A Simulation-Based and AI-Supported Analysis of Thermal Waste Treatment Technologies: A Simulation-Based and AI-Supported Analysis of Thermal Waste Treatment Technologies

Hatice Dilaver; Kamil Fatih Dilaver

doi:10.5281/zenodo.21390806

Authors

Hatice Dilaver Niğde Ömer Halis Demir Universitesi
Kamil Fatih Dilaver https://orcid.org/0000-0001-7557-9238

DOI:

https://doi.org/10.5281/zenodo.21390806

Keywords:

Municipal solid waste, Waste-to-energy, Incineration, Gasification, Machine learning, Sustainability

Abstract

The rapid increase in municipal solid waste (MSW) generation has intensified the need for sustainable waste management strategies capable of minimizing environmental impacts while maximizing resource recovery. Thermal waste treatment technologies, including incineration, pyrolysis, and gasification, have emerged as promising alternatives to conventional landfilling due to their potential for waste volume reduction and energy generation. This study presents a simulation-based and artificial intelligence (AI)-supported comparative assessment of major thermal treatment technologies. A systematic literature review covering studies published between 2010 and 2025 was conducted to evaluate environmental performance, energy recovery efficiency, and techno-economic characteristics. Furthermore, a conceptual machine learning framework was developed to analyze the relationships between operational parameters and system performance indicators. Results indicate that while incineration remains the most mature and commercially deployed technology, pyrolysis and gasification offer superior flexibility, lower emissions potential, and greater opportunities for resource recovery under optimized operating conditions. AI-based predictive and optimization models demonstrate considerable potential for enhancing process efficiency, reducing emissions, and supporting real-time operational decision-making. The findings suggest that integrating advanced thermal treatment technologies with AI-driven optimization can significantly contribute to sustainable waste-to-energy systems and circular economy objectives.

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A Simulation-Based and AI-Supported Analysis of Thermal Waste Treatment Technologies