Reliability Assessment of Bioreactor Landfill Performance Using Coupled Thermo-Hydro-Bio-Mechanical Model

This study presents a reliability-based analysis performed using a coupled thermo-hydro-bio-mechanical model to assess the longterm performance of a typical landfill cell geometry under simulated bioreactor landfill conditions. Several Monte-Carlo simulations were carried out by considering randomly generated lognormally distributed fields of different properties of waste to represent the waste heterogeneity. The long-term performance of the simulated bioreactor landfill cell was evaluated in terms of wetted area (W-A), cumulative methane (CH4) gas generated (WCH4), maximum surface settlement (SSmax), and maximum temperature of the waste (Tmax), at different intervals of time during the operation of the simulated bioreactor landfill cell. A deterministic analysis was also conducted using the mean values for the different properties of waste. The results show that the deterministic analysis overpredicts the maximum value for W-A, WCH4, SSmax, and T-max obtained from the stochastic analysis by approximately 10%, 17%, 25%, and 14%, respectively. The most likely values estimated for the four performance metrics from the stochastic analysis were approximately 44%, 88,000 m(3), 5.5 m, and 62.5 degrees C, respectively. It was also determined that the waste's anisotropy with regard to its hydraulic conductivity and the biodegradation-induced void change parameter had the most influence on W-A and the SSmax in the landfill cell, respectively. (C) 2022 American Society of Civil Engineers.

Automated summary

This study presents a reliability-based analysis using a coupled thermo-hydro-bio-mechanical model to assess the long-term performance of a landfill cell under bioreactor landfill conditions. Monte-Carlo simulations and deterministic analysis were carried out, and the results reveal the influence of waste heterogeneity and waste properties on the performance metrics of the landfill cell. The stochastic analysis provides more accurate predictions and identifies the factors that have the greatest impact on the performance of the landfill cell.