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ETDs @PUC-Rio
Estatística
Título: MACHINE LEARNING FOR FAILURE DETECTION IN BAKERY INDUSTRIAL EFFLUENTS TREATMENT BY ELECTROCOAGULATION
Autor: THIAGO DA SILVA RIBEIRO
Colaborador(es): MAURICIO LEONARDO TOREM - Orientador
BRUNNO FERREIRA DOS SANTOS - Coorientador
Catalogação: 19/OUT/2023 Língua(s): ENGLISH - UNITED STATES
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.
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
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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