Surrogate-based optimisation of process systems to recover resources from wastewater
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ID: 282983
2023
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Abstract
Wastewater systems are transitioning towards integrative process systems to
recover multiple resources whilst simultaneously satisfying regulations on
final effluent quality. This work contributes to the literature by bringing a
systems-thinking approach to resource recovery from wastewater, harnessing
surrogate modelling and mathematical optimisation techniques to highlight
holistic process systems. A surrogate-based process synthesis methodology was
presented to harness high-fidelity data from black box process simulations,
embedding first principles models, within a superstructure optimisation
framework. Modelling tools were developed to facilitate tailored
derivative-free optimisation solutions widely applicable to black box
optimisation problems. The optimisation of a process system to recover energy
and nutrients from a brewery wastewater reveals significant scope to reduce the
environmental impacts of food and beverage production systems. Additionally,
the application demonstrates the capabilities of the modelling methodology to
highlight optimal processes to recover carbon, nitrogen, and phosphorous
resources whilst also accounting for uncertainties inherent to wastewater
systems.
| Reference Key |
guo2023surrogatebased
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|---|---|
| Authors | Alex Durkin; Miao Guo |
| Journal | arXiv |
| Year | 2023 |
| DOI |
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| URL | |
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