Surrogate-based optimisation of process systems to recover resources from wastewater

TL;DR

This paper introduces a surrogate-based optimization framework for resource recovery in wastewater systems, integrating high-fidelity data and first principles models to optimize processes for energy and nutrient recovery.

Contribution

It presents a novel surrogate-based process synthesis methodology that combines black box simulations with first principles models within a superstructure optimization framework.

Findings

Significant potential to reduce environmental impacts of wastewater treatment.

Effective recovery of energy, nitrogen, and phosphorus demonstrated.

Methodology accounts for uncertainties in wastewater systems.

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.