Título: | MACHINE LEARNING FOR FAILURE DETECTION IN BAKERY INDUSTRIAL EFFLUENTS TREATMENT BY ELECTROCOAGULATION | ||||||||||||
Autor: |
THIAGO DA SILVA RIBEIRO |
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Colaborador(es): |
MAURICIO LEONARDO TOREM - Orientador BRUNNO FERREIRA DOS SANTOS - Coorientador |
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Catalogação: | 19/OUT/2023 | Língua(s): | ENGLISH - UNITED STATES |
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Tipo: | TEXT | Subtipo: | THESIS | ||||||||||
Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
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Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=64369&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=64369&idi=2 |
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DOI: | https://doi.org/10.17771/PUCRio.acad.64369 | ||||||||||||
Resumo: | |||||||||||||
Electrocoagulation is an emerging wastewater treatment method that
combines the benefits of coagulation, flotation, and electrochemistry. As a result of
the inherent complexity of processes associated with wastewater treatment plants,
it is difficult to respond swiftly and correctly to the dynamic circumstances that are
necessary to ensure effluent quality. Therefore, this thesis aims to identify the
operational condition of a wastewater treatment plant that has adopted
electrocoagulation for treating bakery wastewater. Three operational conditions
based on effluent clarification and reaction sludge were the target variables. The
thesis is divided into two essays. The first endeavor used seven feature selection
methods to select the most important features in a given dataset. The performance
of neural network classification models trained on the original feature set was
compared to the performance of those that were trained on a subset of features that
had been curated using feature selection techniques. The model that utilised feature
selection was found to have the best performance (F1-score = 0.92) and an
improvement of more than 30 percent in preventing false positives. The second
contribution brought a model that could detect anomalous process behavior using
only wastewater surface color images from two small-size camera modules. The
performance of various methods, including MLP, LSTM, SVM, and XGBoost was
assessed. The LSTM model outperformed the others in terms of macro average
Precision (84.620 percent), Recall (84.531 percent), and F1-score (84.499 percent), but the XGBoost
model comes closely in second with Precision (83.922 percent), Recall (82.272 percent), and
F1-score (83.005 percent).
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