Fault detection for control of wastewater treatment plants

2006 ◽  
Vol 53 (4-5) ◽  
pp. 375-382 ◽  
Author(s):  
O. Schraa ◽  
B. Tole ◽  
J.B. Copp
Keyword(s):  
Wastewater Treatment ◽  
Process Control ◽  
Real Time ◽  
Control Structure ◽  
Wastewater Treatment Plants ◽  
Process Models ◽  
Sensor Data ◽  
Time Data ◽  
Model Based Control ◽  
Model Based

Interest in real-time model-based control is increasing as more and more facilities are being asked to meet stricter effluent requirements while at the same time minimizing costs. It has been identified that biological process models and automated process control technologies are being used at wastewater treatments plants throughout the world and that great potential for optimising biotreatment may exist with the integration of these two technology areas. According to our experience, wastewater treatment plants are indeed looking for ways to successfully integrate their modelling knowledge into their process control structure; however, there are practical aspects of this integration that must be addressed if the benefits of this integration are to be realised. This paper discusses the practical aspects of monitoring, filtering and analysing real sensor data with an aim to produce a reliable real-time data stream that might be used within a model-based control structure. Several real case study examples are briefly discussed in this paper.

Components of a model-based operation system for wastewater treatment plants

1999 ◽  
Vol 39 (4) ◽  
pp. 103-111 ◽  
Author(s):  
Frank Obenaus ◽  
Karl-Heinz Rosenwinkel ◽  
Jens Alex ◽  
Ralf Tschepetzki ◽  
Ulrich Jumar
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Operation System ◽  
Model Based ◽  
Measuring Devices ◽  
Crucial Component ◽  
Simulation Calculation ◽  
On Line ◽  
Main Components

This report presents the main components of a system for the model-based control of aerobic biological wastewater treatment plants. The crucial component is a model which is linked to the actual processes via several interfaces and which contains a unit which can immediately follow up the current process state. The simulation calculation of the model is based on data which are yielded by on-line measuring devices. If the sensors should fail at times, there are available a number of alternative concepts, some of which are based on the calculations of artificial neural networks or linear methods.

Innovative real-time sensing of flow dynamics in groundwater and sediments to map contaminant spreading

2021 ◽  
Author(s):  
Goedele Verreydt ◽  
Niels Van Putte ◽  
Timothy De Kleyn ◽  
Joris Cool ◽  
Bino Maiheu
Keyword(s):  
Surface Water ◽  
Groundwater Flow ◽  
Real Time ◽  
Flow Direction ◽  
Flow Dynamics ◽  
Sensor Data ◽  
Time Data ◽  
Mass Fluxes ◽  
First Case

<p>Groundwater dynamics play a crucial role in the spreading of a soil and groundwater contamination. However, there is still a big gap in the understanding of the groundwater flow dynamics. Heterogeneities and dynamics are often underestimated and therefore not taken into account. They are of crucial input for successful management and remediation measures. The bulk of the mass of mass often is transported through only a small layer or section within the aquifer and is in cases of seepage into surface water very dependent to rainfall and occurring tidal effects.</p><p> </p><p>This study contains the use of novel real-time iFLUX sensors to map the groundwater flow dynamics over time. The sensors provide real-time data on groundwater flow rate and flow direction. The sensor probes consist of multiple bidirectional flow sensors that are superimposed. The probes can be installed directly in the subsoil, riverbed or monitoring well. The measurement setup is unique as it can perform measurements every second, ideal to map rapid changing flow conditions. The measurement range is between 0,5 and 500 cm per day.</p><p> </p><p>We will present the measurement principles and technical aspects of the sensor, together with two case studies.</p><p> </p><p>The first case study comprises the installation of iFLUX sensors in 4 different monitoring wells in a chlorinated solvent plume to map on the one hand the flow patterns in the plume, and on the other hand the flow dynamics that are influenced by the nearby popular trees. The foreseen remediation concept here is phytoremediation. The sensors were installed for a period of in total 4 weeks. Measurement frequency was 5 minutes. The flow profiles and time series will be presented together with the determined mass fluxes.</p><p> </p><p>A second case study was performed on behalf of the remediation of a canal riverbed. Due to industrial production of tar and carbon black in the past, the soil and groundwater next to the small canal ‘De Lieve’ in Ghent, Belgium, got contaminated with aliphatic and (poly)aromatic hydrocarbons. The groundwater contaminants migrate to the canal, impact the surface water quality and cause an ecological risk. The seepage flow and mass fluxes of contaminants into the surface water were measured with the novel iFLUX streambed sensors, installed directly in the river sediment. A site conceptual model was drawn and dimensioned based on the sensor data. The remediation concept to tackle the inflowing pollution: a hydraulic conductive reactive mat on the riverbed that makes use of the natural draining function of the waterbody, the adsorption capacity of a natural or secondary adsorbent and a future habitat for micro-organisms that biodegrade contaminants. The reactive mats were successfully installed and based on the mass flux calculations a lifespan of at least 10 years is expected for the adsorption material.  </p>

