  This paper addresses the particularities in optimal control of the uranium
extraction-scrubbing operation in the PUREX process. The control problem
requires optimally stabilizing the system at a desired solvent saturation
level, guaranteeing constraints, disturbance rejection, and adapting to set
point variations. A qualified simulator named PAREX was developed by the French
Alternative Energies and Atomic Energy Commission (CEA) to simulate
liquid-liquid extraction operations in the PUREX process. However, since the
mathematical model is complex and is described by a system of nonlinear, stiff,
high-dimensional differential-algebraic equations (DAE), applying optimal
control methods will lead to a large-scale nonlinear programming problem with a
huge computational burden. The solution we propose in this work is to train a
neural network to predict the process outputs using the measurement history.
This neural network architecture, which employs the long short-term memory
(LSTM), linear regression and logistic regression networks, allows reducing the
number of state variables, thus reducing the complexity of the optimization
problems in the control scheme. Furthermore, nonlinear model predictive control
(NMPC) and moving horizon estimation (MHE) problems are developed and solved
using the PSO (Particle Swarm Optimization) algorithm. Simulation results show
that the proposed adaptive optimal control scheme satisfies the requirements of
the control problem and provides promise for experimental testing.
