽  
Natividad Duro ◽  
Raquel Dormido
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Control Systems ◽  
Weather Forecasting ◽  
Wastewater Treatment Plants ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Soft Sensor ◽  
Physical Sensors ◽  
Useful Signal

Control of wastewater treatment plants (WWTPs) is challenging not only because of their high nonlinearity but also because of important external perturbations. One the most relevant of these perturbations is weather. In fact, different weather conditions imply different inflow rates and substance (e.g., N-ammonia, which is among the most important) concentrations. Therefore, weather has traditionally been an important signal that operators take into account to tune WWTP control systems. This signal cannot be directly measured with traditional physical sensors. Nevertheless, machine learning-based soft-sensors can be used to predict non-observable measures by means of available data. In this paper, we present novel research about a new soft-sensor that predicts the current weather signal. This weather prediction differs from traditional weather forecasting since this soft-sensor predicts the weather conditions as an operator does when controling the WWTP. This prediction uses a model based on past WWTP influent states measured by only a few physical and widely applied sensors. The results are encouraging, as we obtained a good accuracy level for a relevant and very useful signal when applied to advanced WWTP control systems.

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.

Integrated soft sensor model for flow control

2006 ◽  
Vol 53 (4-5) ◽  
pp. 473-482 ◽  
Author(s):  
G. Äijälä ◽  
D. Lumley
Keyword(s):  
Wastewater Treatment ◽  
Control System ◽  
Flow Control ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Soft Sensor ◽  
Plant Performance ◽  
Unit Process ◽  
Flow Models ◽  
Flow Control System

Tighter discharge permits often require wastewater treatment plants to maximize utilization of available facilities in order to cost-effectively reach these goals. Important aspects are minimizing internal disturbances and using available information in a smart way to improve plant performance. In this study, flow control throughout a large highly automated wastewater treatment plant (WWTP) was implemented in order to reduce internal disturbances and to provide a firm foundation for more advanced process control. A modular flow control system was constructed based on existing instrumentation and soft sensor flow models. Modules were constructed for every unit process in water treatment and integrated into a plant-wide model. The flow control system is used to automatically control recirculation flows and bypass flows at the plant. The system was also successful in making accurate flow estimations at points in the plant where it is not possible to have conventional flow meter instrumentation. The system provides fault detection for physical flow measuring devices. The module construction allows easy adaptation for new unit processes added to the treatment plant.

Sign in / Sign up

Export Citation Format

Share Document