Surrogate-based branch-and-bound algorithms for simulation-based black-box optimization

Zhai, Jianyuan; Boukouvala, Fani

doi:10.1007/s11081-022-09740-5

Cited by 4 publications

(2 citation statements)

References 64 publications

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“…To choose a reasonable set of operating parameters to simulate at the 25,344 input conditions, as listed in Table S5, we first developed an optimized set of operating parameters at standard ambient conditions ( T amb = 298 K, P amb = 105 kPa, C normalC O 2 , a m b = 400 ppm) at 60% RH. This optimization used a python data-driven surrogate-based branch-and-bound (DDSBB) optimization package developed by Zhai and Boukouvala. , This approach treats gPROMS simulations as a black-box function and fits the output with surrogate models as well as convex underestimates to determine the global optimum. Information on the uniform sampling inputs used with gPROMs is available as a supplementary file, and further information about the DDSBB calculations is given in the Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

“…This optimization used a python data-driven surrogate-based branch-and-bound (DDSBB) optimization package developed by Zhai and Boukouvala. 42 , 43 This approach treats gPROMS simulations as a black-box function and fits the output with surrogate models as well as convex underestimates to determine the global optimum. Information on the uniform sampling inputs used with gPROMs is available as a supplementary file, and further information about the DDSBB calculations is given in the Supporting Information .…”

Section: Methodsmentioning

confidence: 99%

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Assessing Impacts of Atmospheric Conditions on Efficiency and Siting of Large-Scale Direct Air Capture Facilities

Cai,

Coletti,

Sholl

et al. 2024

JACS Au

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The cost and efficiency of direct air capture (DAC) of carbon dioxide (CO 2 ) will be decisive in determining whether this technology can play a large role in decarbonization. To probe the role of meteorological conditions on DAC we examine, at 1 × 1°resolution for the continental United States (U.S.), the impacts of temperature, humidity, atmospheric pressure, and CO 2 concentration for a representative amine-based adsorption process. Spatial and temporal variations in atmospheric pressure and CO 2 concentration lead to strong variations in the CO 2 available in ambient air across the U.S. The specific DAC process that we examine is described by a process model that accounts for both temperature and humidity. A process that assumes the same operating choices at all locations in the continental U.S. shows strong variations in performance, with the most influential variables being the H 2 O gas phase volume fraction and temperature, both of which are negatively correlated with DAC productivity for the specific process that we consider. The process also shows a moderate positive correlation of ambient CO 2 with productivity and recovery. We show that optimizing the DAC process at seven representative locations to reflect temporal and spatial variations in ambient conditions significantly improves the process performance and, more importantly, would lead to different choices in the sites for the best performance than models based on a single set of process conditions. Our work provides a framework for assessing spatial variations in DAC performance that could be applied to any DAC process and indicates that these variations will have important implications in optimizing and siting DAC facilities.

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