(554d) Superstructure Formulation and Optimization for Carbon Capture Process

Authors: 
Yuan, Z. - Presenter, Carnegie Mellon University
Sahinidis, N., Carnegie Mellon University
Miller, D. C., National Energy Technology Laboratory



Carbon capture, utilization, and storage technology is an essential route to achieving a meaningful reduction of global CO2 emissions in the context of continued fossil fuel use in the power sector. Under the Carbon Capture Simulation Initiative (CCSI) initiated by the Department of Energy, the proposed work mainly focuses on the optimal synthesis of solid sorbent based carbon capture process.  

In this work, a superstructure formulation for the optimal synthesis of a fluidized bed reactor based capture system has been established to seek the optimal layout and its corresponding optimal design/operation levels simultaneously. Since detailed first principle models of the reactors are computationally intractable for large scale superstructure optimization, the reactors are represented by a set of low complexity algebraic surrogate models generated by the Automated Learning of Algebraic Models for Optimization (ALAMO) [1].

The superstructure formulation includes limits on the maximum number of fluidized bed reactors in adsorber and regenerator series, detailed models of compressors, low-complexity algebraic models of reactors, first principles models for heat exchangers and mixers, and an objective to minimize the capital, labor, operating/maintenance costs together with the power cost for compressors and elevators, while achieving at least 90% capture target.

The superstructure formulation is a mixed-integer nonlinear programming (MINLP) problem, which is solved using the BARON global optimization system [2].  The solution provides interconnections and relevant design/operation levels, along with a selected subset of the postulated stages of adsorbers and regenerators.  

References

[1]. Cozad AL, Sahinidis NV, & Miller DC. Learning surrogate models for simulation-based optimization. In preparation.

[2]. Tawarmalani, M & Sahinidis N. V. A polyhedral branch-and-cut approach to global optimization. Mathematical Programming, 103, 225-249, 2005.

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