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How to Model CCUS Chemistry in OLI Studio: Stream Analyzer

 

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Overview

Carbon capture, utilization, and storage (CCUS) plays a critical role in reducing CO₂ emissions and mitigating global temperature rise. One of the technical challenges in CCUS lies in the safe transportation of captured CO₂. Process impurities such as nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and hydrogen sulfide (H₂S) may be present in the CO₂ stream. When combined with moisture, these species can form highly corrosive acids, posing risks of severe degradation and potential failure in carbon steel pipelines and storage systems.

A detailed understanding of the electrolyte chemistry of these impurities is essential for assessing when they may form corrosive compounds and when they may remain relatively benign. Accurate simulation requires the simultaneous prediction of reaction equilibria and phase equilibria, particularly in dense phases where CO₂ is the dominant component. Such models are critical for defining safe operating windows for the transport and storage of CO₂.

To address these challenges, OLI Systems has collaborated with the Norwegian Institute for Energy Technology (IFE) to incorporate experimentally validated thermodynamic models into the OLI software platform. These models allow for improved prediction of corrosive species formation and provide a basis for evaluating safe operating conditions in CO₂ transport pipelines and shipping systems.

Modeling Approach

The CO2 stream containing impurities has a composition given in Table 1.

Table 1. Composition of CO2 stream containing impurities

Component Amount (Note)
CO2 >95%
Total Inerts + HC Total 5% (<1% N2, H2, CO, Ar and CH4)
H2O 85 ppmv (4 lb/mmscf)
H2S 0
NOx <20 ppmv
SOx <20 ppmv
O2 <500 ppmv
Pressure 85 bar
Temperature 5/25
Material Carbon Steel

 

Assumption: The inert gases (mix of N2, H2, CO, Ar, and CH4) were not added in the modeling.

For all the calculations, the Mixed Solvent Electrolyte (MSE) model was used, and the phases: Vapor (Va), acid-rich phase (Liq1), dense phase (Liq2), and solid phase (So) were turned on. When impurities are present in the CO2-dense phase, such as NOx and SOx, the following oxidation states need to be selected in the model: S(-2), S(0), S(+4), and S(+6) for sulfur, and N(+2), N(+4), and N(+5) for nitrogen.

Table 2 allows us to track the specific components that are reacting based on their oxidation states.

Table 2. Oxidation state and its equivalent component

Oxidation State

Component

S(-2) H2S
S(0) S8
S(+4) SO2
S(+6) SO4-2, HSO4- H2SO4

N(-3)

NH₄⁺, NH₃, NH₄HSO₄, (NH₄)₂SO₄·NH₄HSO₄, NH₄HSO₄·H₂SO₄
N(+2) NO
N(+4) NO2
N(+5) HNO3

OLI Simulation Results

Three different set of results will be presented: (1) A temperature survey from 5ºC to 25ºC showing the composition of the acid that drops out from the CO2 dense phase, (2) the calculated solubility curve for H2SO4 for the given stream composition and (3) the calculated corrosion rates caused by the acid phase that drops out of the CO2-rich phase on carbon steel. 

  1. Temperature Survey

    Figure 1 shows the acid composition that drops out of the CO2-dense phase based on the input data given in Table 1. Notice that predicted liquid phase contains different species that contribute to its acidity; for example, at 25ºC, H3O+ (37.5 mol%) and HSO4- (36.2 mol%) are the most predominant acid species, followed by H2SO4 (1.27%), SO4-2 (0.63 mol%), and HNO3 (0.043 mol%).
     

    Figure 1. Composition of main species in the liquid phase that drops out of the CO2-dense phase as a function of temperature.
  2. Solubility Curves

    Solubility curves for H2SO4 using the stream composition given in Table 1 for four different temperatures (-5, 10, 25, and 50 ºC) are shown in Figure 2. A reference solubility plot (IFE publication, see Appendix A) for a 96%wt H2SO4 is shown in Figure 3.

     

    Figure 2. Solubility curve for H2SO4 for a stream composition given in Table 1.
    Figure 3. Solubility curve for a 96 wt% H2SO4 solution with no impurities present.

Conclusion

The modeling and simulation of CO₂ transport streams containing trace impurities highlight the importance of accounting for both chemical speciation and phase behavior in dense CO₂ systems. Even at low concentrations, species such as SOₓ and NOₓ can form strong acids under certain conditions, creating a significant risk of corrosion in carbon steel infrastructure.

Using the Mixed Solvent Electrolyte (MSE) framework (now fully available in OLI v12.5), engineers and chemists can predict both reaction equilibria and phase equilibria with improved accuracy. This allows for the identification of conditions under which impurities remain stable versus when they form corrosive phases. These predictive capabilities provide a robust scientific basis for defining safe operating windows and support the reliable design and operation of CO₂ pipelines and shipping systems within CCUS projects.

Collaborative developments, such as the thermodynamic models created by OLI in partnership with the Norwegian Institute for Energy Technology (IFE), demonstrate how advanced simulation tools bridge experimental data with practical engineering. With these enhancements now implemented in OLI Studio v12.5, the industry has a more powerful platform to ensure safe, efficient, and long-term carbon capture, utilization, and storage.

Appendix A

Figure 4. Experimental data, 2019

 

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