Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
@article{210677,
author = {Anha J. Pathan and Rohini. S.Hore and Snehal P. Shewale and Chinmayi B. Pendase and Amruta M. Khodave and Prajakta P. Mali},
title = {AI Enabled Smart Waste-to-Energy and Carbon Recovery System},
journal = {International Journal of Innovative Research in Technology},
year = {2026},
volume = {13},
number = {6},
pages = {1897-1902},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=210677},
abstract = {As plastic and other combustible waste continues to increase, methods to effectively dispose of it must be found that reduce environmental impact and extract useful materials. Conventional disposal methods result in wasted energy and uncontrolled burning can lead to harmful emissions. This project details the design and development of an AI enabled waste-to-energy and carbon capture prototype. The prototype converts heat energy from burning waste into electrical energy whilst monitoring key parameters throughout the process.
The prototype consists of a combustion chamber, exhaust system, TEG and heat sink, filter, and a Raspberry Pi 3 Model B+ for monitoring. The filter is made with stainless steel mesh and HEPA filters. Throughout the operation temperature and other sensor data will be recorded. A python programme will be used with the linear regression model to analyse the data. This will help understand how the conditions affect energy capture and provide a basis for more intelligent waste treatments. This experiment combines heat energy capturing, monitoring and data analysis. The results of the experiment must be analysed based on the temperature, voltage, filtering and operation data. More testing needs to be done to see how much energy is being produced and how effective it is at controlling pollution. It will also determine if it is viable for carbon capture.},
keywords = {Waste to Energy, Thermoelectric Generator, Raspberry Pi, Artificial Intelligence, Linear Regression, Heat Recovery, Waste Management.},
month = {November},
}
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