Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network

Cañete, J. Fernández de; Saz-Orozco, P. del; Baratti, Roberto; Mulas, Michela; Ruano, A. E.; García-Cerezo, Alfonso J.

doi:10.1016/j.eswa.2016.06.028

Cited by 95 publications

(31 citation statements)

References 34 publications

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“…These include neural networks (Zhang et al, 2019;Ta & Wei, 2018;Ren et al, 2018;Dabrowski et al, 2018a;de Canete et al, 2016;Schmid & Koskiaho, 2006;Dogan et al, 2009;Ranković et al, 2010;Basant et al, 2010;He et al, 2011;Ahmed, 2017) and other machine learning models (Shi et al, 2019;Xu et al, 2017;Olyaie et al, 2017;Duan et al, 2016). Dabrowski et al (2018b) describe two datadriven state-space models for modelling DO, pH, and temperature in prawn ponds.…”

Section: Water Quality Modellingmentioning

confidence: 99%

Enforcing mean reversion in state space models for prawn pond water quality forecasting

Dabrowski

Rahman

Pagendam

et al. 2020

Computers and Electronics in Agriculture

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The contribution of this study is a novel approach to introduce mean reversion in multi-step-ahead forecasts of state-space models. This approach is demonstrated in a prawn pond water quality forecasting application.The mean reversion constrains forecasts by gradually drawing them to an average of previously observed dynamics. This corrects deviations in forecasts caused by irregularities such as chaotic, non-linear, and stochastic trends. The key features of the approach include (1) it enforces mean reversion, (2) it provides a means to model both short and long-term dynamics, (3) it is able to apply mean reversion to select structural state-space components, and (4) it is simple to implement. Our mean reversion approach is demonstrated on various state-space models and compared with several time-series models on a prawn pond water quality dataset. Results show that mean reversion reduces long-term forecast errors by over 60% to produce the most accurate models in the comparison. more) of pond water temperature typically varies within some bounds. These bounds are maintained as the underlying process seeks thermodynamic equilibrium within a changing environment. Without knowledge of the underlying process, the longer-term dynamics can appear as a slowly varying stochastic trend. Forecasting such processes can be challenging when stochastic trends cause forecasts to deviate. Models should realistically incorporate some form of constraint or bounds. Our hypothesis is that such a constraint can be imposed by modelling the stochastic variations with a fixed attractor distribution that long-term trends are drawn towards. In this form, the long-term behaviour of the process may have some stable, marginal distribution when integrated over time (long periods of time or just the recent past). In this study we propose a novel approach to introduce an attractor distribution in non-stationary state-space models. The attractor distribution models previously observed dynamics. Mean reversion is enforced through introducing pseudoobservations into the Kalman filter during forecasting. These pseudo-observations are samples of the attractor distribution mean. The result is that the filtering operation during forecasting naturally draws the forecasts towards the mean of the previously observed dynamics.The proposed approach can model both short and long-term dynamics and it allows for the selection of which state space components should be mean reverting. Furthermore, the approach is easily implemented using the standard Kalman filter and it has broad appeal as it addresses problems that are found in many domains other than aquaculture. . Sequential data assimilation with a nonlinear quasi-geostrophic model using monte carlo methods to forecast error statistics.

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Section: Water Quality Modellingmentioning

confidence: 99%

Enforcing mean reversion in state space models for prawn pond water quality forecasting

Dabrowski

Rahman

Pagendam

et al. 2020

Computers and Electronics in Agriculture

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“…The optimal neural network was used together with PCA to develop soft sensor in the application for an activated sludge process (ASP) of a large-scale municipal WWTP in simulation framework for prediction of COD, TSS and TN content [32]. Efficient on-line estimation of nutrient concentration can improve effluent quality measurements in WWTP without having to purchase expensive but sometimes unreliable instruments, thus an economical improvement of the plant performance.…”

Section: Neural Network 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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“…It is a major research focus in the field of dredging to develop a mud concentration measurement method with the article advantages of low-cost, simple maintenance, and high-precision. To solve the measurement problem of certain variables in industrial production, the development of soft sensors and the resulting soft sensor technology are an important direction for the research and development of detection and process control [10][11][12]. There are two most commonly used methods for building soft sensor models, namely, mechanism modelling and data-driven modelling.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger

Wang

Fan

Pan

et al. 2020

Sensors

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The dredger construction environment is harsh, and the mud concentration meter can be damaged from time to time. To ensure that the dredger can continue construction operations when the mud concentration meter is damaged, the development of a dredger with advantages of low price and simple operation that can be used in emergency situations is essential. The characteristic spare mud concentration meter is particularly critical. In this study, a data-driven soft sensor method is proposed that can predict the mud concentration in real time and can mitigate current marine mud concentration meter malfunctions, which affects continuous construction. This sensor can also replace the mud concentration meter when the construction is stable, thereby extending its service life. The method is applied to two actual construction cases, and the results show that the stacking generalization (SG) model has a good prediction effect in the two cases, and its goodness of fit R2 values are as high as 0.9774 and 0.9919, indicating that this method can successfully detect the mud concentration.

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Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network

Cited by 95 publications

References 34 publications

Enforcing mean reversion in state space models for prawn pond water quality forecasting

Enforcing mean reversion in state space models for prawn pond water quality forecasting

Physical and Soft Sensor Technologies for Wastewater Quality Management

A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger

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