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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


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 languageEnglish
Article number100548
JournalSustainable Computing: Informatics and Systems
Publication statusPublished - Jun 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Inc.


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.

FundersFunder number
Center of Istanbul Waste Management and City Cleaning
Turkish State Meteorological Service


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


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