Formal verification of wastewater treatment processes using events detected from continuous signals by means of artificial neural networks. Case study: SBR plant

Luccarini, L.; Bragadin, Gianni; Colombini, Gabriele; Mancini, Maurizio; Mello, Paola; Montali, Marco; Sottara, Davide

doi:10.1016/j.envsoft.2009.05.013

Cited by 43 publications

(29 citation statements)

References 39 publications

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“…Moreover, if the process is not monitored, nothing can ensure its effectiveness, i.e., the effluent is conforming to the required standards. In this regard, Luccarini et al [47] have proposed an ANN model for the intelligent management of biological treatment process. The model has been applied to the data collected from a SBR for municipal wastewater treatment, equipped with probes for on-line acquisition of signals such as pH, oxidation-reduction potential (ORP), and dissolved oxygen (DO).…”

Section: Ann Modeling Of Biological Water and Wastewater Treatment Prmentioning

confidence: 99%

Modeling of Biological Water and Wastewater Treatment Processes Using Artificial Neural Networks

Khataee¹,

Kasiri²

2011

CLEAN Soil Air Water

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A growing world population, unrelenting urbanization, increasing scarcity of good quality water resources, and rising fertilizer applications are the driving forces behind the accelerating upward trend in the use of efficient methods of water and wastewater treatment such as biological processes. Due to the complexity of the reactions in biological processes, a few studies have been performed involving the modeling of biological removal of water pollutants. Thus, the application of the artificial neural networks (ANNs) to predict the performance of the biological systems has been attempted. ANNs are computer-based systems that are designed to simulate the learning process of neurons in the human brain. One of the characteristics of modeling based on ANNs is that it does not require the mathematical description of the phenomena involved in the process. This review article describes the application of ANNs for modeling of biological water and wastewater treatment processes. Examples of early applications of ANNs in modeling and simulation of biological water and wastewater treatment processes in the presence of various microalgae, macroalgae, bacteria, microbes, yeasts, anaerobic sludge, aerated submerged biofilms, and submerged membrane bioreactors are reviewed.

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Section: Ann Modeling Of Biological Water and Wastewater Treatment Prmentioning

confidence: 99%

Modeling of Biological Water and Wastewater Treatment Processes Using Artificial Neural Networks

Khataee¹,

Kasiri²

2011

CLEAN Soil Air Water

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“…Besides, while ANN needed a large quantity of data, GM-ANN could handle smaller quantities. In another study, a real-time monitoring of wastewater treatment process was achieved with the aid of multivariate statistical methods and ANN (Luccarini et al, 2010). In that study, Luccarini et al installed some probes for the acquisition of signals such as pH, ORP and dissolved oxygen in a SBR and manipulated these data in an ANN model.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…In that study, Luccarini et al installed some probes for the acquisition of signals such as pH, ORP and dissolved oxygen in a SBR and manipulated these data in an ANN model. The research aimed to verify the treatment process based on the continuous signals obtained on-line from the reactor (Luccarini et al, 2010). The signals from the plant were transmitted by telecommunication facilities and the data were reduced using PCA method and then analyzed.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Regarding the maximum coefficient of correlation (R) and the minimum mean absolute percentage errors (MAPE) of the predictions, the developed model showed a satisfactory performance in comparison with the pure ANN models. Therefore, the model developed could be recommended in order to optimize design considerations of the treatment process (Pai et al, 2009 (Zhu et al, 1998), optimal control of a wastewater treatment process integrated with PCA (Choi and Park, 2001), Kohonen Self-Organizing Feature Maps (KSOFM) to analyze the process data of municipal wastewater treatment plant (Timothy Hong et al, 2003), Unsupervised networks for modeling the wastewater treatment process (Garcia and Gonzalez, 2004;Hong and Bhamidimarri, 2003;Cinar, 2005), Grey Model ANN (GM-ANN) to predict suspended solids (SS) and COD of hospital wastewater treatment reactor effluents (Pai, 2007), on-line monitoring of a reactor (Luccarini, 2010 (Chen, 2003), control and supervise the submerged biofilm wastewater treatment reactor , modeling the nonlinear relationships between the removal rate of pollutants and their chemical dosages in a paper mill wastewater treatment plant Table 5 summarizes the aforementioned models together with the advantages and drawbacks which might be considered for selection in applied projects and utilization in industrial scales. As observed the most extensively used model is ANN.…”

Section: Fig 4 Overview Of the Ga-ann Modelmentioning

confidence: 99%

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Influential Parameters on Biomethane Generation in Anaerobic Wastewater Treatment Plants

Tabatabaei¹,

Sulaiman²,

Nikbakht³

et al. 2011

Alternative Fuel

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“…Aguado et al [4] adopted artificial neural networks (ANN) to develop a methodology able to identify the optimal length of SBR phases, Luccarini et al [5] used feedforward ANN in order to detect the characteristic points of SBR cycle, such as nitrate knee, ammonia valley and ammonia breakpoint, visible in pH, ORP and DO profiles.…”

Section: Introductionmentioning

confidence: 99%

Monitoring denitrification by means of pH and ORP in continuous-flow conventional activated sludge processes

Luccarini

Pulcini

Sottara³

et al. 2017

DWt

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a b s t r a c tIndirect signal analysis (pH, ORP and DO) are often used in monitoring and control of SBRs (Sequencing Batch Reactors), where operating conditions can be clearly identified during the various cyclic phases. Only few studies applied this methodology to control continuous flow plants, as it is much more difficult to identify operating conditions because of continually variable inflow characteristics. This work applied indirect signal analysis to control pre-denitrification in continuous-flow activated sludge processes: (i) a laboratory-scale plant, fed with synthetic wastewater, simulating real municipal wastewater and (ii) a pilot-scale plant, fed with real sewage. Three different ranges of ORP values identify three operational conditions of the denitrification process. (1) ORP > 0 mV means that nitrates and/or nitrites are present, possibly due to a low C/N ratio. (2) -50 < ORP < -200 mV is typical of normal operating conditions, that is with a balanced C/N ratio. (3) ORP < -350 mV means that oxidized nitrogen load is too low or that C/N exceeds the stoichiometric ratio. The trend of pH, instead, points out if and how the process is evolving from one to another operating condition. The correlation between pH and ORP signals (as well as their derivatives) allows to restore normal operating conditions by acting on the internal recycle flow-rate. Improved denitrification process ensures lower effluent nitrate concentration, and reduce external carbon dosage to achieve stricter nitrogen limits.

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Formal verification of wastewater treatment processes using events detected from continuous signals by means of artificial neural networks. Case study: SBR plant

Cited by 43 publications

References 39 publications

Modeling of Biological Water and Wastewater Treatment Processes Using Artificial Neural Networks

Modeling of Biological Water and Wastewater Treatment Processes Using Artificial Neural Networks

Influential Parameters on Biomethane Generation in Anaerobic Wastewater Treatment Plants

Monitoring denitrification by means of pH and ORP in continuous-flow conventional activated sludge processes

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