Enhanced Anaerobic Degradation and Modeling of Raw and Treated Municipal Solid Waste Publisher Pubmed



Siddiqui Y S ; Siddiqui A A ; Ayub S ; Changani F ; Dehghani M H
Source: Scientific Reports Published:2026

Abstract

Conventional landfills suffer from slow waste stabilization, resulting in delayed methane recovery and persistent environmental pollution. This study addresses this challenge by investigating the comparative biogas generation potential and stabilization kinetics of raw and treated municipal solid waste (MSW) under anaerobic degradation in lab scale sealed bioreactors over a 300-day period, while integrating predictive modeling of biogas and chemical oxygen demand (COD) dynamics as an innovative analytical approach. Key parameters, including pH, volatile fatty acids (VFAs), chemical oxygen demand (COD), ammonia-nitrogen (NH3-N), electrical conductivity (EC) and heavy metal concentrations, were monitored to evaluate system performance. Treated MSW (windrow compost) exhibited early methanogenesis within one week, achieving a methane concentration of about 66%, whereas raw waste required nearly five weeks due to higher VFA buildup and acidic conditions that inhibited microbial activity. Cumulative biogas production stabilized at 68 L for treated and 365 L for raw waste, with methane content reaching 70% in the latter. Treated waste showed faster pH stabilization (above 6.5), sharper COD reduction of about 98%, and enhanced microbial efficiency compared to raw waste. Elevated NH3-N and heavy metal levels (Zn, Cu, Ni) in raw MSW declined progressively with digestion. A novel predictive modeling approach was applied using the ORANGE data-mining to reliably forecast biogas generation and organic matter degradation trends. The strong agreement between predicted and observed values demonstrates the capability of data-driven tools to capture the complex behaviour of waste degradation processes. This integration of experimental bioreactor studies with predictive modeling represents a key innovation of the work, enabling improved forecasting of landfill performance. From an environmental perspective, such predictive capability supports better bioreactor management, faster waste stabilization, and more efficient biogas recovery, contributing to sustainable waste-to-energy systems and reduced long-term environmental risks associated with landfill operations. © The Author(s) 2026.