Probabilistic life cycle assessment of ash-based sintered lightweight aggregates manufactured with producer gas and coal-operated thermal power

Abstract

The infrastructure growth in the world is expected to result in huge requirement of 12.5 billion tonnes of coarse aggregates in 2050. The utilization of artificial aggregates can pave a feasible pathway for tackling the issue of scarcity of natural aggregates. Life cycle assessment (LCA) is an environment management tool, which has been used for the large-scale acceptability of sintered flyash lightweight aggregates (SFLA) in the construction fraternity. The low quality of data inputs for LCA study induces bias and increase in uncertainty of evaluated impacts. In the current study, a probabilistic LCA framework has been developed for assessing the environmental impacts from the manufacturing of SFLA. The uncertainty distribution range in each of the input variables was identified and introduced in the model with the help of random numbers. In this study, uncertainty analysis is also carried out using Monte Carlo Simulation for the comparative analysis of baseline scheme with three alternative schemes of SFLA manufacturing process. Finally, the sensitivity analysis (SA) was also undertaken for studying the robustness of LCA model outputs. The global warming potential (GWP) for the baseline scenario is 198.6 kg CO2 eq. per t of SFLA. Three alternative schemes were proposed for which comparative impact assessment is carried out, which highlighted the GWP impacts reduces to 166.7 kg CO2 eq. per t of SFLA (16% lower), 142.6 kg CO2 eq. per t of SFLA (28% lower) and 123.4 kg CO2 eq. per t of SFLA (38% lower) for first, second and third alternative schemes respectively as compared to the baseline scheme. Sintering process is contributing highest to impact mainly due to emissions from combustion of coal present in raw mix, CO2 emissions from electricity consumed during the process and CO2 generated from producer gas production which is used for thermal energy in sintering process. The results of probabilistic LCA study show that there are significant variations in the coefficient of variation across the various unit processes and across the four impact categories.