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Early prediction of Biochemical Methane Potential through statistical and kinetic modelling of initial gas production.

Strömberg, Sten LU ; Nistor, Mihaela and Liu, Jing LU (2015) In Bioresource Technology 176. p.233-241
Abstract
A major drawback of Biochemical Methane Potential (BMP) tests is their long test duration, which could be reduced substantially if the final gas production could be predicted at an earlier stage. For this purpose, this study evaluates 61 different algorithms for their capability to predict the final BMP and required degradation time based on data from 138 BMP tests of various substrate types. By combining the best algorithms it was possible to predict the BMP with a relative root mean squared error (rRMSE) of less than 10% just 6days after initiation of the experiment. The results from this study indicate that there is a possibility to shorten the test length substantially by combining laboratory tests and intelligent prediction... (More)
A major drawback of Biochemical Methane Potential (BMP) tests is their long test duration, which could be reduced substantially if the final gas production could be predicted at an earlier stage. For this purpose, this study evaluates 61 different algorithms for their capability to predict the final BMP and required degradation time based on data from 138 BMP tests of various substrate types. By combining the best algorithms it was possible to predict the BMP with a relative root mean squared error (rRMSE) of less than 10% just 6days after initiation of the experiment. The results from this study indicate that there is a possibility to shorten the test length substantially by combining laboratory tests and intelligent prediction algorithms. Shorter test duration may widen the possible applications for BMP tests in full-scale biogas plants, allowing for a better selection and proper pricing of biomass. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Bioresource Technology
volume
176
pages
233 - 241
publisher
Elsevier
external identifiers
  • pmid:25461008
  • wos:000345982900032
  • scopus:84911977947
ISSN
1873-2976
DOI
10.1016/j.biortech.2014.11.033
language
English
LU publication?
yes
id
d08fa08e-cdeb-4d7f-a8d9-727e4c3596eb (old id 4912998)
date added to LUP
2015-01-12 17:17:37
date last changed
2017-07-23 03:11:28
@article{d08fa08e-cdeb-4d7f-a8d9-727e4c3596eb,
  abstract     = {A major drawback of Biochemical Methane Potential (BMP) tests is their long test duration, which could be reduced substantially if the final gas production could be predicted at an earlier stage. For this purpose, this study evaluates 61 different algorithms for their capability to predict the final BMP and required degradation time based on data from 138 BMP tests of various substrate types. By combining the best algorithms it was possible to predict the BMP with a relative root mean squared error (rRMSE) of less than 10% just 6days after initiation of the experiment. The results from this study indicate that there is a possibility to shorten the test length substantially by combining laboratory tests and intelligent prediction algorithms. Shorter test duration may widen the possible applications for BMP tests in full-scale biogas plants, allowing for a better selection and proper pricing of biomass.},
  author       = {Strömberg, Sten and Nistor, Mihaela and Liu, Jing},
  issn         = {1873-2976},
  language     = {eng},
  pages        = {233--241},
  publisher    = {Elsevier},
  series       = {Bioresource Technology},
  title        = {Early prediction of Biochemical Methane Potential through statistical and kinetic modelling of initial gas production.},
  url          = {http://dx.doi.org/10.1016/j.biortech.2014.11.033},
  volume       = {176},
  year         = {2015},
}