Shall we use hardware sensor measurements or soft-sensor estimates? Case study in a full-scale WWTP

Haimi, Henri; Corona, Francesco; Mulas, Michela; Sundell, Laura; Heinonen, Mari; Vahala, Riku

doi:10.1016/j.envsoft.2015.07.013

Cited by 22 publications

(7 citation statements)

References 46 publications

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“…198 Soft sensing may be another promising way to generate useful information out of easy-to-collect data. 199,200 Maintenance costs: High maintenance costs are a common problem in massively modular infrastructure, 201 because of the costs of regular inspections, cleaning, sludge collection or repair. 202 Their operation will consequently benefit substantially from developments in sensoring and automation.…”

Section: On-site Drinking and Wastewater Treatmentmentioning

confidence: 99%

The Potential of Knowing More: A Review of Data-Driven Urban Water Management

Eggimann

Mutzner

Wani

et al. 2017

Environ. Sci. Technol.

202

131

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The promise of collecting and utilizing large amounts of data has never been greater in the history of urban water management (UWM). This paper reviews several data-driven approaches which play a key role in bringing forward a sea change. It critically investigates whether data-driven UWM offers a promising foundation for addressing current challenges and supporting fundamental changes in UWM. We discuss the examples of better rain-data management, urban pluvial flood-risk management and forecasting, drinking water and sewer network operation and management, integrated design and management, increasing water productivity, wastewater-based epidemiology and on-site water and wastewater treatment. The accumulated evidence from literature points toward a future UWM that offers significant potential benefits thanks to increased collection and utilization of data. The findings show that data-driven UWM allows us to develop and apply novel methods, to optimize the efficiency of the current network-based approach, and to extend functionality of today's systems. However, generic challenges related to data-driven approaches (e.g., data processing, data availability, data quality, data costs) and the specific challenges of data-driven UWM need to be addressed, namely data access and ownership, current engineering practices and the difficulty of assessing the cost benefits of data-driven UWM.

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Section: On-site Drinking and Wastewater Treatmentmentioning

confidence: 99%

The Potential of Knowing More: A Review of Data-Driven Urban Water Management

Eggimann

Mutzner

Wani

et al. 2017

Environ. Sci. Technol.

202

131

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“…Automatic control systems are able to improve the automation of sewage treatment and monitoring, and can reduce the labor intensity of monitoring personnel and field operators. Furthermore, it also enhances the continuity of monitoring of sewage companies with obvious significance [123].…”

Section: Components For Integrating Green Energy With Energy–efficmentioning

confidence: 99%

Integration of Green Energy and Advanced Energy-Efficient Technologies for Municipal Wastewater Treatment Plants

Guo

Sun

Pan

et al. 2019

IJERPH

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Wastewater treatment can consume a large amount of energy to meet discharge standards. However, wastewater also contains resources which could be recovered for secondary uses under proper treatment. Hence, the goal of this paper is to review the available green energy and biomass energy that can be utilized in wastewater treatment plants. Comprehensive elucidation of energy-efficient technologies for wastewater treatment plants are revealed. For these energy-efficient technologies, this review provides an introduction and current application status of these technologies as well as key performance indicators for the integration of green energy and energy-efficient technologies. There are several assessment perspectives summarized in the evaluation of the integration of green energy and energy-efficient technologies in wastewater treatment plants. To overcome the challenges in wastewater treatment plants, the Internet of Things (IoT) and green chemistry technologies for the water and energy nexus are proposed. The findings of this review are highly beneficial for the development of green energy and energy-efficient wastewater treatment plants. Future research should investigate the integration of green infrastructure and ecologically advanced treatment technologies to explore the potential benefits and advantages.

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“…In order to perform estimation of primary variables, models are developed in which correlation between primary and secondary variables are reconstructed [25]. Sensor array Full-scale, municipal [44], [45] NO3-N Sensor rank Full-scale, municipal [46] Soft-sensor has found successful applications to determine quality indices for wastewater by monitoring of related parameters in various research efforts. A summary of soft-sensor application in wastewater quality management considered in this paper is shown in Table 1, in which only data-derived soft sensors are reviewed.…”

Section: Applications Of Soft-sensorsmentioning

confidence: 99%

“…A proposed system that ranks the measurements and estimates shall enable the complementary use of hardware measurement and soft sensor prediction to determine nitrate-nitrogen (NO3 − N) concentrations in the denitrifying post-filtration unit of a municipal WWTP [46]. Automatic changing between measurements from hardware instruments and corresponding software estimate was done using switching procedure based on the ranking of hardware sensor reading and output data from ten soft sensors.…”

Section: Self-validating Soft Sensorsmentioning

confidence: 99%

Physical and Soft Sensor Technologies for Wastewater Quality Management

Mamat¹,

Ramli²,

Abdullah³

et al. 2018

IJEME

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Physical sensors are used mostly to detect sludge and odour in wastewater. Black box modelling or dataderived model using the correlation of input-output parameters is the preferred method as we have assessed. This is due to the non-complex approach of such models as opposed to model-driven, mechanistic models. The latter is hard to be adopted for soft-sensor development due to the inherent complexities and uncertainties. The commonest methods for soft sensor model development are ANN and ANFIS. Many other improvements of these methods are achieved by combining with other techniques to enhance the prediction performance of the soft sensors. Accuracy and precision of data collected for soft sensor modelling has become a vital concern at present to ensure the reliability of wastewater quality indices predicted by the soft sensors. Reduction of the level of reliability of the sensor system in monitoring and controlling of WWTPs would lead to serious lapses in the wastewater quality management. In this backdrop we recommend SEVA soft sensor as one of the best potential solutions which could be offered by the existing technologies.

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Shall we use hardware sensor measurements or soft-sensor estimates? Case study in a full-scale WWTP

Cited by 22 publications

References 46 publications

The Potential of Knowing More: A Review of Data-Driven Urban Water Management

The Potential of Knowing More: A Review of Data-Driven Urban Water Management

Integration of Green Energy and Advanced Energy-Efficient Technologies for Municipal Wastewater Treatment Plants

Physical and Soft Sensor Technologies for Wastewater Quality Management

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