Effects of metal salt addition on odor and process stability during the anaerobic digestion of municipal waste sludge

2015 ◽  
Vol 46 ◽  
pp. 449-458 ◽  
Author(s):  
Timothy Abbott ◽  
Cigdem Eskicioglu
Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 158
Author(s):  
Ain Cheon ◽  
Jwakyung Sung ◽  
Hangbae Jun ◽  
Heewon Jang ◽  
Minji Kim ◽  
...  

The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational parameters. Five ML models, which included tree-, regression-, and neural network-based algorithms, were applied to predict the methane yield in BEAD reactor. The results showed that various 1-step ahead ML models, which utilized prior data of BEAD performances, could enhance prediction accuracy. In addition, 1-step ahead with retraining algorithm could improve prediction accuracy by 37.3% compared with the conventional multi-step ahead algorithm. The improvement was particularly noteworthy in tree- and regression-based ML models. Moreover, 1-step ahead with retraining algorithm showed high potential of achieving efficient prediction using pH as a single input data, which is plausibly an easier monitoring parameter compared with the other parameters required in bioprocess models.


2020 ◽  
Vol 54 ◽  
pp. 72-84 ◽  
Author(s):  
John Ryue ◽  
Long Lin ◽  
Farokh Laqa Kakar ◽  
Elsayed Elbeshbishy ◽  
Abdullah Al-Mamun ◽  
...  

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