(258e) Development of the Process Optimization Methodology Based on the Carbon-Techno-Economic Analysis: Application to the Post-Combustion CO2 Capture Process

According to the World Energy Outlook (IEA, 2022), the power and industry sectors are the major contributors to carbon emissions.¹ Carbon emissions have been reduced by improving energy efficiency in these fields, but carbon emissions should be considered in the process design stage for further reduction. Recently, a carbon tax has been introduced to incentivize the reduction of carbon emissions.² Through the carbon tax, the carbon emissions are considered in the economic aspects, and it causes an additional expense. Therefore, designing a cost-efficient process that takes the carbon tax into account is important.

As a transitional strategy between the present-day and carbon-free industry, it is viable to introduce a post-combustion CO₂ capture (PCC) process. The monoethanolamine (MEA)-based PCC process is one of the mature technologies that can be easily incorporated into the existing processes.³ Nevertheless, it necessitates a considerable amount of thermal energy for the solvent regeneration process, and the associated steam cost constitutes a significant fraction of the operating cost.⁴ To address this issue, previous studies have focused on minimizing the thermal energy requirement or cost of the PCC process. However, the carbon tax has been predominantly utilized for process analysis, and only a few studies have employed it in the design of the PCC process.

This study introduces the carbon-techno-economic analysis (CTEA) model. The CTEA model differs in that it additionally reflects carbon tax in addition to the capital cost and the operating cost considered in the techno-economic analysis (TEA) model. The methodology is applied to a PCC process treating flue gas from a natural gas combined cycle (NGCC) power plant. The NGCC process is validated based on the DOE/NETL report,⁵ and the PCC process simulation is conducted using Aspen Plus V11. The absorber and stripper of the PCC process are modeled using a rate-based approach. The optimization model, including the CTEA model, is developed on a Python environment, and equipment costs are estimated based on the bare module cost. The Bayesian optimization technique is utilized to find the optimal solution with three different objective functions: (i) thermal energy requirement, (ii) TEA-based cost model, and (iii) CTEA-based cost model.

To figure out economic feasibility by the carbon tax, we assumed seven different carbon taxes into two categories: (i) current trends in South Korea and (ii) strict regulations for net-zero emissions. For the former category, the carbon taxes are assumed to rise by $12.5, $25, $50, and $100 per tonne CO₂; for the latter category, the carbon taxes are $150, $200, and $250 per tonne CO₂ emissions. The optimization is performed for the PCC process, while the NGCC process is used for calculating the amount of decreased power generation.

The optimization results are presented as follows:

• Energy optimization and TEA-based optimization are not affected by the carbon tax price. Therefore, the optimization results converge to the same operating conditions for these objective functions.
• Through the CTEA-based optimization with various scenarios, we found three major trade-offs as follows:
  
  • As the carbon tax price increases, a larger amount of CO2 should be captured, which increases thermal energy consumption.
  • The increase in carbon tax prices increases energy consumption in the PCC process and lead to a decrease in net power generation in the NGCC process.
  • The PCC process cost increases to reduce the carbon emissions cost as the carbon tax price increases.
• The cost of the PCC process for energy optimization is higher than that of TEA-based optimization; CTEA-based optimization results in a lower PCC process cost than energy optimization when the carbon tax price is low. Nevertheless, if the carbon tax exceeds $50/tonne-CO₂, the PCC process cost from CTEA-based optimization becomes more expensive than that of energy optimization. Furthermore, this trend continues to rise as the carbon tax price increases.
• Despite this, due to the decrease in carbon emissions cost, the total profit considering the carbon tax is lowest for CTEA-based optimization.

The result shows that using CTEA-based optimization can achieve higher profit by deriving process conditions that can reduce the carbon emissions cost even as energy consumption and process cost increase, even in high carbon tax price. It is also demonstrated the potential of carbon taxes to induce carbon emissions reduction in fossil fuel-based processes. These findings provide valuable insights into the trade-offs between net power generation, specific reboiler duty, CO₂ capture level, and profitability in the context of different carbon tax scenarios. Overall, this study highlights the importance of including carbon taxes as a key variable in optimization models for maintaining profitability and reducing the negative environmental impact of carbon emissions. The results of this research are expected to aid companies in establishing operational strategies for their processes, while also assisting governments in formulating appropriate carbon tax policies that encourage carbon emissions reduction.

REFERENCE

[1] International Energy Agency, “World Energy Outlook 2022,” (2022).

[2] Wang, C., et al., “Supply chain enterprise operations and government carbon tax decisions considering carbon emissions,” J. Clean Prod.,152, 271-280 (2017).

[3] Jande, Y.A. C., et al., “Energy minimization in monoethanolamine‐based CO2 capture using capacitive deionization,” Int. J. Energy Res., 38 (12), 1531-1540 (2014).

[4] Oh, S., et al., “Energy minimization of MEA-based CO2 capture process,” Appl. Energy, 169, 353-362 (2016).

[5] U.S. National Energy Technology Laboratory, “Cost and Performance Baseline for Fossil Energy Plants Volume 1: Bituminous Coal and Natural Gas to Electricity,” (2022).