The increase in the use of biofuels raised new challenges to engineering problems. In this context, the optimization of chemical reactors, particularly bioreactors and photobioreactors, is crucial to improve the production of biofuels in a sustainable manner. This paper reports the development of an optimization method and its application to the design of a continuous flow bioreactor envisaged to be used in industrial fermentation processes. Mass and momentum conservation equations are simulated via CFD and specific a posteriori performance parameters, determined from the flow solution, are fed into a multiobjective evolutionary algorithm to obtain corrections to the parameters of the geometrical configuration of the reactor. This heuristics is iterated to obtain an optimized configuration vis-à-vis the flow aspects portrayed by the performance parameters, such as the shear stress and the residence time variations. An open source computer package (PyCFD-O) was developed to perform CFD simulations and the optimization processes automatically. First, it calls the pre-processor to generate the computational geometry and the mesh. Then it performs the simulations using OpenFOAM, calculates the output parameters and iterates the procedure. The PyCFD-O package has proved reliable and robust in a test case, a ∼1 m3 continuous fermentation reactor. The multiobjective optimization procedure actually corresponds to search for the Pareto frontier in the solution space characterized by its geometric parameters and the associated performance parameters (dispersion o residence times and shear stresses). Optimal design configurations were obtained representing the best tradeoff between antagonistic objectives, i.e. the so-called non-dominant solutions. 
Optimal industrial reactor design: development of a multiobjective optimization method based on a posteriori performance parameters calculated from CFD flow solutions