scholarly journals AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiang Yu ◽  
Chun Shan ◽  
Jilong Bian ◽  
Xianfei Yang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Computing Environment ◽  
The Real ◽  
Edge Node ◽  
Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

Assessing energy performance and critical issues of a large wastewater treatment plant through full-scale data benchmarking

2019 ◽  
Vol 80 (8) ◽  
pp. 1421-1429 ◽  
Author(s):  
Maria Rosa di Cicco ◽  
Antonio Spagnuolo ◽  
Antonio Masiello ◽  
Carmela Vetromile ◽  
Mariano Nappa ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Real Time ◽  
Wastewater Treatment Plant ◽  
Energy Demand ◽  
Treatment Plant ◽  
Energy Performance ◽  
Detailed Knowledge ◽  
Time Data ◽  
Critical Issues ◽  
System Variables

Abstract The wastewater sector accounts for 25% of the global energy demand in the water sector. Since this consumption is expected to increase in the forthcoming years, energy optimization strategies are needed. A truly effective planning of energy improvement measures requires a detailed knowledge of a system, which can only be achieved through energy audit and real-time monitoring. In order to improve the identification of critical issues related to the use of energy resources within a wastewater treatment plant (WWTP), the paper shows the results of a monitoring campaign performed on a large WWTP in southern Italy. Data obtained for the audit cover a 4-year timeframe (2014–2017). Energy–environmental performance has been evaluated through the benchmarking of: system variables, specific consumptions, and operational indicators. Moreover, by using a real-time data measurement and acquisition system it has been possible to evaluate the real performance of the most energy-intensive apparatus of the plant (a turbo-blower), over a period of 8 months. The main results indicate that (a) the plant is mainly affected by a massive capture of infiltrations, working in conditions close to the maximum hydraulic capacity, (b) real-time energy measurements are necessary to accurately characterize plant consumptions and adequately assess their critical aspects.

scholarly journals Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data

2020 ◽  
Vol 12 (23) ◽  
pp. 10175
Author(s):  
Fatima Abdullah ◽  
Limei Peng ◽  
Byungchul Tak
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Streaming Data ◽  
Sensor Data ◽  
Simple Type ◽  
Data Sampling ◽  
Time Data ◽  
Latency Reduction ◽  
Network Delays ◽  
Real Time Data

The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to heavily delayed network operations. In streaming data analytics, the ability to obtain real time data insight is crucial for computational sustainability for many IoT enabled applications such as environmental monitors, pollution and climate surveillance, traffic control or even E-commerce applications. However, such network delays prevent us from achieving high quality real-time data analytics of environmental information. In order to address this challenge, we propose the Fog Sampling Node Selector (Fossel) technique that can significantly reduce the IoT network and processing delays by algorithmically selecting an optimal subset of fog nodes to perform the sensor data sampling. In addition, our technique performs a simple type of query executions within the fog nodes in order to further reduce the network delays by processing the data near the data producing devices. Our extensive evaluations show that Fossel technique outperforms the state-of-the-art in terms of latency reduction as well as in bandwidth consumption, network usage and energy consumption.

scholarly journals Real time detection and characterisation of bioaerosol emissions from wastewater treatment plants

2020 ◽  
Vol 721 ◽  
pp. 137629 ◽  
Author(s):  
Jiang-han Tian ◽  
Cheng Yan ◽  
Zaheer Ahmad Nasir ◽  
Sonia Garcia Alcega ◽  
Sean Tyrrel ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Real Time ◽  
Wastewater Treatment Plants ◽  
Real Time Detection
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