(428e) Design of an MEA-Based Carbon Capture System for Cement Industry
AIChE Annual Meeting
Wednesday, November 17, 2021 - 4:33pm to 4:54pm
Cement production is one of the largest and most energy-intensive industries in the world. In 2018, the production of cement accounted for about 7% of global CO2 emissions [IEA, 2018]. In 2019, the cement production was responsible for 40.9 MMT CO2 equivalent in the U.S., accounting for 24.4% of U.S. total industrial CO2 emissions [EPA, 2021]. In cement plants, CO2 emissions originate from the calcination of limestone where CaCO3 is converted to CaO and CO2, and from the combustion process of the fossil fuels in the calciner and rotary kiln [Voldsund et al., 2019]. Thus, the operating conditions and the final products impact the CO2 generated at the plant (both flow rate and CO2 mol%), which depends on the fuel used, air/fuel mass ratio, composition and flow rate of raw materials for cement production, flame temperature for combustion and flame temperature location. The CO2 concentration ranges from 14 to 33 mol% [Hasan et al., 2012], assuming that flue gas is an ideal gas, then the vol% is equal to mol%. The DOE/NETL-2002/1163 report shows a typical CO2 composition of 18.9 mol%, which will be considered as the baseline for this study. CO2 emissions associated with cement production are targeted for reduction, but will inevitably come at reduced efficiency and increased costs, which may affect product quality. To properly evaluate the required balance between GHG emissions, utility cost, and product quality, an integrated uncertainty quantification analysis and system optimization are required.
This work leverages the open-source Carbon Capture Simulation Initiativeâs (CCSI) toolset (https://github.com/CCSI-Toolset) and uses the baseline MEA system model [Morgan et al., 2018] developed for flue gas from a Supercritical Pulverized Coal-fired plant as a basis for this study. A kiln model to estimate the CO2 emissions originated from the calcination process and an Aspen Plus model have been developed to estimate the CO2 emissions from the combustion processes.
Finally, all the models are integrated and simulated using the Framework for Optimization, Uncertainty Quantification, and Surrogates (FOQUS) [Miller et al., 2016]. We leverage the FOQUS capabilities for performing uncertainty quantification and system optimization (aiming at minimizing regeneration reboiler duty considering the uncertainties from upstream cement production process) using derivative-free optimization tools [Larson et al.]. The uncertainty quantification study explored flue gas conditions as a function of kiln operating conditions, including raw material conditions, cement quality, flame temperature, and flame temperature location. The optimization study aims to obtain the optimal design of an MEA system and operating conditions that minimize the reboiler duty while achieving 90% CO2 capture target and a given cement quality. The results show the optimal design of the absorber and regeneration columns (diameter, packing height, and flooding %), optimal heat integration, and number of parallel trains for a baseline cement plant operating condition.
DOE/NETL-2002/1163. Cement kiln flue gas recovery scrubber project: A DOE assessment. Nov. 2001. Document from Inner communications.
EPA. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019- Chapter 4. Industrial Processes and Product Use, 2021. https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissions-and-sinks-1990-2019. [Accessed Mar. 15, 2021].
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IEA. Technology Roadmap â Low-Carbon Transition in the Cement Industry: Foldout, 2018, https://webstore.iea.org/technology-roadmap-low-carbon-transition-in-the-cement-industry-foldout. [Accessed Mar. 15, 2021].
Larson, J., Menickelly, M., & Wild, S.M. Derivative-free optimization methods. 2019. arXiv preprint arXiv:1904.11585, https://arxiv.org/pdf/1904.11585.pdf. [Accessed Mar. 26, 2021].
Morgan, J.C., Soares, C.A., Omell, B., Bhattacharyya, D., Tong, C., Miller, D.C. (2018). Development of a rigorous modeling framework for solvent-based CO2 capture. Part 2: Steady-state validation and uncertainty quantification with pilot plant data. Industrial & Engineering Chemistry Research 57: 10464-10481.
Miller, D.C., Agarwal D.A., Bhattacharyya D., Boverhof J., Cheah Y-W., Chen Y., (2016). Innovative computational tools and models for the design, optimization, and control of carbon capture processes. 26th European Symposium on Computer Aided Process Engineering â ESCAPE 26. 2391-2396.
Voldsund, M., Gardarsdottir, S.O., De Lena, E., PÃ©rez-Calvo, J.F., Jamali, A., Berstad, D., Fu, C., Romano, M., Roussanaly, S., Anantharaman, R. and Hoppe, H., 2019. Comparison of technologies for CO2 capture from cement productionâPart 1: Technical evaluation. Energies, 12(3), p. 559.
Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.