Early prediction of Biochemical Methane Potential through statistical and kinetic modelling of initial gas production.
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4912998
- author
- Strömberg, Sten LU ; Nistor, Mihaela and Liu, Jing LU
- organization
- publishing date
- 2015
- 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
- pmid:25461008
- 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
- 2016-04-01 10:15:44
- date last changed
- 2022-03-04 17:50:38
@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}}, doi = {{10.1016/j.biortech.2014.11.033}}, volume = {{176}}, year = {{2015}}, }