Optimization of landfill gas generation based on a modified first-order decay model: A case study in Quebec province

Abstract

Abstract Landfills will likely remain an essential part of integrated solid waste management systems in many developed and developing countries for the foreseeable future. This paper uses a genetic algorithm to fit parameters to a CH4 and H2S generation model independently based on a modified first-order decay model. In the case of CH4 generation modeling, biodegradable organic waste (OW) was segregated into food waste, yard waste, paper, and wood. In addition to optimizing the OW fractions, key modeling parameters of OW, such as CH4 generation potential (\({L}_{0}\)) and CH4 decay rate (\({k}_{C{H}_{4}}\)), were determined independently for different periods in the life of the landfill. Similarly, in the case of H2S generation modeling, the construction and demolition waste (CD) was classified into fines (FCD) and bulky materials (BCD), and H2S generation potential (\({S}_{0}\)) and H2S decay rate (\({k}_{{H}_{2}S}\)) of FCD and BCD were determined. Landfill gas (LFG) collection data from a site in Quebec province (Canada) was used to validate the LFG generation model. A range of scenarios was analyzed using the validated model, including twelve scenarios for CH4 and two for H2S modeling, respectively. The results showed that the differentiation of more waste types improves the modeling accuracy for CH4. Moreover, within the decade-long lifetime of a landfill, the waste management strategies change, requiring different assumptions for the modeling. Also, the work showed the importance of considering how different sectors of a landfill are filled over time. Finally, scenario twelve, which assumed four waste types, constant three periodic waste fractions, and six sectors, had the lowest residual sum of squares (RSS) value. For H2S generation modeling, both scenarios, with or without separate fits of \({S}_{0}\) and \({k}_{{H}_{2}S}\) for FCD and BCD, predicted the generated H2S well and had a very similar RSS value. Further data could improve H2S generation modeling.