Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model

Bolette Dybkjær Hansen; Jamshid Tamouk; Christian A. Tidmarsh; Rasmus Johansen; Thomas B. Moeslund; David Getreuer Jensen

doi:10.1007/978-3-030-58799-4_53

Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model

Bolette Dybkjær Hansen, Jamshid Tamouk, Christian A. Tidmarsh, Rasmus Johansen, Thomas B. Moeslund, David Getreuer Jensen

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

5 Citationer (Scopus)

Abstract

Biogas production is a complicated process and mathematical modeling of the process is essential in order to plan the management of the plants. Gompertz models can predict the biogas production, but in co-digestion, where many feedstocks are used it can be hard to obtain a sufficient calibration, and often more research is required in order to find the exact calibration parameters. The scope of this article is to investigate if machine learning approaches can be used to optimize the predictions of Gompertz models. Increasing the precision of the models is important in order to get an optimal usage of the resources and thereby ensure a more sustainable energy production. Three models were tested: A Gompertz model (Mean Absolute Percentage Error (MAPE) = 9.61%), a machine learning model (MAPE = 4.84%), and a hybrid model (MAPE = 4.52%). The results showed that the hybrid model could decrease the error in the predictions with 53% when predicting the methane production one day ahead. When encountering an offset in the predictions the reduction of the error was increased to 66%.

Originalsprog	Engelsk
Titel	Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings
Redaktører	Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Chiara Garau, Ivan Blecic, David Taniar, Bernady O. Apduhan, Ana Maria A.C. Rocha, Eufemia Tarantino, Carmelo Maria Torre, Yeliz Karaca
Antal sider	12
Vol/bind	12249
Forlag	Springer
Publikationsdato	1 okt. 2020
Sider	734-745
ISBN (Trykt)	978-3-030-58798-7
ISBN (Elektronisk)	978-3-030-58799-4
DOI	https://doi.org/10.1007/978-3-030-58799-4_53
Status	Udgivet - 1 okt. 2020
Begivenhed	International Conference on Computational Science and its Applications - Saint Petersburg University, Saint Petersburg, Rusland Varighed: 1 jul. 2019 → 4 jul. 2019 Konferencens nummer: 19 http://www.iccsa.org

Konference

Konference	International Conference on Computational Science and its Applications
Nummer	19
Lokation	Saint Petersburg University
Land/Område	Rusland
By	Saint Petersburg
Periode	01/07/2019 → 04/07/2019
Internetadresse	http://www.iccsa.org

Emneord

Biogas
Prediction
Gompertz model
Machine Learning

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10.1007/978-3-030-58799-4_53

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Citationsformater

Hansen, B. D., Tamouk, J., Tidmarsh, C. A., Johansen, R., Moeslund, T. B., & Jensen, D. G. (2020). Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model. I O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blecic, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, C. M. Torre, & Y. Karaca (red.), Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings (Bind 12249, s. 734-745). Springer. https://doi.org/10.1007/978-3-030-58799-4_53

Hansen, Bolette Dybkjær ; Tamouk, Jamshid ; Tidmarsh, Christian A. et al. / Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model. Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. red. / Osvaldo Gervasi ; Beniamino Murgante ; Sanjay Misra ; Chiara Garau ; Ivan Blecic ; David Taniar ; Bernady O. Apduhan ; Ana Maria A.C. Rocha ; Eufemia Tarantino ; Carmelo Maria Torre ; Yeliz Karaca. Bind 12249 Springer, 2020. s. 734-745

@inproceedings{326c191f9328447ab28abc3ec4e7de78,

title = "Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model",

abstract = "Biogas production is a complicated process and mathematical modeling of the process is essential in order to plan the management of the plants. Gompertz models can predict the biogas production, but in co-digestion, where many feedstocks are used it can be hard to obtain a sufficient calibration, and often more research is required in order to find the exact calibration parameters. The scope of this article is to investigate if machine learning approaches can be used to optimize the predictions of Gompertz models. Increasing the precision of the models is important in order to get an optimal usage of the resources and thereby ensure a more sustainable energy production. Three models were tested: A Gompertz model (Mean Absolute Percentage Error (MAPE) = 9.61%), a machine learning model (MAPE = 4.84%), and a hybrid model (MAPE = 4.52%). The results showed that the hybrid model could decrease the error in the predictions with 53% when predicting the methane production one day ahead. When encountering an offset in the predictions the reduction of the error was increased to 66%.",

keywords = "Biogas, Prediction, Gompertz model, Machine Learning, Biogas, Predicition, Gompertz model, Machine learning",

author = "Hansen, {Bolette Dybkj{\ae}r} and Jamshid Tamouk and Tidmarsh, {Christian A.} and Rasmus Johansen and Moeslund, {Thomas B.} and Jensen, {David Getreuer}",

year = "2020",

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booktitle = "Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings",

