Waste-to-Energy Framework: An intelligent energy recycling management

Kiymet Kaya; Elif Ak; Yusuf Yaslan; Sema Fatma Oktug

doi:10.1016/j.suscom.2021.100548

Waste-to-Energy Framework: An intelligent energy recycling management

Kiymet Kaya
, Elif Ak
, Yusuf Yaslan^*
, Sema Fatma Oktug

^*Corresponding author for this work

Istanbul Technical University

Research output: Contribution to journal › Article › peer-review

28 Citations (Scopus)

Abstract

Nowadays, waste to energy (WTE) transformation solutions play a vital role in waste disposal. Accurate WTE resource planning can be made using high-performance waste amount prediction models. Thus, a significant gain can be obtained both in economic and environmental terms. In this paper, we proposed different machine learning models to predict the amount of municipal solid waste (MSW) to be used for smart energy management systems. To point this problem, we study a new WTE Framework and use the real-world data set obtained from MSW stations on the European side of Istanbul, Turkey. The basis of our motivation for choosing Istanbul is based on the ‘Waste Incineration and Power Generation Plant,’ which was built in Eyupsultan, Istanbul in 2017 and is planned to be operational in 2021. This plant will be Europe's largest domestic waste incinerator with a capacity of 3000 tons/day. For the proposed WTE framework, we first build an ensemble model, Gradient Boosting (GB), to predict the amount of MSW using daily data related to other variables such as seasonality and socio-economic status. Then we use the calorific index value to predict generated energy from solid waste, categorized in 14 different waste types.

Original language	English
Article number	100548
Journal	Sustainable Computing: Informatics and Systems
Volume	30
DOIs	https://doi.org/10.1016/j.suscom.2021.100548
Publication status	Published - Jun 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Inc.

Funding

We are thankful to Center of Istanbul Waste Management and City Cleaning (ISTAC) and the Turkish State Meteorological Service for providing the waste and meteorological data used in this study. Furthermore, we would like to thank Dr. Burak Kantarci for his constructive comments.

Funders
Center of Istanbul Waste Management and City Cleaning
ISTAC
Turkish State Meteorological Service

UN SDGs

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

SDG 7 Affordable and Clean Energy
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production

Keywords

MSW
Machine learning
Smart city
Waste management
Waste-To-Energy

Access to Document

10.1016/j.suscom.2021.100548

Cite this

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title = "Waste-to-Energy Framework: An intelligent energy recycling management",

abstract = "Nowadays, waste to energy (WTE) transformation solutions play a vital role in waste disposal. Accurate WTE resource planning can be made using high-performance waste amount prediction models. Thus, a significant gain can be obtained both in economic and environmental terms. In this paper, we proposed different machine learning models to predict the amount of municipal solid waste (MSW) to be used for smart energy management systems. To point this problem, we study a new WTE Framework and use the real-world data set obtained from MSW stations on the European side of Istanbul, Turkey. The basis of our motivation for choosing Istanbul is based on the {\textquoteleft}Waste Incineration and Power Generation Plant,{\textquoteright} which was built in Eyupsultan, Istanbul in 2017 and is planned to be operational in 2021. This plant will be Europe's largest domestic waste incinerator with a capacity of 3000 tons/day. For the proposed WTE framework, we first build an ensemble model, Gradient Boosting (GB), to predict the amount of MSW using daily data related to other variables such as seasonality and socio-economic status. Then we use the calorific index value to predict generated energy from solid waste, categorized in 14 different waste types.",

keywords = "MSW, Machine learning, Smart city, Waste management, Waste-To-Energy",

author = "Kiymet Kaya and Elif Ak and Yusuf Yaslan and Oktug, \{Sema Fatma\}",

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year = "2021",

month = jun,

doi = "10.1016/j.suscom.2021.100548",

language = "English",

volume = "30",

journal = "Sustainable Computing: Informatics and Systems",

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T2 - An intelligent energy recycling management

AU - Kaya, Kiymet

AU - Ak, Elif

AU - Yaslan, Yusuf

AU - Oktug, Sema Fatma

PY - 2021/6

Y1 - 2021/6

N2 - Nowadays, waste to energy (WTE) transformation solutions play a vital role in waste disposal. Accurate WTE resource planning can be made using high-performance waste amount prediction models. Thus, a significant gain can be obtained both in economic and environmental terms. In this paper, we proposed different machine learning models to predict the amount of municipal solid waste (MSW) to be used for smart energy management systems. To point this problem, we study a new WTE Framework and use the real-world data set obtained from MSW stations on the European side of Istanbul, Turkey. The basis of our motivation for choosing Istanbul is based on the ‘Waste Incineration and Power Generation Plant,’ which was built in Eyupsultan, Istanbul in 2017 and is planned to be operational in 2021. This plant will be Europe's largest domestic waste incinerator with a capacity of 3000 tons/day. For the proposed WTE framework, we first build an ensemble model, Gradient Boosting (GB), to predict the amount of MSW using daily data related to other variables such as seasonality and socio-economic status. Then we use the calorific index value to predict generated energy from solid waste, categorized in 14 different waste types.

AB - Nowadays, waste to energy (WTE) transformation solutions play a vital role in waste disposal. Accurate WTE resource planning can be made using high-performance waste amount prediction models. Thus, a significant gain can be obtained both in economic and environmental terms. In this paper, we proposed different machine learning models to predict the amount of municipal solid waste (MSW) to be used for smart energy management systems. To point this problem, we study a new WTE Framework and use the real-world data set obtained from MSW stations on the European side of Istanbul, Turkey. The basis of our motivation for choosing Istanbul is based on the ‘Waste Incineration and Power Generation Plant,’ which was built in Eyupsultan, Istanbul in 2017 and is planned to be operational in 2021. This plant will be Europe's largest domestic waste incinerator with a capacity of 3000 tons/day. For the proposed WTE framework, we first build an ensemble model, Gradient Boosting (GB), to predict the amount of MSW using daily data related to other variables such as seasonality and socio-economic status. Then we use the calorific index value to predict generated energy from solid waste, categorized in 14 different waste types.

KW - MSW

KW - Machine learning

KW - Smart city

KW - Waste management

KW - Waste-To-Energy

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DO - 10.1016/j.suscom.2021.100548

M3 - Article

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SN - 2210-5379

VL - 30

JO - Sustainable Computing: Informatics and Systems

JF - Sustainable Computing: Informatics and Systems

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ER -

Waste-to-Energy Framework: An intelligent energy recycling management

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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