1 research outputs found
Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design
Identifying optimal photobioreactor configurations and process operating conditions is
critical to industrialize microalgae-derived biorenewables. Traditionally, this was addressed
by testing numerous design scenarios from integrated physical models coupling
computational fluid dynamics and kinetic modelling. However, this approach presents
computational intractability and numerical instabilities when simulating large-scale systems,
causing time-intensive computing efforts and infeasibility in mathematical optimization.
Therefore, we propose an innovative data-driven surrogate modelling framework which
considerably reduces computing time from months to days by exploiting state-of-the-art deep
learning technology. The framework built upon a few simulated results from the physical
model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then
adopts a hybrid stochastic optimization algorithm to explore untested processes and find
optimal solutions. Through verification, this framework was demonstrated to have
comparable accuracy to the physical model. Moreover, multi-objective optimization was
incorporated to generate a Pareto-frontier for decision-making, advancing its applications in
complex biosystems modelling and optimization