Advanced Process Modeling and Optimization of Amine-Based Carbon Capture Process

With the rise of carbondioxide (CO2) concentration in the atmosphere to more than 400 parts per million (ppm), research efforts have been focused on achieving net-zero carbon emission technologies. Post-combustion CO2 capture (PCC) is a key strategy in reducing CO2 emissions. Amine-based CO2 capture is the baseline technology for retrofitting existing power stations. However, the integration of amine-based PCC technology with power plants to reduce greenhouse gas emissions incurs a high energy penalty, decreasing a powerplant’s efficiency by about 23 percentage points. Understanding the capture plant dynamics plays an important role in its technical and economic performance. Rigorous models are critical to understanding how to address the emission of CO2 from large point sources such as power plants and the techno-economic performance of capture units, as the power plants increasingly cycle due to increased penetration of renewables into the grid. These issues are very important because it addresses the costs of achieving net-zero carbon systems in carbon capture-fitted plants. Modeling and simulation of advanced multiscale process units have great potential in accelerating the commercialization of carbon capture technologies. The objective of this study is to develop high-fidelity process units that are computationally tractable for amine-based CO¬2 capture process in an equation-oriented platform suitable for optimization. These process units include gas-liquid contactors for absorption/stripping operation, rich-lean heat exchanger, reboiler, and condenser. Process models of these units involve development of rigorous sub-models for the thermodynamic and transport properties, and the hydrodynamic and mass transfer sub-models of specific unit operations. Rigorous thermophysical property models for challenging solvent systems in PCC such as mixed solvent electrolyte systems and advanced mass transfer models are developed. A comprehensive description of the thermodynamic framework for multi-electrolyte mixed solvent systems is presented, where the parameter structure of the symmetric electrolyte-Non-Random Two Liquid (e-NRTL) model is reformulated and a thermodynamically consistent and analytically derived formulation for the excess enthalpy is developed from the e-NRTL model. The refined parameter structure of the e-NRTL model removes a numerical singularity in the absence of ionic species and extends the derived excess enthalpy expressions to non-electrolyte systems. The thermodynamic framework is demonstrated for the MEA-H2O-CO2 case study using experimental data on thermodynamic quantities for the binary MEA-H2O and the ternary MEA-H2O-CO2 systems. Also, multi-component transport of molecular and ionic species is modeled using Maxwell-Stefan (MS) transport equation and Nernst-Planck equation that includes the effect of electrostatic forces in the two-film model for interface transfer in gas-liquid contactors. The process units are validated with available data from the National Carbon Capture Center (NCCC) in Alabama, USA, and Norway’s Technology Centre Mongstad (TCM) test facility for carbon capture technologies, along with Wetted Wall Column data from the literature. A plant-wide model of a reference monoethanolamine (MEA)-based PCC unit is developed and used for steady-state optimization under part-load operations and variable capture rates using flue gas similar to pulverized coal and natural gas-combined cycle power plants to minimize the energy penalty and identify optimal operating conditions under part-load and variable capture operations. The process unit models are implemented in the Institute for the Design of Advanced Energy System (IDAES) computational platform. This provides carbon capture technology in an integrated platform with access to advanced solver algorithms for the design and operation of complex and interacting systems, such as the upstream power generation and the downstream post-combustion carbon capture. Several approaches are developed for generating good initial guesses for future state and algebraic variables. In addition, capabilities in IDAES for activating and deactivating constraints are exploited to develop a sequential initialization strategy. IDAES software platform is open-source and all codes developed as part of this work are publicly available. Hence, researchers can use and/or modify the codes for designing and optimizing their respective technologies. Models, algorithms, and codes developed as part this research can also be used in academic institutions for teaching and research