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All Journal Green Engineering: Journal of Engineering and Applied Science
Krisna Widi Nugraha
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering

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AI driven Circular Waste to Energy Conversion System Using Smart Thermal Monitoring and Emission Optimization for Sustainable Urban Infrastructure Kiki Ahmad Baihaqi; Krisna Widi Nugraha; Rian Ardianto; Rosyid Ridlo Al-Hakim; Riza Phahlevi Marwanto; Erick Fernando
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.289

Abstract

This study explores the integration of Artificial Intelligence (AI) with thermal optimization in Waste-to-Energy (WtE) systems to enhance both energy recovery and emission control. Introduction: The growing need for sustainable urban waste management has highlighted the importance of optimizing WtE systems. AI technologies, including machine learning and deep learning, have shown potential in improving the efficiency of WtE processes, especially in reducing emissions and enhancing energy recovery. Literature Review: Previous research indicates that AI has been successfully applied to various WtE technologies such as pyrolysis, gasification, and incineration, yet the integration of AI specifically for thermal optimization remains underexplored. Most studies focus on predictive models for emission reduction rather than real time thermal optimization. Materials and Method: The study proposes the development of an AI-driven framework that integrates real time data collection from IoT sensors, predictive modeling, and real time control algorithms. The system optimizes key parameters such as combustion temperature and fuel flow to enhance energy recovery and minimize emissions. The method includes data collection from operational WtE plants, followed by model development using machine learning algorithms. Results and Discussion: Initial simulations and pilot testing showed significant improvements in energy efficiency and emission reduction. AI-driven systems outperformed conventional WtE systems by optimizing operational parameters in real time. The study identifies gaps in AI integration for thermal optimization and suggests future research directions, including the integration of AI with smart grids and carbon credit systems for more sustainable WtE operations.
Co-Authors Baihaqi, Kiki Ahmad Erick Fernando Rian Ardianto Riza Phahlevi Marwanto Rosyid Ridlo Al-Hakim