(67e) Development of an Integrated Mass Transfer and Kinetic Model from Multi-Scale Data for CO2 Capture Using Concentrated Piperazine

Process modeling and simulation with rigorous, validated models is critical for accelerating the development and deployment of CO₂ capture technologies while simultaneously reducing the risk in process scale-up [1,2]. The U.S. DOE’s Carbon Capture Simulation for Industry Impact (CCSI²) program is applying the Carbon Capture Simulation Initiative (CCSI) Toolset to accelerate the development of carbon capture technologies. CCSI developed a multiscale, simultaneous regression approach for mass transfer and kinetic parameters resulting in a highly accurate fundamental process model of CO₂ absorption into MEA solvent [3,4]. Concentrated piperazine (PZ) solution has been proposed as potential alternative solvent with improved performance due to faster kinetics, higher capacity, and a lower degradation rate compared with MEA [5]. In this work, an integrated mass transfer and kinetic model the PZ solvent will be presented.

Rigorous rate-based process models consist of several submodels with varying complexity. These models must accurately represent mass and energy balances of the phases, non-ideal thermodynamics, submodels for packing hydraulics, mass and heat transfer coefficients, and physico-chemical properties. Moreover, solvent based post-combustion CO₂ absorption modeling is complex due to the coupled effects of thermodynamics, mass transfer, and reactions. It is therefore imperative that the gas-liquid interface model accounts for complex chemical reactions.

To facilitate the absorption process, packed columns are often used for gas-liquid contacting. Several empirical correlations have been developed for the hydrodynamics and mass transfer coefficients in these packed columns; however, most of these packing models were developed using data based on simplified experiments of different systems and, in certain cases, relied on assumptions not generally valid for CO₂ capture technologies [6]. In general, the development of these models is done sequentially whereas in the real chemical solvent based CO₂ capture process, it is difficult to separate the mass transfer and chemical reaction phenomena due to fast reaction kinetics. In addition, data is often collected at different scales and different operating regimes while using the following methodologies:

• Mass transfer coefficients and reaction kinetics are estimated from wetted wall column (WWC) experiments.
  1. The physical liquid film mass transfer coefficient is measured by disregarding the gas fim resistance from absorption rate data of CO₂ into water.
  2. The gas side film mass transfer coefficient is measured using the instanteneous irreverasible reaction system, i.e., from absorption data of SO₂ into aqueous NaOH solution.
• Assuming the mass transfer coefficient models to be same, the experiments from packed columns are then used to obtain an interfacial area model.

The sequential development of these submodels results in the uncertainty for each submodel being fixed and carried to the next model. It was shown that there is a large uncertainty associated with applying these correlations for CO₂ capture applications [7,8].

To minimize the total combined uncertainty, a simultaneous parameter regression of the mass transfer coefficients, pressure drop, interfacial area, and kinetic reaction models are performed using multi-scale experimental data from literature for pilot-scale packed columns and lab-scale wetted wall columns experiments. This simulatneous regression methodology was shown to be more aacurate for MEA solvent than the sequential regression methodology enabling resulting models to accurately predict process performance at different scales (pilot and semi-industrial) [2,4,9,10].

Concentrated piperazine with advanced process configurations was tested at the pilot scale and was found to be less energy intesive than MEA [11–13]. The integrated mass transfer, hydrodynamic, and kinetic models for concentrated piperazine solvent using the simultaneous regression is accomlished through the use of the Framework for Optimization Quantification of Uncertainty and Surrogates (FOQUS) toolset [14,15] linked with Aspen Plus. The developed model will be validated against independent experimental data [16–18].

References

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[2] Chinen AS, Morgan JC, Omell B, Bhattacharyya D, Tong C, Miller DC. Development of a Gold-Standard Model for Solvent-Based CO2 Capture. Part 1: Hydraulic and Mass Transfer Models and Their Uncertainty Quantification. Ind Eng Chem Res 2018;submitted.

[3] Miller DC, Syamlal M, Mebane DS, Storlie C, Bhattacharyya D, Sahinidis NV, et al. Carbon Capture Simulation Initiative: A Case Study in Multiscale Modeling and New Challenges. Annu Rev Chem Biomol Eng 2014;5:301–23. doi:10.1146/annurev-chembioeng-060713-040321.

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[9] Morgan JC, Chinen AS, Omell B, Bhattacharyya D, Tong C, Miller DC. Thermodynamic modeling and uncertainty quantification of CO 2 -loaded aqueous MEA solutions. Chem Eng Sci 2017;168:309–24. doi:10.1016/j.ces.2017.04.049.

[10] Morgan JC, Chinen AS, Omell B, Bhattacharyya D, Tong C, Miller DC, et al. Development of a Gold-Standard Model for Solvent-Based CO2 Capture. Part 2: Steady-State Validation and Uncertainty Quantification with Pilot Plant Data. Ind Eng Chem Res 2018;submitted.

[11] Cousins A, Huang S, Cottrell A, Feron PHM, Chen E, Rochelle GT. Pilot-scale parametric evaluation of concentrated piperazine for CO ₂ capture at an Australian coal-fired power station. Greenh Gases Sci Technol 2015;5:7–16. doi:10.1002/ghg.1462.

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[13] Lin Y-J, Chen E, Rochelle GT. Pilot plant test of the advanced flash stripper for CO2 capture. Faraday Discuss 2016;192:37–58. doi:10.1039/C6FD00029K.

[14] Eslick JC, Ng B, Gao Q, Tong CH, Sahinidis NV, Miller DC. A Framework for Optimization and Quantification of Uncertainty and Sensitivity for Developing Carbon Capture Systems. Energy Procedia 2014;63:1055–63. doi:10.1016/j.egypro.2014.11.113.

[15] Miller DC, Eslick J, Boverhof J, Leek J, Tong C, Chen Y, et al. FOQUS: A Framework for Optimization and Quantification of Uncertainty 2015.

[16] Chen E, Madan T, Sachde D, Walters MS, Nielsen P, Rochelle GT. Pilot Plant Results with Piperazine. Energy Procedia 2013;37:1572–83. doi:10.1016/j.egypro.2013.06.033.

[17] Chen E, Fulk S, Sache D, Lin Y, Rochelle GT. Pilot Plant Activities with Concentrated Piperazine. Energy Procedia 2014;63:1376–91. doi:10.1016/j.egypro.2014.11.147.

[18] Chen E, Zhang Y, Lin Y, Nielsenb P, Rochelle G. Review of Recent Pilot Plant Activities with Concentrated Piperazine. Energy Procedia 2017;114:1110–27. doi:10.1016/j.egypro.2017.03.1266.