Artificial Intelligence-Driven Optimization and Decision Support for Integrated Waste-to-Energy Systems in Climate-Vulnerable Megacities: A Case Study of Dhaka, Bangladesh

Abstract

The nexus of waste management and energy insecurity poses a critical challenge for climate-vulnerable megacities undergoing rapid urbanization. Dhaka, Bangladesh, A city marked by escalating solid waste generation, inadequate source segregation, overburdened landfills, and an overreliance on fossil fuels, epitomizes this crisis. This research investigates the transformative potential of Artificial Intelligence (AI) in optimizing integrated Waste-to-Energy (WtE) systems as a strategic response to urban sustainability deficits. Leveraging a multidisciplinary framework, this paper evaluates the contextual suitability of key WtE technologies- incineration, gasification, pyrolysis, and anaerobic digestion- based on Dhaka’s high-moisture, organic-rich waste stream. This study presents a comprehensive architecture for AI-enhanced WtE operations encompassing predictive waste stream analytics, robotic sorting, dynamic process control, real-time emissions minimization, and intelligent decision support for urban planners. The synergistic integration of AI enables energy recovery from heterogeneous waste and enhances feedstock characterization, adaptive combustion tuning, and biogas optimization, thus significantly improving system efficiency and reducing lifecycle emissions. Case comparisons demonstrate that AI-based forecasting models achieve sub-1% error margins in waste volume prediction, outperforming conventional methods by over 85% in accuracy. Furthermore, the study underscores the broader implications of AI-driven WtE systems for climate resilience, circular economy integration, and energy security. Socioeconomic and governance barriers- such as the informal waste sector, policy fragmentation, and algorithmic bias- are critically examined, with targeted strategies proposed for ethical and equitable deployment. This work advocates a paradigm shift from linear disposal models to intelligent, regenerative urban metabolism. The proposed AI-WtE convergence offers a scalable, replicable blueprint for megacities globally to transition toward low-carbon, resource-efficient futures while reinforcing climate adaptation and public health resilience.