Energy content modelling for municipal solid waste using adaptive neuro-fuzzy inference system (anfis)

Oluwatobi Adeleke; Stephen A. Akinlabi; Paul A. Adedeji; Tien Chien Jen

doi:10.1007/978-981-15-5753-8_17

Energy content modelling for municipal solid waste using adaptive neuro-fuzzy inference system (anfis)

University of Johannesburg
Walter Sisulu University

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

3 Citations (Scopus)

Abstract

Recovery of energy from municipal solid waste (MSW) will not only add to the national electrical energy generation capacity, but it will also minimize the quantity of waste that ends up in landfill, consequently mitigating its environmental impact. This study has developed ANFIS model to forecast the energy content of waste generated in Johannesburg, South Africa, based on the physical component of the waste: plastic, paper, organics, metals, and textile as input against the energy content. The fuzzy c-means (FCM) clustering technique was explored for data clustering in the ANFIS model. The model was trained with 70% of the data and 30% for validation. The performance of the network was evaluated using root mean square error (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The RMSE, MAD, and MAPE of the model were 0.3418, 0.2692, and 7.7991, respectively. The forecast accuracy of ANFIS was compared with ANN, giving a MAPE of 7.7991 and 13.7870, respectively. ANFIS gave a better forecast accuracy and recommended for energy content prediction of municipal solid waste.

Original language	English
Title of host publication	Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019
Editors	Seyed Sattar Emamian, Farazila Yusof, Mokhtar Awang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	177-185
Number of pages	9
ISBN (Print)	9789811557521
DOIs	https://doi.org/10.1007/978-981-15-5753-8_17
Publication status	Published - 2020
Event	5th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2019 - Kuala Lumpur, Malaysia Duration: 19 Nov 2019 → 21 Nov 2019

Publication series

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

Conference

Conference	5th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2019
Country/Territory	Malaysia
City	Kuala Lumpur
Period	19/11/19 → 21/11/19

UN SDGs

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

SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production

Keywords

Adaptive neuro-fuzzy inference system
Municipal solid waste
Season

ASJC Scopus subject areas

Automotive Engineering
Aerospace Engineering
Mechanical Engineering
Fluid Flow and Transfer Processes

Access to Document

10.1007/978-981-15-5753-8_17

Cite this

Adeleke, O., Akinlabi, S. A., Adedeji, P. A., & Jen, T. C. (2020). Energy content modelling for municipal solid waste using adaptive neuro-fuzzy inference system (anfis). In S. S. Emamian, F. Yusof, & M. Awang (Eds.), Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019 (pp. 177-185). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-5753-8_17

Adeleke, Oluwatobi ; Akinlabi, Stephen A. ; Adedeji, Paul A. et al. / Energy content modelling for municipal solid waste using adaptive neuro-fuzzy inference system (anfis). Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019. editor / Seyed Sattar Emamian ; Farazila Yusof ; Mokhtar Awang. Springer Science and Business Media Deutschland GmbH, 2020. pp. 177-185 (Lecture Notes in Mechanical Engineering).

@inproceedings{6cd3e963bedc4dd8a74e8cbea03de82f,

title = "Energy content modelling for municipal solid waste using adaptive neuro-fuzzy inference system (anfis)",

abstract = "Recovery of energy from municipal solid waste (MSW) will not only add to the national electrical energy generation capacity, but it will also minimize the quantity of waste that ends up in landfill, consequently mitigating its environmental impact. This study has developed ANFIS model to forecast the energy content of waste generated in Johannesburg, South Africa, based on the physical component of the waste: plastic, paper, organics, metals, and textile as input against the energy content. The fuzzy c-means (FCM) clustering technique was explored for data clustering in the ANFIS model. The model was trained with 70\% of the data and 30\% for validation. The performance of the network was evaluated using root mean square error (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The RMSE, MAD, and MAPE of the model were 0.3418, 0.2692, and 7.7991, respectively. The forecast accuracy of ANFIS was compared with ANN, giving a MAPE of 7.7991 and 13.7870, respectively. ANFIS gave a better forecast accuracy and recommended for energy content prediction of municipal solid waste.",

keywords = "Adaptive neuro-fuzzy inference system, Municipal solid waste, Season",

author = "Oluwatobi Adeleke and Akinlabi, \{Stephen A.\} and Adedeji, \{Paul A.\} and Jen, \{Tien Chien\}",

