Abstract
Various solid waste management methods have been investigated to effectively dispose of Municipal Solid Waste (MSW). These methods have been met with challenges such as cost and toxic emissions into the atmosphere. To address CO2 emissions, three approaches have been proposed: absorbing existing CO2 from the atmosphere, transitioning to renewable energies, and adopting carbon capture and storage (CCS) technologies. Chemical Looping Combustion (CLC), a promising CCS process, offers low energy costs and up to 100% CO2 capture efficiency. Implementing CLC for MSW management provides several advantages, including enhanced CO2 capture, power generation due to its highly exothermic nature, and reduced emission of dioxins compared to conventional combustion.
This study assessed the CLC performance of MSW in a bench scale fluidized bed batch reactor, the interaction of ash components with ilmenite in a horizontal batch reactor, and different machine learning applications such as Artificial neural network (ANN) and Response surface methodology (RSM) were used to estimate the CLC process performance and optimize the process condition for the CLC of different solid fuels (waste paper (WP), and sugarcane bagasse (SCB)) blends.
The fluidized bed batch reactor experiment analysis revealed that the flue gas from MSW / biomass blends had higher CO concentrations than CO2, and this was attributed to limited contact between the oxygen carrier and char. Temperature positively influenced carbon conversion from 800-900℃, but negligible effects were observed as it increased to 950℃ for individual samples and their blends. The ilmenite oxygen carrier's reactivity remained relatively stable during similar experiments and increased after solid fuel experiments.
Furthermore, process simulation results closely matched experimental results, particularly for MSW/biomass blends. The RSM analysis identified significant input variables affecting combustion parameters, with steam-to- carbon ratio, blend ratio, and solid flow rate having the most influence. The optimum input conditions for maximizing combustion parameters were determined. Under these conditions, the optimum input variables achieved 92% combustion efficiency, 71% CO2 yield, and 96% carbon capture efficiency. The statistical result obtained from RSM analysis of variance and the artificial neural network (ANN) demonstrated high accuracy in predicting the combustion performances. Based on the combustion efficiency, CO2 yield and CO2
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capture efficiency responses, a high performance correlation (R2 > 0.8) was obtained for all the combustion parameters analyzed.
The interaction of the ilmenite with the ash obtained from waste paper and sugarcane bagasse was investigated. The results showed that using ash produced from conventional ash production did not have a visible interaction between the ash elements and the oxygen carrier, as seen in the SEM-EDX compared with that obtained from the fluidized bed reactor where Ca-Fe-Ti interaction was observed with the waste paper and Si-Fe-Ti with the sugarcane bagasse.
In conclusion, the use of waste paper and sugarcane bagasse as solid fuels via chemical looping combustion is a favorable waste management process. This is due to its ability to capture CO2, generate power with high exothermicity, and reduce the emission of dioxins compared to conventional combustion. The combination of waste paper and sugarcane bagasse also improves the performance of individual samples under optimal conditions, with ilmenite exhibiting efficient and stable reactivity.