Nonlinear Model Predictive Control for Uranium Extraction-Scrubbing Operation in Spent Nuclear Fuel Treatment Process

TL;DR

This paper develops a nonlinear model predictive control strategy for uranium extraction in nuclear fuel reprocessing, improving accuracy, safety, and operational constraints management over traditional control methods.

Contribution

It introduces a set-point tracking NMPC tailored for the uranium extraction-scrubbing process, ensuring optimal operation and constraint satisfaction.

Findings

NMPC outperforms PID and open-loop controllers in accuracy

Effective constraint management and safety guarantees

Improved process stability and recovery from violations

Abstract

This paper addresses the particularities of the uranium extraction-scrubbing operation in a spent nuclear fuel treatment process (PUREX-Plutonium Uranium Refining by Extraction) through the use of set-point tracking MPC (Model Predictive Control). The presented controller uses the feed solution flow rate as the manipulated variable to control the saturation of the solvent at the extraction step. In addition, it guarantees not to loose uranium in the raffinates, and ensures equipment limitations during operation time. Simulation results show that the tracking NMPC effectively ensures accurate set point tracking and constraints guarantee. As a result, the system can be driven to its optimal working condition, avoid and recover from constraint violations. The control performance was compared with PID and openloop controllers.