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Prediction of anaerobic digestion performance and identification of critical operational parameters using machine learning algorithms.
Bioresour Technol ; 298: 122495, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31830658
ABSTRACT
Machine learning has emerges as a novel method for model development and has potential to be used to predict and control the performance of anaerobic digesters. In this study, several machine learning algorithms were applied in regression and classification models on digestion performance to identify determinant operational parameters and predict methane production. In the regression models, k-nearest neighbors (KNN) algorithm demonstrates optimal prediction accuracy (root mean square error = 26.6, with the dataset range of 259.0-573.8), after narrowing prediction coverage by excluding extreme outliers from the validation set. In the classification models, logistic regression multiclass algorithm yields the best prediction accuracy of 0.73. Feature importance reveals that total carbon was the determinant operational parameter. These results demonstrate the great potential of using machine learning algorithms to predict anaerobic digestion performance.
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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Algoritmos / Aprendizado de Máquina Aspecto clínico: Predição / Prognóstico Idioma: Inglês Revista: Bioresour Technol Assunto da revista: Engenharia Biomédica Ano de publicação: 2020 Tipo de documento: Artigo País de afiliação: Estados Unidos