(585u) Optimal Design Strategy of an Aerated Stirred Tank Reactor Using Computational Fluid Dynamics and Bayesian Multi-Objective Optimization Combined Method

Optimal
Design Strategy of an Aerated Stirred Tank Reactor Using Computational Fluid
Dynamics and Bayesian Multi-Objective Optimization Combined Method

Seongeon Park, Minjun
Kim, Jonggeol Na, Jinjoo An and Chonghun Han*

School of
Chemical & Biological Engineering, Seoul National University, Seoul, South
Korea

ABSTRACT

Optimal reactor design is still a poorly understood problem
compared to process optimization problems, although the performance of a
reactor greatly influences the efficiency of whole process. This is partly due
to the inability of numerical reactor models to predict the non-ideal mixing
behaviors. Nowadays, computational fluid dynamics (CFD) has emerged as a
solution to overcome such limitations by reflecting the complex 3D
hydrodynamics inside a reactor.

However, the optimization problem involving CFD simulation
is difficult to solve, because the computational cost for runs of simulation is
very expensive, and gradient information which may guide the parameter search
is unavailable. Bayesian optimization (BO), a recently popularized
gradient-free black-box global optimization algorithm, is a very promising
approach for such problems. BO builds a surrogate model of the objective
function based on the data points already explored, and exploits information
from the model to decide where to search next.

In this research, a strategy to find the optimal design of
an aerated stirred tank reactor with two Rushton turbine impellers using CFD
and BO combined method is proposed. A multi-objective extension of BO was
employed in order to maximize the effective gas holdup and minimize the power
consumption simultaneously. Six optimization variables to define the
geometrical structure of the reactor were chosen: the aspect ratio of the tank,
the impeller diameter to the tank diameter ratio, the clearance of each
impeller to the tank height ratio, the gas sparger diameter to impeller
diameter ratio and the sparger clearance to the tank height ratio. The CFD
reactor model works as a black-box function of which the inputs are design
variables and the outputs are objective function values. The reactor structure
of Alves (2002) was chosen to be a base case and its experimental data were
used to validate the CFD reactor model.

As a result, the algorithm found the best solutions, which
comprise a Pareto front, within 50 iterations. Furthermore, the whole optimal
design process could be automated without any need of human decision practices.

REFERENCE

Alves, S. S., et al., "Experimental and
modelling study of gas dispersion in a double turbine stirred tank," Chemical Engineering
Science, 57(3), pp.
487-496 (2002)