MANAGEMENT DECISION SUPPORT SYSTEM OF SOLVENT-BASED POST-COMBUSTION CARBON CAPTURE

Abstract

A management decision-support framework for a coal-fired power plant with solvent based post combustion CO2 capture (PCC) (integrated plant) is proposed and developed in this thesis. A brief introduction pertaining to the solvent-based PCC technology, thesis motivations and objectives are given in Chapter 1. Chapter 2 comprises a comprehensive literature review of solvent-based PCC plant from the bottom level (PCC instrumentation level) until the top level (managerial decision of PCC system). Chapter 3 describes the development of solvent-based PCC dynamic model via empirical methods. Open-loop dynamic analyses are presented to provide a deeper understanding of the dynamic behaviour of key variables in solvent-based PCC plant. Chapter 4 presents the design of the control architecture for solvent-based PCC plant. Two control algorithms developed, which utilise conventional proportional, integral and derivative (PID) controller and advanced model predictive control (MPC). Chapter 5 proposes a conceptual framework for optimal operation of the integrated plant. The MPC scheme is chosen as the control algorithm while mixed integer non-linear programming (MINLP) using genetic algorithm (GA) function is employed in the optimization algorithm. Both algorithms are integrated to produce a hybrid MPC-MINLP algorithm. Capability and applicability of the algorithm is evaluated based on 24 hours and annual operation of integrated plant. Chapter 6 extends the scope of Chapter 5 by evaluating the relevance of solvent-based PCC technology in the operation of black coal-fired power plant in Australia. This chapter considers a prevailing climate policy established in Australia namely Emission Reduction Fund (ERF). Finally, the concluding remarks and future extensions of this research are presented in Chapter 7.