Influence of trace element supplementation on anaerobic digestion of chicken manure: Linking process stability to methanogenic population dynamics

2018 ◽  
Vol 181 ◽  
pp. 794-800 ◽  
Author(s):  
Rahim Molaey ◽  
Alper Bayrakdar ◽  
Recep Önder Sürmeli ◽  
Bariş Çalli
Keyword(s):  
Population Dynamics ◽  
Anaerobic Digestion ◽  
Trace Element ◽  
Chicken Manure ◽  
Process Stability

scholarly journals Anaerobic digestion of chicken manure: Influence of trace element supplementation

2018 ◽  
Author(s):  
Rahim Molaey ◽  
Alper Bayrakdar ◽  
Recep Önder Sürmeli ◽  
Bariş Çalli
Keyword(s):  
Anaerobic Digestion ◽  
Trace Element ◽  
Chicken Manure

Effects of different types of biochar on the anaerobic digestion of chicken manure

2019 ◽  
Vol 275 ◽  
pp. 258-265 ◽  
Author(s):  
Junting Pan ◽  
Junyi Ma ◽  
Xiaoxia Liu ◽  
Limei Zhai ◽  
Xihui Ouyang ◽  
...  
Keyword(s):  
Anaerobic Digestion ◽  
Chicken Manure ◽  
Different Types

scholarly journals Application of Various Machine Learning Models for Process Stability of Bio-Electrochemical Anaerobic Digestion

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 158
Author(s):  
Ain Cheon ◽  
Jwakyung Sung ◽  
Hangbae Jun ◽  
Heewon Jang ◽  
Minji Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anaerobic Digestion ◽  
Prediction Accuracy ◽  
Input Data ◽  
Process Stability ◽  
Operational Parameters ◽  
Monitoring Parameter ◽  
Efficient Prediction ◽  
Single Input ◽  
Machine Learning Models

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.

scholarly journals The feasibility of trace element supplementation for stable operation of wheat stillage-fed biogas tank reactors

2011 ◽  
Vol 64 (2) ◽  
pp. 320-325 ◽  
Author(s):  
J. Gustavsson ◽  
B. H. Svensson ◽  
A. Karlsson
Keyword(s):  
Trace Element ◽  
Hydraulic Retention Time ◽  
Loading Rate ◽  
Biogas Production ◽  
Organic Loading Rate ◽  
Stable Operation ◽  
Organic Loading ◽  
Process Stability ◽  
Volatile Solids ◽  
High Sulfate

The aim of this study was to investigate the effect of trace element supplementation on operation of wheat stillage-fed biogas tank reactors. The stillage used was a residue from bio-ethanol production, containing high levels of sulfate. In biogas production, high sulfate content has been associated with poor process stability in terms of low methane production and accumulation of process intermediates. However, the results of the present study show that this problem can be overcome by trace element supplementations. Four lab-scale wheat stillage-fed biogas tank reactors were operated for 345 days at a hydraulic retention time of 20 days (37 °C). It was concluded that daily supplementation with Co (0.5 mg L−1), Ni (0.2 mg L−1) and Fe (0.5 g L−1) were required for maintaining process stability at the organic loading rate of 4.0 g volatile solids L−1 day−1.

ADM1 based mathematical model of trace element precipitation/dissolution in anaerobic digestion processes

2018 ◽  
Vol 267 ◽  
pp. 666-676 ◽  
Author(s):  
Bikash Chandra Maharaj ◽  
Maria Rosaria Mattei ◽  
Luigi Frunzo ◽  
Eric D. van Hullebusch ◽  
Giovanni Esposito
Keyword(s):  
Mathematical Model ◽  
Anaerobic Digestion ◽  
Trace Element
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