Simulation of NMPC for a Laboratory Adiabatic CSTR with an Exothermic Reaction

In this paper, we present nonlinear system identification and nonlinear model predictive control (NMPC) for a laboratory-scale adiabatic continuous stirred tank reactor (CSTR) with an exothermic reaction. We describe the equipment used in the process, and we present a process model based on first principles. We use a maximum likelihood estimation (MLE) approach based on the process model and the continuous-discrete extended Kalman filter (CD-EKF) to estimate four model parameters. The NMPC is based on the process model (with the estimated model parameters), the CD-EKF, and a nonlinear least-squares regulator with input (Tikhonov) and rate-of-movement regularization. We present simulations demonstrating that the NMPC (implemented in Python and C) can track any stable and unstable steady state for this system with multiple steady states in some operational regions.