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Hansen, BD, Tamouk, J, Tidmarsh, CA, Johansen, R, Moeslund, TB & Jensen, DG 2020, Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model. i O Gervasi, B Murgante, S Misra, C Garau, I Blecic, D Taniar, BO Apduhan, AMAC Rocha, E Tarantino, CM Torre & Y Karaca (red), Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. bind 12249, Springer, s. 734-745, International Conference on Computational Science and its Applications, Saint Petersburg, Rusland, 01/07/2019. https://doi.org/10.1007/978-3-030-58799-4_53

Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model. / Hansen, Bolette Dybkjær; Tamouk, Jamshid; Tidmarsh, Christian A. et al.
Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. red. / Osvaldo Gervasi; Beniamino Murgante; Sanjay Misra; Chiara Garau; Ivan Blecic; David Taniar; Bernady O. Apduhan; Ana Maria A.C. Rocha; Eufemia Tarantino; Carmelo Maria Torre; Yeliz Karaca. Bind 12249 Springer, 2020. s. 734-745.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model

AU - Hansen, Bolette Dybkjær

AU - Tamouk, Jamshid

AU - Tidmarsh, Christian A.

AU - Johansen, Rasmus

AU - Moeslund, Thomas B.

AU - Jensen, David Getreuer

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PY - 2020/10/1

Y1 - 2020/10/1

N2 - Biogas production is a complicated process and mathematical modeling of the process is essential in order to plan the management of the plants. Gompertz models can predict the biogas production, but in co-digestion, where many feedstocks are used it can be hard to obtain a sufficient calibration, and often more research is required in order to find the exact calibration parameters. The scope of this article is to investigate if machine learning approaches can be used to optimize the predictions of Gompertz models. Increasing the precision of the models is important in order to get an optimal usage of the resources and thereby ensure a more sustainable energy production. Three models were tested: A Gompertz model (Mean Absolute Percentage Error (MAPE) = 9.61%), a machine learning model (MAPE = 4.84%), and a hybrid model (MAPE = 4.52%). The results showed that the hybrid model could decrease the error in the predictions with 53% when predicting the methane production one day ahead. When encountering an offset in the predictions the reduction of the error was increased to 66%.

AB - Biogas production is a complicated process and mathematical modeling of the process is essential in order to plan the management of the plants. Gompertz models can predict the biogas production, but in co-digestion, where many feedstocks are used it can be hard to obtain a sufficient calibration, and often more research is required in order to find the exact calibration parameters. The scope of this article is to investigate if machine learning approaches can be used to optimize the predictions of Gompertz models. Increasing the precision of the models is important in order to get an optimal usage of the resources and thereby ensure a more sustainable energy production. Three models were tested: A Gompertz model (Mean Absolute Percentage Error (MAPE) = 9.61%), a machine learning model (MAPE = 4.84%), and a hybrid model (MAPE = 4.52%). The results showed that the hybrid model could decrease the error in the predictions with 53% when predicting the methane production one day ahead. When encountering an offset in the predictions the reduction of the error was increased to 66%.

KW - Biogas

KW - Prediction

KW - Gompertz model

KW - Machine Learning

KW - Biogas

KW - Predicition

KW - Gompertz model

KW - Machine learning

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Hansen BD, Tamouk J, Tidmarsh CA, Johansen R, Moeslund TB, Jensen DG. Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model. I Gervasi O, Murgante B, Misra S, Garau C, Blecic I, Taniar D, Apduhan BO, Rocha AMAC, Tarantino E, Torre CM, Karaca Y, red., Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. Bind 12249. Springer. 2020. s. 734-745 doi: 10.1007/978-3-030-58799-4_53

Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model

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