Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques

Corominas, Lluís; Garrido-Baserba, Manel; Villez, Kris; Olsson, Gustaf; Cortés, Ulises; Poch, Manel

doi:10.1016/j.envsoft.2017.11.023

Cited by 158 publications

(88 citation statements)

References 169 publications

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“…By several accounts, the lack of online sensor data quality poses a longstanding challenge for both the advancement of environmental science and engineering practice (Rieger et al, 2005(Rieger et al, , 2006Rosén et al, 2008;Rieger et al, 2010;Haimi et al, 2013;Corominas et al, 2018). It is therefore not surprising that considerable time and energy has been invested in methods for automated quality assessment and quality control of online measurement devices (e.g., Thomann et al, 2002;Thomann, 2008;Corominas et al, 2011;Spindler and Vanrolleghem, 2012;Alferes et al, 2013;Spindler, 2014;Villez and Habermacher, 2016;Le et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Characterizing Long-term Wear and Tear of Ion-Selective pH Sensors

Ohmura¹,

Thürlimann²,

Kipf³

et al. 2018

Preprint

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The development and validation of methods for fault detection and identification in wastewater treatment research today relies on two important assumptions: {\em (i)} that sensor faults appear at distinct times in different sensors and {\em (ii)} that any given sensor will function near-perfectly for a significant amount of time following installation. In this work, we show that such assumptions are unrealistic, at least for sensors built around an ion-selective measurement principle. Indeed, long-term exposure of sensors to treated wastewater shows that sensors exhibit important fault symptoms that appear simultaneously and with similar intensity. Consequently, our work suggests that focus of research on methods for fault detection and identification should be reoriented towards methods that do not rely on the assumptions mentioned above. This study also provides the very first empirically validated sensor fault model for wastewater treatment simulation and we recommend its use for effective benchmarking of both fault detection and identification methods and advanced control strategies. Finally, we evaluate the value of redundancy for the purpose of remote sensor validation in decentralized wastewater treatment systems.

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Section: Introductionmentioning

confidence: 99%

Characterizing Long-term Wear and Tear of Ion-Selective pH Sensors

Ohmura¹,

Thürlimann²,

Kipf³

et al. 2018

Preprint

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“…Ability of arbitrary selection of variables in creation process of ANN model also involves cognitive aspect of given phenomenon [17]. Based on rank analysis of individual input variables, significance classification in terms of their influence on approximated value can be made [18]. Ranking allow better understanding of changes observed during given process [19].…”

Section: Resultsmentioning

confidence: 99%

Artificial Neural Network (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases

Ofman

Struk-Sokołowska

2019

Water

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Paper presents artificial neural network models (ANN) approximating concentration of selected nitrogen forms in wastewater after sequence batch reactor operating with aerobic granular activated sludge (GSBR) in the anaerobic and aerobic phases. Aim of the study was to determine parameters conditioning effectiveness of selected nitrogen forms removal in GSBR reactor process phases. Models of artificial neural networks were developed separately for N-NH4, N-NO3 and total nitrogen concentration in particular process phases of GSBR reactor. In total, 6 ANN models were presented in this paper. ANN models were made as multilayer perceptron (MLP), which were learned using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Developed ANN models indicated variables the most influencing of particular nitrogen forms in aerobic and anaerobic phase of GSBR reactor. Concentration of estimated nitrogen form at the beginning of anaerobic or aerobic phase, depending on ANN model, in all ANN models influenced approximated value. Obtained determination coefficients varied from 0.996 to 0.999 and were depending on estimated nitrogen form and GSBR process phase. Hence, developed ANN models can be used in further studies on modeling of nitrogen forms in anaerobic and aerobic phase of GSBR reactors.

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“…In engineering practice, water-treatment and wastewatertreatment plants are becoming more highly automated, and remote monitoring is increasingly used in distribution and/or conveyance systems, resulting in a substantial increase in the amount of data generated during system operation. These developments offer opportunities for performance optimization (Corominas et al 2018;Ingildsen and Olsson 2016). They may also allow for novel management strategies, such as using excess sewer capacity to reduce overflows at wastewater-treatment plants (Zhang et al 2018).…”

Section: Data Fire Hosementioning

confidence: 99%