Non-linear system Identification and control of Solvent-Based Post-Combustion CO2 Capture Process

Solvent-based post-combustion capture (PCC) is a well-developed technology for CO2 capture from power plants and industry. A reliable model that captures the dynamics of the solvent-based capture process is essential to implement suitable control system design. Typically, first principles models are used, however, they usually require comprehensive knowledge and in-depth understanding of the process. In addition, the high computational time required and high complexity of the first principles models makes it unsuitable for control system design implementation. This thesis is aimed at the development of a reliable dynamic model via system identification technique as well as a suitable process control strategy for the solvent-based post-combustion CO2 capture process. The nonlinear autoregressive with exogenous (NARX) inputs model is employed to represent the relationship between the input variables and output variables as two multiple-input single-output (MISO) sub-systems. The forward regression with orthogonal least squares (FROLS) algorithm is implemented to select an accurate model structure that best describes the dynamics within the process. The prediction performance of the identified NARX models is promising and shows that the models capture the underlying dynamics of the CO2 capture process. The model obtained was adopted for various process control system design of the solvent-based PCC process (conventional PI, MPC, and NMPC). For the conventional PI controller design, multivariable control analysis was carried out to determine a suitable control structure. Control performance evaluation of the control schemes reveals that the NMPC scheme was suitable to control the solvent-based PCC process at flexible operations. Findings obtained from the thesis underlines the advancement in dynamic modelling and control implementation of solvent-based PCC process

Experimental study of CO2 solubility in high concentration MEA solution for intensified solvent-based carbon capture

The solvent-based carbon capture process is the most matured and economical route for decarbonizing the power sector. In this process, aqueous monoethanolamine (MEA) is commonly used as the solvent for CO2 scrubbing from power plant and industrial flue gases. Generally, aqueous MEA with 30 wt% (or less) concentration is considered the benchmark solvent. The CO2 solubility data in aqueous MEA solution, used for modelling of the vapour-liquid equilibria (VLE) of CO2 in MEA solutions, are widely published for 30 wt% (or less) concentration. Aqueous MEA with higher concentrations (from 40 to 100 wt%) is considered in solvent-based carbon capture designs with techniques involving process intensification (PI). PI techniques could improve the process economics and operability of solvent-based carbon capture. Developing PI for application in capture process requires CO2 solubility data for concentrated MEA solutions. These data are however limited in literature. The modelling of the vapour-liquid equilibria (VLE) of CO2 in MEA solutions for PI-based solvent capture techniques involving stronger MEA solution of about 80 wt% concentration requires solubility data at the concentration. In this study, the data for 80 wt% MEA is presented for 40,60, 100 and 120oC. The experimental technique and analytical procedure in this study were validated by comparing the measurements for 30 wt% MEA with data from the literature. The data from this study can be fitted to VLE models such as electrolyte NRTL, extended UNIQUAC etc. which is an important component of solvent-based capture model using MEA as the solvent. More accurate VLE models will improve the prediction accuracy of capture level, rich loading etc. using PI-based solvent-based capture model

Non-linear system identification of solvent-based post-combustion CO2 capture process

Solvent-based post combustion capture (PCC) is a well-developed technology for CO2 capture from power plants and industry. A reliable model that captures the dynamics of the solvent-based capture process is essential to implement suitable control design. Typically, first principle models are used, however they usually require comprehensive knowledge and deep understanding of the process. System identification approach is adopted to obtain a model that accurately describes the dynamics between key variables in the process. The nonlinear auto-regressive with exogenous (NARX) inputs model is employed to represent the relationship between the input variables and output variables as two Multiple-Input Single-Output (MISO) sub-models. The forward regression with orthogonal least squares (FROLS) algorithm is implemented to select an accurate model structure that best describes the dynamics within the process. The prediction performance of the identified NARX models is promising and shows that the models capture the underlying dynamics of the CO2 capture process

Experimental study of CO

The solvent-based carbon capture process is the most matured and economical route for decarbonizing the power sector. In this process, aqueous monoethanolamine (MEA) is commonly used as the solvent for CO2 scrubbing from power plant and industrial flue gases. Generally, aqueous MEA with 30 wt% (or less) concentration is considered the benchmark solvent. The CO2 solubility data in aqueous MEA solution, used for modelling of the vapour-liquid equilibria (VLE) of CO2 in MEA solutions, are widely published for 30 wt% (or less) concentration. Aqueous MEA with higher concentrations (from 40 to 100 wt%) is considered in solvent-based carbon capture designs with techniques involving process intensification (PI). PI techniques could improve the process economics and operability of solvent-based carbon capture. Developing PI for application in capture process requires CO2 solubility data for concentrated MEA solutions. These data are however limited in literature. The modelling of the vapour-liquid equilibria (VLE) of CO2 in MEA solutions for PI-based solvent capture techniques involving stronger MEA solution of about 80 wt% concentration requires solubility data at the concentration. In this study, the data for 80 wt% MEA is presented for 40,60, 100 and 120oC. The experimental technique and analytical procedure in this study were validated by comparing the measurements for 30 wt% MEA with data from the literature. The data from this study can be fitted to VLE models such as electrolyte NRTL, extended UNIQUAC etc. which is an important component of solvent-based capture model using MEA as the solvent. More accurate VLE models will improve the prediction accuracy of capture level, rich loading etc. using PI-based solvent-based capture model