On real-time control and process monitoring of wastewater treatment plants: real-time process monitoring

Wade, Matthew J.; Sánchez, Antonio F. Rial; Katebi, M.R.

doi:10.1191/0142331205tm140oa

Cited by 15 publications

(11 citation statements)

References 8 publications

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“…Most of the works for real-time monitoring the pollutants in the wastewater are developed to monitor the WWTP parameters, as discussed in the following. Online monitoring of the WWTP using Supervisory Control And Data Acquisition (SCADA) application was developed in [15,16]. SCADA system provides real-time monitoring of the WWTP parameters, such as pH, TSS, DO, COD [15].…”

Section: Introductionmentioning

confidence: 99%

IoT Based Real-Time Monitoring of Phytoremediation of Wastewater using the Mathematical Model Implemented on the Embedded Systems

Hendriarianti¹,

Soetedjo²

2021

IJIES

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This paper presents the Internet of things (IoT) technology for real-time monitoring of wastewater phytoremediation. Phytoremediation is a technique to remove pollutants from the wastewater using the plants. A conventional method to monitor the phytoremediation performance is by taking the samples of the contaminants from a site and measuring them at the laboratory. This method needs many works for data preparation and analysis. A recent development on the IoT technology may eliminate such tasks by a real-time monitoring system. In the proposed realtime monitoring system, several phytoremediation models are implemented on embedded hardware and connected to the Thingspeak IoT platform. The proposed system aims to provide a real-time monitoring system to better model the phytoremediation by examining the monitoring data time interval and fitting techniques. From the experiments, the proposed monitoring system achieves a data transfer reliability of 81.4% when the period of the data transmission is one minute, which is suitable for one-day interval real-time monitoring system. The proposed monitoring system can build a phytoremediation model using a polynomial fitting with a higher fit than the existing methods using an exponential fitting. Further, the proposed method promises a better solution in terms of the best model, the low cost, and the acceptable accuracy.

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Section: Introductionmentioning

confidence: 99%

IoT Based Real-Time Monitoring of Phytoremediation of Wastewater using the Mathematical Model Implemented on the Embedded Systems

Hendriarianti¹,

Soetedjo²

2021

IJIES

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“…In a complicated and large plant, energy efficiency, performance, and reliability may be considered as control purposes (Ammous et al, 2016). However, in a small size plant, issues such as software platform for real-time control and monitoring, measurement noise, model mismatch, and actuator fault should be considered as control tasks (Jiang et al, 2017; Sánchez et al, 2005; Wade et al, 2005). In this regard, a robust fault-tolerant control (FTC) system is recommended to handle model mismatch and measurement noise.…”

Section: Introductionmentioning

confidence: 99%

Integrated model predictive fault-tolerant control, and fault detection based on the parity space approach for a reverse osmosis desalination unit

Mehrad

Kargar

2020

Transactions of the Institute of Measurement and Control

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Actuator faults are inevitable in small reverse osmosis desalination plants. It may cause energy losses and reduce the quality of the freshwater, which may endanger human life. This paper focuses on the integrated fault detection and fault-tolerant control approach. The primary motivation of this paper is to propose a novel integrated fault detection and fault-tolerant control approach. The actuator fault is estimated using the concept of parity space approach. Then the system model is updated in the fault-tolerant control block using the information of the estimated fault parameter. Moreover, the proposed approach uses the receding-horizon predictive control-bounded data uncertainties controller, which is the robust and stable variant of generalized predictive control. The remaining uncertainty caused by the model and observer is compensated by this controller. The structure of a small reverse osmosis desalination plant is deployed. In this plant, the permeate flow rate and conductivity are controlled by a retentate valve and a bypass valve, which add a small amount of inlet to the outlet. The performances of three predictive model controllers are evaluated, and a comparison is made between their computational costs, stability, and robustness. The plant is considered to be linear time-invariant and subject to model uncertainties, measurement noise, and actuator fault in the retentate valve as efficiency dropping. The results reveal the robustness of the proposed approach concerning noise and matched uncertainties as well as its accommodation to actuator fault up to 90%.

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“…Principal component analysis (PCA) (Jackson, 1991), the most commonly used MSPM method, projects the original data into a lower dimensional space while tracking the maximum variance and hence splitting the original space into an approximated principal subspace and a residual subspace. For fault detection, PCA conventionally incorporates two control charts for the analysis, the Hotelling’s T 2 and the Squared Prediction Error (SPE), calculated based on an off-line PCA reference model for each new observation in on-line monitoring (Guo et al, 2018; Venkatasubramanian et al, 2003; Wade et al, 2005). The Hotelling’s T 2 represents the approximate model that indicates how far each sample is from the centre of the model.…”

Section: Introductionmentioning

confidence: 99%

Online monitoring scheme using principal component analysis through Kullback-Leibler divergence analysis technique for fault detection

Bounoua

Benkara

Kouadri

et al. 2019

Transactions of the Institute of Measurement and Control

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Principal component analysis (PCA) is a common tool in the literature and widely used for process monitoring and fault detection. Traditional PCA is associated with the two well-known control charts, the Hotelling’s T2 and the squared prediction error (SPE), as monitoring statistics. This paper develops the use of new measures based on a distribution dissimilarity technique named Kullback-Leibler divergence (KLD) through PCA by measuring the difference between online estimated and offline reference density functions. For processes with PCA scores following a multivariate Gaussian distribution, KLD is computed on both principal and residual subspaces defined by PCA in a moving window to extract the local disparity information. The potentials of the proposed algorithm are afterwards demonstrated through an application on two well-known processes in chemical industries; the Tennessee Eastman process as a reference benchmark and three tank system as an experimental validation. The monitoring performance was compared to recent results from other multivariate statistical process monitoring (MSPM) techniques. The proposed method showed superior robustness and effectiveness recording the lowest average missed detection rate and false alarm rates in process fault detection.

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On real-time control and process monitoring of wastewater treatment plants: real-time process monitoring

Cited by 15 publications

References 8 publications

IoT Based Real-Time Monitoring of Phytoremediation of Wastewater using the Mathematical Model Implemented on the Embedded Systems

IoT Based Real-Time Monitoring of Phytoremediation of Wastewater using the Mathematical Model Implemented on the Embedded Systems

Integrated model predictive fault-tolerant control, and fault detection based on the parity space approach for a reverse osmosis desalination unit

Online monitoring scheme using principal component analysis through Kullback-Leibler divergence analysis technique for fault detection

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