  To create efficient-high performing processes, one must find an optimal
design with its corresponding controller that ensures optimal operation in the
presence of uncertainty. When comparing different process designs, for the
comparison to be meaningful, each design must involve its optimal operation.
Therefore, to optimize a process' design, one must address design and control
simultaneously. For this, one can formulate a bilevel optimization problem,
with the design as the outer problem in the form of a mixed-integer nonlinear
program (MINLP) and a stochastic optimal control as the inner problem. This is
intractable by most approaches. In this paper we propose to compute the optimal
control using reinforcement learning, and then embed this controller into the
design problem. This allows to decouple the solution procedure, while having
the same optimal result as if solving the bilevel problem. The approach is
tested in two case studies and the performance of the controller is evaluated.
The case studies indicate that the proposed approach outperforms current
state-of-the-art simultaneous design and control strategies. This opens a new
avenue to address simultaneous design and control of engineering systems.