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Adeleke, O, Akinlabi, SA , Adedeji, PA & Jen, TC 2020, Energy content modelling for municipal solid waste using adaptive neuro-fuzzy inference system (anfis). in SS Emamian, F Yusof & M Awang (eds), Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 177-185, 5th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2019, Kuala Lumpur, Malaysia, 19/11/19. https://doi.org/10.1007/978-981-15-5753-8_17

Energy content modelling for municipal solid waste using adaptive neuro-fuzzy inference system (anfis). / Adeleke, Oluwatobi; Akinlabi, Stephen A.; Adedeji, Paul A. et al.
Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019. ed. / Seyed Sattar Emamian; Farazila Yusof; Mokhtar Awang. Springer Science and Business Media Deutschland GmbH, 2020. p. 177-185 (Lecture Notes in Mechanical Engineering).

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

TY - GEN

T1 - Energy content modelling for municipal solid waste using adaptive neuro-fuzzy inference system (anfis)

AU - Adeleke, Oluwatobi

AU - Akinlabi, Stephen A.

AU - Adedeji, Paul A.

AU - Jen, Tien Chien

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd 2020.

PY - 2020

Y1 - 2020

N2 - Recovery of energy from municipal solid waste (MSW) will not only add to the national electrical energy generation capacity, but it will also minimize the quantity of waste that ends up in landfill, consequently mitigating its environmental impact. This study has developed ANFIS model to forecast the energy content of waste generated in Johannesburg, South Africa, based on the physical component of the waste: plastic, paper, organics, metals, and textile as input against the energy content. The fuzzy c-means (FCM) clustering technique was explored for data clustering in the ANFIS model. The model was trained with 70% of the data and 30% for validation. The performance of the network was evaluated using root mean square error (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The RMSE, MAD, and MAPE of the model were 0.3418, 0.2692, and 7.7991, respectively. The forecast accuracy of ANFIS was compared with ANN, giving a MAPE of 7.7991 and 13.7870, respectively. ANFIS gave a better forecast accuracy and recommended for energy content prediction of municipal solid waste.

AB - Recovery of energy from municipal solid waste (MSW) will not only add to the national electrical energy generation capacity, but it will also minimize the quantity of waste that ends up in landfill, consequently mitigating its environmental impact. This study has developed ANFIS model to forecast the energy content of waste generated in Johannesburg, South Africa, based on the physical component of the waste: plastic, paper, organics, metals, and textile as input against the energy content. The fuzzy c-means (FCM) clustering technique was explored for data clustering in the ANFIS model. The model was trained with 70% of the data and 30% for validation. The performance of the network was evaluated using root mean square error (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The RMSE, MAD, and MAPE of the model were 0.3418, 0.2692, and 7.7991, respectively. The forecast accuracy of ANFIS was compared with ANN, giving a MAPE of 7.7991 and 13.7870, respectively. ANFIS gave a better forecast accuracy and recommended for energy content prediction of municipal solid waste.

KW - Adaptive neuro-fuzzy inference system

KW - Municipal solid waste

KW - Season

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DO - 10.1007/978-981-15-5753-8_17

M3 - Conference contribution

AN - SCOPUS:85091291672

SN - 9789811557521

T3 - Lecture Notes in Mechanical Engineering

SP - 177

EP - 185

BT - Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019

A2 - Emamian, Seyed Sattar

A2 - Yusof, Farazila

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PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2019

Y2 - 19 November 2019 through 21 November 2019

ER -

Adeleke O, Akinlabi SA , Adedeji PA , Jen TC. Energy content modelling for municipal solid waste using adaptive neuro-fuzzy inference system (anfis). In Emamian SS, Yusof F, Awang M, editors, Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019. Springer Science and Business Media Deutschland GmbH. 2020. p. 177-185. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-981-15-5753-8_17

Energy content modelling for municipal solid waste using adaptive neuro-fuzzy inference system (anfis)

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

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