Zero-Order Kinetic Modeling of Methane Emissions from Organic Waste: A Case Study of the Abidjan Landfill System

Abstract

The rapid urbanization and population growth of Abidjan, the economic capital of Côte d'Ivoire, have led to a significant increase in the generation of municipal solid waste (MSW), a large portion of which is biodegradable. Inefficient waste disposal in uncontrolled landfills results in uncontrolled methane (CH₄) emissions, a greenhouse gas with a global warming potential 28 times greater than that of carbon dioxide. Managing these methane emissions presents a major challenge for the environmental and energy sustainability of the city. This study proposes the application of a simplified kinetic model to predict the energy potential of waste in Abidjan, specifically focusing on the Kossihouen landfill. In this context, the term “simplified kinetic model” refers to a zero-order approach that assumes a constant methane production rate, independent of the substrate concentration, making it suitable for contexts where detailed waste degradation data is scarce. Due to the difficulty of obtaining experimental data in tropical contexts, the simplified model, which assumes a constant methane production rate independent of substrate concentration, was chosen as a practical and effective solution for estimating methane emissions. The results demonstrate that the waste from the Kossihouen landfill could generate an annual electrical output of 15.34 MW, sufficient to supply energy to 92,000 people in 2021. These empirical results were compared with theoretical estimates from other kinetic models, showing consistent trends but highlighting that theoretical maximum values cannot be fully achieved in practice due to recovery limitations.  Furthermore, this energy recovery leads to a reduction in greenhouse gas emissions. This approach provides local policymakers and energy planners with a valuable tool for assessing the feasibility of biogas recovery and sustainable waste management projects. Additionally, the study lays the foundation for future research on the adaptation of simplified models in other African contexts facing similar challenges.