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Model selection, identification and validation in anaerobic digestion: A review
Anaerobic digestion enables waste (water) treatment and energy production in the form of biogas. The successful implementation of this process has lead to an increasing interest worldwide. However, anaerobic digestion is a complex biological process, where hundreds of microbial populations are invol...
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Published in: | Water research (Oxford) 2011-11, Vol.45 (17), p.5347-5364 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Anaerobic digestion enables waste (water) treatment and energy production in the form of biogas. The successful implementation of this process has lead to an increasing interest worldwide. However, anaerobic digestion is a complex biological process, where hundreds of microbial populations are involved, and whose start-up and operation are delicate issues. In order to better understand the process dynamics and to optimize the operating conditions, the availability of dynamic models is of paramount importance. Such models have to be inferred from prior knowledge and experimental data collected from real plants. Modeling and parameter identification are vast subjects, offering a realm of approaches and methods, which can be difficult to fully understand by scientists and engineers dedicated to the plant operation and improvements. This review article discusses existing modeling frameworks and methodologies for parameter estimation and model validation in the field of anaerobic digestion processes. The point of view is pragmatic, intentionally focusing on simple but efficient methods.
► The study discusses methods for parameter estimation and model validation in anaerobic digestion. ► Several models of different complexity have been developed. ► Identification of its parameters keep being the most critical task. ► Sensitivity analyses (local and global) may play a key role helping to tackle with this issue. ► Covariance and confidence intervals enable obtaining an estimation of the parameters accuracy. |
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ISSN: | 0043-1354 1879-2448 |
DOI: | 10.1016/j.watres.2011.08.059 |