Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester

Modestus O. Okwu; Lagouge K. Tartibu; Olusegun D. Samuel; Henry O. Omoregbee; Anna E. Ivbanikaro

doi:10.1007/978-3-030-85030-2_17

Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester

Modestus O. Okwu
, Lagouge K. Tartibu
, Olusegun D. Samuel
, Henry O. Omoregbee
, Anna E. Ivbanikaro

Mechanical and Industrial Engineering Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

This study indicates the modelling and optimization of biogas production on assorted substrates of poultry wastes (PW) and cow dung using RSM and ANN. Three-layered ANN feedforward BP and RSM models were developed to estimate the yield of biogas produced via mixture of CD and PW droppings produced from a bio-digester system in the ratio 1:2. At the first run, maximum biogas yield of 51.3% was achieved with 38:23 CD/PW within the retention time of 9 days. The results showed that the coefficient of determination (R²) of the RSM and ANN models were 0.9998 and 1.0. The root-mean-square-error (RMSE) for best RSM and ANN were obtained at 0.0055 and 0.00022188. The study showed that ANN result seems marginally better than the RSM model. This is a confirmation that biomass could be harnessed in solving the current global energy crisis.

Original language	English
Title of host publication	Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings
Editors	Ignacio Rojas, Gonzalo Joya, Andreu Catala
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	202-215
Number of pages	14
ISBN (Print)	9783030850296
DOIs	https://doi.org/10.1007/978-3-030-85030-2_17
Publication status	Published - 2021
Event	16th International Work-Conference on Artificial Neural Networks, IWANN 2021 - Virtual, Online Duration: 16 Jun 2021 → 18 Jun 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12861 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Work-Conference on Artificial Neural Networks, IWANN 2021
City	Virtual, Online
Period	16/06/21 → 18/06/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

ANN
Anaerobic digestion
Biogas yield
Feedstock
RSM

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-85030-2_17

Cite this

Okwu, M. O., Tartibu, L. K., Samuel, O. D., Omoregbee, H. O., & Ivbanikaro, A. E. (2021). Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester. In I. Rojas, G. Joya, & A. Catala (Eds.), Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings (pp. 202-215). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12861 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85030-2_17

Okwu, Modestus O. ; Tartibu, Lagouge K. ; Samuel, Olusegun D. et al. / Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester. Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings. editor / Ignacio Rojas ; Gonzalo Joya ; Andreu Catala. Springer Science and Business Media Deutschland GmbH, 2021. pp. 202-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester",

abstract = "This study indicates the modelling and optimization of biogas production on assorted substrates of poultry wastes (PW) and cow dung using RSM and ANN. Three-layered ANN feedforward BP and RSM models were developed to estimate the yield of biogas produced via mixture of CD and PW droppings produced from a bio-digester system in the ratio 1:2. At the first run, maximum biogas yield of 51.3\% was achieved with 38:23 CD/PW within the retention time of 9 days. The results showed that the coefficient of determination (R2) of the RSM and ANN models were 0.9998 and 1.0. The root-mean-square-error (RMSE) for best RSM and ANN were obtained at 0.0055 and 0.00022188. The study showed that ANN result seems marginally better than the RSM model. This is a confirmation that biomass could be harnessed in solving the current global energy crisis.",

keywords = "ANN, Anaerobic digestion, Biogas yield, Feedstock, RSM",

author = "Okwu, \{Modestus O.\} and Tartibu, \{Lagouge K.\} and Samuel, \{Olusegun D.\} and Omoregbee, \{Henry O.\} and Ivbanikaro, \{Anna E.\}",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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}

Okwu, MO, Tartibu, LK, Samuel, OD, Omoregbee, HO & Ivbanikaro, AE 2021, Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester. in I Rojas, G Joya & A Catala (eds), Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12861 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 202-215, 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual, Online, 16/06/21. https://doi.org/10.1007/978-3-030-85030-2_17

Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester. / Okwu, Modestus O.; Tartibu, Lagouge K.; Samuel, Olusegun D. et al.
Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings. ed. / Ignacio Rojas; Gonzalo Joya; Andreu Catala. Springer Science and Business Media Deutschland GmbH, 2021. p. 202-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12861 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester

AU - Okwu, Modestus O.

AU - Tartibu, Lagouge K.

AU - Samuel, Olusegun D.

AU - Omoregbee, Henry O.

AU - Ivbanikaro, Anna E.

PY - 2021

Y1 - 2021

N2 - This study indicates the modelling and optimization of biogas production on assorted substrates of poultry wastes (PW) and cow dung using RSM and ANN. Three-layered ANN feedforward BP and RSM models were developed to estimate the yield of biogas produced via mixture of CD and PW droppings produced from a bio-digester system in the ratio 1:2. At the first run, maximum biogas yield of 51.3% was achieved with 38:23 CD/PW within the retention time of 9 days. The results showed that the coefficient of determination (R2) of the RSM and ANN models were 0.9998 and 1.0. The root-mean-square-error (RMSE) for best RSM and ANN were obtained at 0.0055 and 0.00022188. The study showed that ANN result seems marginally better than the RSM model. This is a confirmation that biomass could be harnessed in solving the current global energy crisis.

AB - This study indicates the modelling and optimization of biogas production on assorted substrates of poultry wastes (PW) and cow dung using RSM and ANN. Three-layered ANN feedforward BP and RSM models were developed to estimate the yield of biogas produced via mixture of CD and PW droppings produced from a bio-digester system in the ratio 1:2. At the first run, maximum biogas yield of 51.3% was achieved with 38:23 CD/PW within the retention time of 9 days. The results showed that the coefficient of determination (R2) of the RSM and ANN models were 0.9998 and 1.0. The root-mean-square-error (RMSE) for best RSM and ANN were obtained at 0.0055 and 0.00022188. The study showed that ANN result seems marginally better than the RSM model. This is a confirmation that biomass could be harnessed in solving the current global energy crisis.

KW - ANN

KW - Anaerobic digestion

KW - Biogas yield

KW - Feedstock

KW - RSM

UR - https://www.scopus.com/pages/publications/85115148056

U2 - 10.1007/978-3-030-85030-2_17

DO - 10.1007/978-3-030-85030-2_17

M3 - Conference contribution

AN - SCOPUS:85115148056

SN - 9783030850296

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 202

EP - 215

BT - Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings

A2 - Rojas, Ignacio

A2 - Joya, Gonzalo

A2 - Catala, Andreu

PB - Springer Science and Business Media Deutschland GmbH

T2 - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021

Y2 - 16 June 2021 through 18 June 2021

ER -

Okwu MO, Tartibu LK, Samuel OD, Omoregbee HO, Ivbanikaro AE. Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester. In Rojas I, Joya G, Catala A, editors, Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 202-215. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-85030-2_17

Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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