A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

Obafemi O. Olatunji; Stephen Akinlabi; Nkosinathi Madushele; Paul A. Adedeji

doi:10.1007/978-981-15-4745-4_95

A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

University of Johannesburg

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

5 Citations (Scopus)

Abstract

The elemental composition of biomass is a significant property, which determines the energy content of biomass feedstock. This article develops a prediction model based on a hybrid adaptive neuro-fuzzy inference system (ANFIS) optimized with genetic algorithm (GA). The model inputs were the proximate constituents of biomass which are ash, fixed carbon, and volatile matter. These were used to predict the hydrogen (H), oxygen (O) and carbon (C) content of biomass fuels. The proposed algorithm was evaluated based on some known performance metrics. The root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of correlation (CC), mean absolute error (MAE) are 3.673, 2.4609, 5.1757, 0.9464, 0.309 at computation time (CT) of 33.65 secs for carbon (C); 0.6293, 0.4168, 8.3011, 0.75581, 0.0716 at CT of 40.21 secs for hydrogen (H); 4.4538, 3.1042, 13.3983,0.9167, 0.9899 at CT of 33.57 secs for oxygen (O), respectively. Regression analysis was also carried out to determine the level of dependence among the correlated variables. The model performance shows that GA-ANFIS can be applied in the computation of the elemental composition of biomass for strategic decision-making.

Original language	English
Title of host publication	Trends in Manufacturing and Engineering Management - Select Proceedings of ICMechD 2019
Editors	S. Vijayan, Nachiappan Subramanian, K. Sankaranarayanasamy
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	1099-1114
Number of pages	16
ISBN (Print)	9789811547447
DOIs	https://doi.org/10.1007/978-981-15-4745-4_95
Publication status	Published - 2021
Event	2nd International Conference on Mechanical Engineering Design, ICMechD 2019 - Chennai, India Duration: 25 Apr 2019 → 26 Apr 2019

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	2nd International Conference on Mechanical Engineering Design, ICMechD 2019
Country/Territory	India
City	Chennai
Period	25/04/19 → 26/04/19

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Biomass feedstock
Efficient utilization
Elemental composition
GA-ANFIS

ASJC Scopus subject areas

Automotive Engineering
Aerospace Engineering
Mechanical Engineering
Fluid Flow and Transfer Processes

Access to Document

10.1007/978-981-15-4745-4_95

Cite this

Olatunji, O. O., Akinlabi, S., Madushele, N., & Adedeji, P. A. (2021). A GA-ANFIS Model for the Prediction of Biomass Elemental Properties. In S. Vijayan, N. Subramanian, & K. Sankaranarayanasamy (Eds.), Trends in Manufacturing and Engineering Management - Select Proceedings of ICMechD 2019 (pp. 1099-1114). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-4745-4_95

Olatunji, Obafemi O. ; Akinlabi, Stephen ; Madushele, Nkosinathi et al. / A GA-ANFIS Model for the Prediction of Biomass Elemental Properties. Trends in Manufacturing and Engineering Management - Select Proceedings of ICMechD 2019. editor / S. Vijayan ; Nachiappan Subramanian ; K. Sankaranarayanasamy. Springer Science and Business Media Deutschland GmbH, 2021. pp. 1099-1114 (Lecture Notes in Mechanical Engineering).

@inproceedings{7793c1da34064408979ec7e3655f6818,

title = "A GA-ANFIS Model for the Prediction of Biomass Elemental Properties",

abstract = "The elemental composition of biomass is a significant property, which determines the energy content of biomass feedstock. This article develops a prediction model based on a hybrid adaptive neuro-fuzzy inference system (ANFIS) optimized with genetic algorithm (GA). The model inputs were the proximate constituents of biomass which are ash, fixed carbon, and volatile matter. These were used to predict the hydrogen (H), oxygen (O) and carbon (C) content of biomass fuels. The proposed algorithm was evaluated based on some known performance metrics. The root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of correlation (CC), mean absolute error (MAE) are 3.673, 2.4609, 5.1757, 0.9464, 0.309 at computation time (CT) of 33.65 secs for carbon (C); 0.6293, 0.4168, 8.3011, 0.75581, 0.0716 at CT of 40.21 secs for hydrogen (H); 4.4538, 3.1042, 13.3983,0.9167, 0.9899 at CT of 33.57 secs for oxygen (O), respectively. Regression analysis was also carried out to determine the level of dependence among the correlated variables. The model performance shows that GA-ANFIS can be applied in the computation of the elemental composition of biomass for strategic decision-making.",

keywords = "Biomass feedstock, Efficient utilization, Elemental composition, GA-ANFIS",

author = "Olatunji, \{Obafemi O.\} and Stephen Akinlabi and Nkosinathi Madushele and Adedeji, \{Paul A.\}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Singapore Pte Ltd.; 2nd International Conference on Mechanical Engineering Design, ICMechD 2019 ; Conference date: 25-04-2019 Through 26-04-2019",

year = "2021",

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language = "English",

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series = "Lecture Notes in Mechanical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

