Optimization of Carbon Capture Systems Using Surrogate Models of Simulated Processes

With increasing demand placed on power generation plants to reduce carbon dioxide (CO2) emissions, processes to separate and capture CO2 for eventual sequestration are highly sought after. Carbon capture processes impart a parasitic load on the power plants; it is estimated that this would increase the cost of electricity from existing pulverized coal plants anywhere from 71-85 percent [1]. The National Energy and Technology Lab (NETL) is working to lower this to below a 30 percent increase. To reach this goal, work is being done not only to accurately simulate these processes, but also to leverage those accurate and detailed simulations to design optimal carbon capture processes. The major challenges include the lack of accurate algebraic models of the processes, computationally costly simulations, and insufficiently robust simulations. The first challenge bars the use of provable derivative-based optimization algorithms. The latter two can either lead to difficult or impossible direct derivative-free optimization. To overcome these difficulties, we take a more indirect method to solving this problem by, first, generating an accurate set of algebraic surrogate models from the simulation then using derivative-based solvers to optimize the surrogate models. We developed a method that uses derivative-based and derivative-free optimization alongside machine learning and statistical techniques to generate the set of low-complexity surrogate models using data sampled from detailed simulations. The models are validated and improved through the use of derivative-free solvers to adaptively sample new simulation points. The resulting surrogate models can then be used in a superstructure-based process synthesis and solved using derivative-based methods to optimize carbon capture processes.