2024 AIChE Annual Meeting
(97b) Comparative and Predictive Analyses of Different Adsorption Processes for Carbon Capture from Cement Plants Flue Gas Streams
Cement and steel industries are among those sectors that are hard to decarbonize in the near future. As such, more efforts should be dedicated to mitigating CO2 emissions from these industries. Specifically considering cement power plant, their flue gas streams are usually at elevated temperature (>100 ºC), which makes the capture process challenging and complex. This study explores modeling and optimization of different adsorption processes such as pressure swing adsorption (PSA), temperature swing adsorption (TSA), and electric swing adsorption (ESA) tailored to the unique requirements of CO2 capture and utilization offering precise insights into key performance indicators (KPIs) crucial for cement production and decarbonization. The process systems engineering (PSE) approach involved optimizing KPIs such as purity, recovery, productivity, and energy consumption with a keen focus on maximizing the production of hybrid supplementary cementitious materials (SCM), while minimizing environmental footprints. The effects of operational key variables on the KPIs and overall capture performance were systematically analyzed for each adsorption scenario. Through rigorous conceptual design, simulation, and empirical validation, we conducted sensitivity analyses and optimized KPIs, with the Kiln's cooler end emerging as a promising CO2 capture source. Assuming a precooling step, the room temperature PSA process acheived purity levels exceeding 90%, surpassing ESA and TSA (85-90%), with a targeted recovery rate potentially exceeding 95% (>90%), providing higher productivity rates of around 80-85%. Targeting energy consumption of 0.8-1.2 kWh/kgCO2 captured, the actual capture rate varied around 1.5 kWh/kgCO2. By bridging performance projections with process engineering solutions, this research contributed to the advancement of decarbonization initiatives in the cement industry, with iterative testing, predictive modeling, and a robust framework, driving towards a sustainable future.