Optimal Feedstock Blending for Energy Maximization and Emission Compliance at Yangon Waste-to-Energy Plant, Myanmar

Main Article Content

Wint Khant
Aung Myo Lwin

Abstract

The rapid urbanization and industrialization of Yangon led to an enormous increase in municipal solid waste (MSW) and industrial waste, necessitating sustainable waste management strategies. The Yangon Waste-to-Energy Plant (YWTEP) has become a necessity to minimize these waste streams into renewable energy without causing environmental pollution. The paper formulates a linear programming (LP) model for maximum blending of the MSW and industrial waste fractions to attain the highest energy recovery while remaining in compliance with Myanmar's emission standards. The model considers various categories of waste, i.e., plastics, paper, organic waste, wood, textiles, rubber, and metals, with varying calorific values and emission factors. The optimal waste composition was 28.85% plastic, 35% paper, 26.15% organic waste, and 10% wood, with an energy production of 18.95 MJ/kg and within emission constraints for NOx, SOx, and dust. Sensitivity analysis showed that variations in waste composition, e.g., due to variations in moisture as a function of season, could influence energy production and emissions, and adaptive strategies should be used. The findings highlight the importance of strategic waste blending for enhancing the efficiency and sustainability of WTE processes, providing real-world guidance for policymakers and plant operators alike. This research presents a fact-driven framework to improve waste-to-energy processes that can facilitate sustainable waste management and renewable energy supply in Yangon and other cities.


Manuscript received: 02 Dec 2025 | Revised: 01 Mac 2026 | Accepted: 08 Apr 2026 | Published: 31 Jul 2026

Article Details

How to Cite
Wint Khant, & Aung Myo Lwin. (2026). Optimal Feedstock Blending for Energy Maximization and Emission Compliance at Yangon Waste-to-Energy Plant, Myanmar. International Journal on Robotics, Automation and Sciences, 8(2), 1–7. https://doi.org/10.33093/ijoras.2026.8.2.1
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Article

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