Techno-economic Optimization of a Green-Field Post-Combustion CO₂ Capture Process Using Superstructure and Rate-Based Models

Abstract

A techno-economic optimization of a commercial-scale, amine-based, post-combustion CO 2 capture process is carried out. The most economically favorable process configuration, sizing and operating conditions are identified using a superstructure formulation. The superstructure has 12 288 possible process configurations and unit operations in the superstructure are described using rigorous, rate-based models. In order to simplify the optimization problem, the problem is decomposed and process simulations are explicitly handled in the process simulator. Optimization is performed externally using a genetic algorithm. The best found process configuration includes the absorber intercooling, the rich vapor recompression, and the cold solvent split. The result of this study is compared in terms of the cost of capture basis and shows 38% reduction on the annual operation cost, compared to the conventional amine-based CO 2 capture process. Moreover, the savings on the total annualized cost is ∼13%, which is an increase of 30% on the annualized investment cost resulting from additional unit operations.

Deterministic Global Optimization with Artificial Neural Networks Embedded

“…An analysis of the scaling of the approach also reveals that deep network require more CPU time for optimization than shallow networks with the same or even larger number of neurons. to, for instance, mixed integer problems where a process superstructure is optimized globally [16,82]. Here, unit operations, thermodynamic models and even dynamics could be emulated by ANNs allowing the utilization of data from different sources such as experiments, process simulations and life-cycle assessment tools (e.g., [83]).…”

Production of Oxymethylene Dimethyl Ethers from Hydrogen and Carbon Dioxide—Part II: Modeling and Analysis for OME3–5

“…Using PEM electrolyzers the overall efficiency is comparable as their specific energy consumption is about the same . Including also a thermal energy demand of 2.20 MJ/kg CO 2 for carbon capture from flue gas with a typical carbon dioxide mole fraction of 13% reduces the overall efficiency to 38%. This efficiency is rather low compared to OME 1 production and other e-fuels.…”

Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas‐to‐liquids

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