Work

Dr. Abdul Mateen ahmed, Mohammed Fardan Ahmed, Mohammed Rizwan

Year: 2025

Type: article

Abstract: The escalating global waste crisis necessitates innovative solutions for sustainable waste management and energy production. This study explores the integration of Artificial Intelligence (AI) in converting municipal solid waste into energy, aiming to optimize waste-to-energy (WTE) processes. Utilizing machine learning algorithms, we developed predictive models to assess waste composition and energy yield, enhancing the efficiency of WTE systems. The results demonstrate that AI-driven approaches can significantly improve energy recovery rates and operational efficiency in WTE facilities. This interdisciplinary approach offers a promising pathway toward sustainable waste management and energy production.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15668338

Author: Dr. Abdul Mateen ahmed, Mohammed Fardan Ahmed, Mohammed Rizwan

Language: English

Topic: Municipal Solid Waste Management

Subfield: Waste Management and Disposal

Field: Environmental Science

Domain: Physical Sciences

SDGs: Responsible consumption and production, Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W6893342622

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