Boiroux, Dimitri

Huusom, Jakob Kjøbsted

Jørgensen, John Bagterp

Nielsen, Marcus Krogh

Ritschel, Tobias Kasper Skovborg

Schroll-Fleischer, Eskild

Wahlgreen, Morten Ryberg

Wu, Hao

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.<br/

Online Research Database In Technology

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

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

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Online Research Database In Technology