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2019-11-28 - Article/Dans un journal avec peer-review - Anglais - 8 page(s)

Giorgetti Simone, Coppitters Diederik , Contino francesco, De Paepe Ward , Bricteux Laurent , Aversano Gianmarco, Parente Alessandro, "Surrogate-Assisted Modeling and Robust Optimization of a micro Gas Turbine Plant with Carbon Capture" in Journal of Engineering for Gas Turbines and Power, 142, 1, 011010 , GTP-19-1326

  • Edition : American Society of Mechanical Engineers (NY)
  • Codes CREF : Recherche énergétique (DI2290), Thermodynamique appliquée (DI2210), Combustion (DI2212), Turbines a gaz (DI2223)
  • Unités de recherche UMONS : Thermique et Combustion (F704)
  • Instituts UMONS : Institut de Recherche en Energétique (Energie)
Texte intégral :

Abstract(s) :

(Anglais) The growing share of wind and solar power in the total energy mix has caused severe problems in balancing the electrical power production. Consequently, in the future, all fossil fuel-based electricity generation will need to be run on a completely flexible basis. Microgas turbines (mGTs) constitute a mature technology which can offer such flexibility. Even though their greenhouse gas emissions are already very low, stringent carbon reduction targets will require them to be completely carbon neutral: this constraint implies the adoption of postcombustion carbon capture (CC) on these energy systems. Despite this attractive solution, an in-depth study along with a robust optimization of this system has not yet been carried out. Hence, in this paper, a typical mGT with exhaust gas recirculation has been coupled with an amine-based CC plant and simulated using the software aspenplus. A rigorous rate-based simulation of the CO2 absorption and desorption in the CC unit offers an accurate prediction; however, its time complexity and convergence difficulty are severe limitations for a stochastic optimization. Therefore, a surrogate-based optimization approach has been used, which makes use of a Gaussian process regression (GPR) model, trained using the aspenplus data, to quickly find operating points of the plant at a very low computational cost. Using the validated surrogate model, a stochastic optimization has been carried out. As a general result, the analyzed power plant proves to be intrinsically very robust, even when the input variables are affected by strong uncertainties.

Identifiants :
  • DOI : https://doi.org/10.1115/1.4044491