Optimization of Chemical Processes Using Surrogate Models Based on a Kriging Interpolation

Quirante, Natalia; Javaloyes, Juan; Ruiz‐Femenia, Rubén

doi:10.1016/b978-0-444-63578-5.50025-6

Cited by 18 publications

(12 citation statements)

References 4 publications

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“…The sampling plan and associated output generated by the process simulation is used to train a Kriging model [23][24][25] using the pyKriging package [16,26,27]. See also [22,28,29] for more information about Kriging in chemical engineering applications. Kriging models were trained for the responses of interest i.e., the objective function (profit) and the constraint function (RVP), but also for total power and crude oil recoverable/export flow.…”

Section: Sampling and Surrogate Modelingmentioning

confidence: 99%

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization Using Surrogate Modeling and Evolutionary Algorithms

Andreasen¹

2020

ChemEngineering

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In this article, the optimization of a realistic oil and gas separation plant has been studied. Using Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with an evolutionary algorithm for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature at various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with a Reid Vapor Pressure (RVP) < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a unique optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator, apparently different, yet more or less equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.

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Section: Sampling and Surrogate Modelingmentioning

confidence: 99%

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization Using Surrogate Modeling and Evolutionary Algorithms

Andreasen¹

2020

ChemEngineering

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“…The sampling plan and recorded simulation model output is used to train a Kriging model [37][38][39] using the pyKriging package [27,40,41]. See also [31,34,42,43] for other examples and more information about Kriging in chemical engineering applications.…”

Section: Sampling and Surrogate Modellingmentioning

confidence: 99%

Optimisation of carbon capture from flue gas from a Waste-to-Energy plant using surrogate modelling and global optimisation

Andreasen

2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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The optimisation of Post Carbon Capture (PCC) from a Waste-to-Energy plant has been studied using Kriging surrogate models trained from a set of rigorous process simulations. The surrogate models allow fast and efficient calculation of model responses required for the optimisation of operating parameters. Optimisation is performed using Differential Evolution (DE) requiring a vast amount of function calculations (>1000) which would be extremely time consuming if done with a rigorous process simulation model. It is found that for meeting a CO2 removal efficiency of 85% for a flue gas containing 12.6 mole % CO2 and a reboiler temperature limited to max. 120 °C, a L/G ratio of approx. 2.2 (kg/kg) is optimal. This is accompanied by a stripper/regenerator pressure of 1.85 bara, a temperature of the flue gas at the lower bound, a temperature approach of the lean amine entering the absorber of 6.5 °C (to the flue gas temperature), and a temperature approach in the L/R heat exchanger of 5 °C. The optimal lean and rich amine loading is approx. 0.21 and 0.52 (mole CO2/mole MEA).

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“…The sampling plan and associated output generated by the process simulation (200 samples for each fluid) is used to train a Kriging model [20,21] for each fluid applied in the simulation using the pyKriging package [15]. See also [19,22,23] for more information about Kriging in chemical engineering applications. A Kriging model is trained for the responses of interest i.e.…”

Section: Design Of Computer Experiments and Surrogate Modelingmentioning

confidence: 99%

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

Andreasen

2019

Preprint

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In this article the optimization of a realistic oil and gas separation plant has been studied. Two different fluids are investigated and compared in terms of the optimization potential. Using Design of Computer Experiment (DACE) via Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with a variety of different evolutionary algorithms for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with an RVP < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a single optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator apparently two different, yet equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.

show abstract

Optimization of Chemical Processes Using Surrogate Models Based on a Kriging Interpolation

Cited by 18 publications

References 4 publications

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization Using Surrogate Modeling and Evolutionary Algorithms

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization Using Surrogate Modeling and Evolutionary Algorithms

Optimisation of carbon capture from flue gas from a Waste-to-Energy plant using surrogate modelling and global optimisation

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

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