Bicriteria optimization approach to analyze incorporation of biofuel and carbon capture technologies

AIChE Journal ◽  
2016 ◽  
Vol 62 (9) ◽  
pp. 3473-3483 ◽  
Author(s):  
Ali Öztürk ◽  
Metin Türkay
Keyword(s):  
Carbon Capture ◽  
Optimization Approach ◽  
Bicriteria Optimization

Surrogate-Assisted Modeling and Robust Optimization of a Micro Gas Turbine Plant With Carbon Capture

2019 ◽  
Author(s):  
Simone Giorgetti ◽  
Diederik Coppitters ◽  
Francesco Contino ◽  
Ward De Paepe ◽  
Laurent Bricteux ◽  
...  
Keyword(s):  
Robust Optimization ◽  
Gas Turbines ◽  
Carbon Capture ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Aspen Plus ◽  
Optimization Approach ◽  
Co2 Absorption ◽  
Exhaust Gas ◽  
Nsga Ii

Abstract 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. Micro Gas Turbines (mGTs) constitutes 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 post-combustion Carbon Capture (CC) on these energy systems. To reduce the CC energy penalty, Exhaust Gas Recirculation (EGR) can be applied to the mGTs increasing the CO2 content in the exhaust gas and reducing the mass flow rate of flue gas to be treated. As a result, a lower investment and operational cost of the CC unit can be achieved. In spite of 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 EGR has been coupled with an amine-based CC plant and simulated using the software Aspen Plus®. 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 Aspen Plus® data, to quickly find operating points of the plant at a very low computational cost. Using the validated surrogate model, a robust optimization using a Non-dominated Sorting Genetic Algorithm II (NSGA II) has been carried out, assessing the influence of each input uncertainty and varying several design variables. As a general result, the analysed power plant proves to be intrinsically very robust, even when the input variables are affected by strong uncertainties.

Surrogate-Assisted Modeling and Robust Optimization of a Micro Gas Turbine Plant With Carbon Capture

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Simone Giorgetti ◽  
Diederik Coppitters ◽  
Francesco Contino ◽  
Ward De Paepe ◽  
Laurent Bricteux ◽  
...  
Keyword(s):  
Robust Optimization ◽  
Stochastic Optimization ◽  
Carbon Capture ◽  
Electrical Power ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Optimization Approach ◽  
Co2 Absorption ◽  
Carbon Reduction ◽  
Input Variables

Abstract 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.

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