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}

Olatunji, OO, Akinlabi, S , Madushele, N & Adedeji, PA 2021, A GA-ANFIS Model for the Prediction of Biomass Elemental Properties. in S Vijayan, N Subramanian & K Sankaranarayanasamy (eds), Trends in Manufacturing and Engineering Management - Select Proceedings of ICMechD 2019. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 1099-1114, 2nd International Conference on Mechanical Engineering Design, ICMechD 2019, Chennai, India, 25/04/19. https://doi.org/10.1007/978-981-15-4745-4_95

A GA-ANFIS Model for the Prediction of Biomass Elemental Properties. / Olatunji, Obafemi O.; Akinlabi, Stephen ; Madushele, Nkosinathi et al.
Trends in Manufacturing and Engineering Management - Select Proceedings of ICMechD 2019. ed. / S. Vijayan; Nachiappan Subramanian; K. Sankaranarayanasamy. Springer Science and Business Media Deutschland GmbH, 2021. p. 1099-1114 (Lecture Notes in Mechanical Engineering).

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

TY - GEN

T1 - A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

AU - Olatunji, Obafemi O.

AU - Akinlabi, Stephen

AU - Madushele, Nkosinathi

AU - Adedeji, Paul A.

PY - 2021

Y1 - 2021

N2 - The elemental composition of biomass is a significant property, which determines the energy content of biomass feedstock. This article develops a prediction model based on a hybrid adaptive neuro-fuzzy inference system (ANFIS) optimized with genetic algorithm (GA). The model inputs were the proximate constituents of biomass which are ash, fixed carbon, and volatile matter. These were used to predict the hydrogen (H), oxygen (O) and carbon (C) content of biomass fuels. The proposed algorithm was evaluated based on some known performance metrics. The root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of correlation (CC), mean absolute error (MAE) are 3.673, 2.4609, 5.1757, 0.9464, 0.309 at computation time (CT) of 33.65 secs for carbon (C); 0.6293, 0.4168, 8.3011, 0.75581, 0.0716 at CT of 40.21 secs for hydrogen (H); 4.4538, 3.1042, 13.3983,0.9167, 0.9899 at CT of 33.57 secs for oxygen (O), respectively. Regression analysis was also carried out to determine the level of dependence among the correlated variables. The model performance shows that GA-ANFIS can be applied in the computation of the elemental composition of biomass for strategic decision-making.

AB - The elemental composition of biomass is a significant property, which determines the energy content of biomass feedstock. This article develops a prediction model based on a hybrid adaptive neuro-fuzzy inference system (ANFIS) optimized with genetic algorithm (GA). The model inputs were the proximate constituents of biomass which are ash, fixed carbon, and volatile matter. These were used to predict the hydrogen (H), oxygen (O) and carbon (C) content of biomass fuels. The proposed algorithm was evaluated based on some known performance metrics. The root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of correlation (CC), mean absolute error (MAE) are 3.673, 2.4609, 5.1757, 0.9464, 0.309 at computation time (CT) of 33.65 secs for carbon (C); 0.6293, 0.4168, 8.3011, 0.75581, 0.0716 at CT of 40.21 secs for hydrogen (H); 4.4538, 3.1042, 13.3983,0.9167, 0.9899 at CT of 33.57 secs for oxygen (O), respectively. Regression analysis was also carried out to determine the level of dependence among the correlated variables. The model performance shows that GA-ANFIS can be applied in the computation of the elemental composition of biomass for strategic decision-making.

KW - Biomass feedstock

KW - Efficient utilization

KW - Elemental composition

KW - GA-ANFIS

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M3 - Conference contribution

AN - SCOPUS:85090536252

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BT - Trends in Manufacturing and Engineering Management - Select Proceedings of ICMechD 2019

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Olatunji OO, Akinlabi S , Madushele N , Adedeji PA. A GA-ANFIS Model for the Prediction of Biomass Elemental Properties. In Vijayan S, Subramanian N, Sankaranarayanasamy K, editors, Trends in Manufacturing and Engineering Management - Select Proceedings of ICMechD 2019. Springer Science and Business Media Deutschland GmbH. 2021. p. 1099-1114. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-981-15-4745-4_95

A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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