Water quality detection based on a data mining process on the California estuary

Castillo, Edwin; Corrales, David Camilo; Lasso, Emmanuel; Ledezma, Agapito; Corrales, Juan Carlos

doi:10.1504/ijbidm.2017.086987

Cited by 1 publication

(1 citation statement)

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“…These systems can predict coagulant dosages by learning from a historical base with adequate WTP data [21,22]. These data need to be selected so that the system does not learn from data that are related to undesirable operational situations [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Clustering Techniques for the Reduction of Chemicals in Water Treatment Plants

Librantz

Santos

2023

Sustainability

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Currently, the use of intelligent models for decision making in the water treatment process is very important, as many plants support their implementation with the aim of obtaining economic, social, and environmental gains. Nevertheless, for these systems to be properly modeled, the data should be carefully selected so that only those that represent good operating practices are used. Thus, this study proposes an approach for identifying water quality and operational scenarios using the expectation maximisation (EM) and self-organising maps (SOMs) techniques when using data from a water treatment plant. The results showed that both techniques were able to identify quantities of different scenarios, some similar and others different, allowing for the evaluation of differences in a robust way. The EM technique resulted in fewer scenarios when compared with the SOMs technique, including in the cluster selection process. The results also indicated that an intelligent model can be trained with data from the proposed clustering, which improves its prediction capacity under different operating conditions; this can lead to savings in chemical product usage and less waste generation throughout the water treatment process, which is in good agreement with cleaner production practices.

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