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 modeling. 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 modeling 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 modeling and optimization. © 2018 American Institute of Chemical Engineers AIChE J, 65: 915–923, 2019

In the present study, a novel technique, which involves numerical computation of the mixing length of algae particles in raceway ponds, was used to evaluate the mixing process. A value of mixing length that is higher than the maximum streamwise distance (MSD) of algae cells indicates that the cells experienced an adequate turbulent mixing in the pond. A coupling methodology was adapted to map the pulsating effects of a 2D paddle wheel on a 3D raceway pond in this study. The turbulent mixing was examined based on the computations of mixing length, residence time, and algae cell distribution in the pond. The results revealed that the use of particle tracing methodology is an improved approach to define the mixing phenomenon more effectively. Moreover, the algae cell distribution aided in identifying the degree of mixing in terms of mixing length and residence time.

Microalgal biofuels have not yet achieved wide-spread commercialization, partially as a result of the complexities involved with designing and scaling up of their biosystems. The sparger design of a pilot-scale photobioreactor (120 L) was optimized to enable the scale-up of biofuel production. An integrated model coupling computational fluid dynamics and microalgal biofuel synthesis kinetics was used to simulate the biomass growth and novel biofuel production (i.e., bisabolene) in the photobioreactor. Bisabolene production from Chlamydomonas reinhardtii mutant was used as an example to test the proposed model. To select the optimal sparger configuration, a rigorous procedure was followed by examining the effects of sparger design parameters (number and diameter of sparger holes and gas flow rates) on spatially averaged bubble volume fraction, light intensity, friction velocity, power input, biomass concentration, and bisabolene production. The optimized sparger design increases the final biomass concentration by 18%, thereby facilitating the scaling up of biofuel production.