Optimization of Power Plant Simulations with Integrated Carbon Capture Systems Using Black-Box Algorithms

With increasing demand placed on power generation plants to reduce carbon dioxide (CO2) emissions, processes to separate and capture CO2 for eventual sequestration are increasingly in demand. Carbon capture technology falls under three main categories (a) pre-combustion, (b) post- combustion, and (c) oxy-combustion. The primary deciding factor in the selection of carbon capture technology is the type of power plant: pre-combustion is typically considered for integrated gasification combined cycle (IGCC) pants, while post-combustion and oxy-combustion systems would be chosen for new or existing pulverized coal (PC) plants [1]. Methods used for CO2 separation include chemical and physical absorption, adsorption, membranes, and cryogenic distillation. Current estimates by the National Energy Technology Laboratory show that increases in the cost of electricity with current carbon capture technology range from 71-85% for PC plants and 35-46% for IGCC plants [2]. We take a simulation-based approach to power plant optimization. Detailed process unit models are implemented in a modular simulation framework in order to provide a higher degree of accuracy than algebraic models formed by applying simplifying assumptions. The ASPEN Plus® models developed include exhaustive fluid packages, rate based mass transfer processes, and complicating recycle streams, all accurately modeled. To optimize the model, we rely on black-box or derivative-free optimization (DFO) solvers. DFO algorithms are advantageous when derivative information is unavailable, unreliable, or prohibitively expensive. Our implementation relies on 25 different DFO packages [3]. Extensive computational results are presented and demonstrate the detailed trade-offs between cost and carbon capture process integration for existing and new power plants.