Integrated Simulation and Optimization for the Whole Chain of CCS : CCS 전 공정 통합 시뮬레이션 및 최적화

다국어 초록 (Multilingual Abstract)

Operation pressure of distillation column is one of the key variable for optimizing
the required energy in a CCS process. It affects the steam drag
point in power plant, the regeneration energy in capture process and the
compression energy in liquefaction process. A new algorithm, which is less
dependent on simulation, for determining optimal stripper pressure for CCS
process using MEA as an absorbent is proposed based on the integrated simulation
model. Total energy required is represented as a function of the pressure
based on the equivalent work. The results show that the compression
work can be reduced at high pressure while that for reboiler increases and
the total energy can be represented as a decreasing function with the stripper
pressure. The evaluated optimal pressure decreases as the terminal pressure
increases, showing the crucial condition for determining operation pressure
of stripper depends on the terminal pressure of liquefaction process. It is also shown that a general analytical solution for optimal pressure including
both the capture and the liquefaction process cannot be made through differentiation
based on Abel Ruffini theorem. The total energy required in the
possible range of the pressure can be estimated directly using approximation
with given input variables.

목차 (Table of Contents)

• 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . 1
• 2. Carbon Capture and Sequestration/Storage(CCS) . . . 3
• 2.1 Concept of CCS . . . . . . . . . . . . . . . . . . . . . . . . 4
• 2.2 Current Status of CCS . . . . . . . . . . . . . . . . . . . . 5
• 2.3 Necessity of the Integrated Simulation . . . . . . . . . . . . 6
• 3. Integrated Simulation Model . . . . . . . . . . . . . . . . 7
• 3.1 Power plant . . . . . . . . . . . . . . . . . . . . . . . . . . 8
• 3.2 Capture process . . . . . . . . . . . . . . . . . . . . . . . . 10
• 3.3 Compression and liquefaction process . . . . . . . . . . . . 12
• 3.4 Transmission and storage process . . . . . . . . . . . . . . . 13
• 4. Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 15
• 4.1 Input and output analysis . . . . . . . . . . . . . . . . . . . 16
• 4.2 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . 17
• 4.3 Selection of the key manipulated variable . . . . . . . . . . 18
• 5. Optimization Algorithm . . . . . . . . . . . . . . . . . . . 19
• 5.1 Problem definition . . . . . . . . . . . . . . . . . . . . . . 20
• 5.2 Formulations . . . . . . . . . . . . . . . . . . . . . . . . . 21
• 5.2.1 Reboiler duty . . . . . . . . . . . . . . . . . . . . . 21
• 5.2.2 Compression work . . . . . . . . . . . . . . . . . . 27
• 5.2.3 Regression analysis for temperature . . . . . . . . . 29
• 5.3 Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
• 5.3.1 Case I. Ship transmission . . . . . . . . . . . . . . . 31
• 5.3.2 Case II. Pipeline transmission . . . . . . . . . . . . 34
• 5.4 Generallization . . . . . . . . . . . . . . . . . . . . . . . . 35
• 5.5 Analytical solution . . . . . . . . . . . . . . . . . . . . . . 38
• 6. Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
• 6.1 Comparison with other studies . . . . . . . . . . . . . . . . 42
• 6.2 Significance and limitations . . . . . . . . . . . . . . . . . . 43
• 7. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . 44
• 8. Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . 46