  This paper addresses the particularities of adaptive optimal control of the
uranium extraction-scrubbing operation in the PUREX process. The process
dynamics are nonlinear, high dimensional, and have limited online measurements.
In addition, analysis and developments are based on a qualified simulation
program called PAREX, which was validated with laboratory and industrial data.
The control objective is to stabilize the process at a desired solvent
saturation level, guaranteeing constraints and handling disturbances. The
developed control strategy relies on optimization-based methods for computing
control inputs and estimates, i.e., Nonlinear Model Predictive Control (NMPC)
and Nonlinear Moving Horizon Estimation (NMHE). The designs of these two
associated algorithms are tailored for this process's particular dynamics and
are implemented through an enhanced Particle Swarm Optimization (PSO) to
guarantee constraint satisfaction. Software-in-the-loop simulations using PAREX
show that the designed control scheme effectively satisfies control objectives
and guarantees constraints during operation.
