Model-based Efficiency Analysis of Power Plants with Carbon Footprint Constraints

To address the issues caused by CO2 emissions from the fossil-fueled combustion process by the power generation system, the comprehensive analysis of large-scale dynamic power plant systems with varying power load and supervisory control architecture that include carbon footprint constraints is presented. Model-based, system-scale dynamic simulation and optimization are useful tools for assessment and prediction of plant performance, decisions on the design configuration, and the tuning of operating procedures and control strategies. Efficiency estimates are provided for all the scenarios studied, and these estimates are optimal in terms of design configuration, control architecture and process sequencing. Moreover, the need to mitigate CO2 emissions leads power plant operators to explore advanced options for efficiency optimization and integration of power plants with carbon capture and storage (CCS) technologies. Process intensification options are explored for near-carbon-neutral, natural-gas-fueled combined cycle power plants, wherein the conventional combustor is replaced by a series of chemical-looping combustion reactors. Integrated power plant models are presented in this work, such as models of steam thermal power plants and combined cycle power plants. This work shows a complete workflow of data collection, model development, validation, control tuning, dynamic optimization formulation and solution, and supervisory control architectures for power generation systems. With the consideration of further reducing CO2 emissions, dynamic modeling and optimization are deployed to design chemical-looping combustion integrated with combined cycle power plants with optimal configuration and performance. The overall plant efficiency is improved by optimizing the chemical-looping reactor design and operation, and modifying the combined plant configuration and design. Moreover, process intensification for chemical-looping combustion reactors was explored in the form of reactor modularization. Specifically, fixed bed reactors were explored that are split into small reactor modules emulating the performance of a simulated moving bed reactor. The scheduling of the reactor modules was solved as a dynamic optimization problem that decides process variables and time intervals for the operation of each module at different chemical looping stages. The optimal scheduling of semi-batch reactors in cyclic arrangement revealed more complex patterns of gas switching that improve the thermodynamic efficiency of the process.