Nonlinear multivariate statistical process monitoring of a water treatment plant

Mendaci, Khaled; Ramdani, Messaoud; Benzaraa, T.

doi:10.1109/icmsao.2013.6552651

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Application On a Water Treatment Plant1

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Cited by 2 publications

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“…The factory that we studied is the purification of water in Oued El Othmania. It is responsible for the distribution of drinking water to many citizens at and around Constantine which is a city in northeast Algeria, it is also the third most populous city in the country [26]. It is known to have many important parameters such as turbidity, temperature, flow rate, and pH value.…”

Section: Application On a Water Treatment Plantmentioning

confidence: 99%

Multimode system condition monitoring using sparsity reconstruction for quality control

Bougheloum

Bekaik

Gherbi

2023

IJECE

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<p>In this paper, we introduce an improved multivariate statistical monitoring method based on the stacked sparse autoencoder (SSAE). Our contribution focuses on the choice of the SSAE model based on neural networks to solve diagnostic problems of complex systems. In order to monitor the process performance, the squared prediction error (SPE) chart is linked with nonparametric adaptive confidence bounds which arise from the kernel density estimation to minimize erroneous alerts. Then, faults are localized using two methods: contribution plots and sensor validity index (SVI). The results are obtained from experiments and real data from a drinkable water processing plant, demonstrating how the applied technique is performed. The simulation results of the SSAE model show a better ability to detect and identify sensor failures.</p>

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Section: Application On a Water Treatment Plantmentioning

confidence: 99%