Model predictive control of phthalic anhydride synthesis in a fixed-bed catalytic reactor via machine learning modeling

This work proposes a general framework for linking a state-of-the-art computational fluid dynamics (CFD) solver, ANSYS Fluent, and other computing platforms using the lock synchronization mechanism in an effort to extend the utilities of CFD solvers from strictly modeling and design to also control and optimization applications. To demonstrate the effectiveness of the proposed approach, a challenging control problem in chemical engineering, i.e., maximizing the product yield and suppressing the hot-spot temperature in a fixed-bed reactor (FBR) with a highly exothermic reaction, is considered. Specifically, phthalic anhydride (PA) synthesis is chosen for this investigation because of its industrial significance and its extreme high exothermicity. Initially, a high-fidelity two-dimensional axisymmetric heterogeneous CFD model for an industrial-scale FBR is developed in ANSYS Fluent. Next, the CFD model is used to explore a wide operating regime of the FBR to create a database, from which recurrent neural network and ensemble learning techniques are used to derive a homogeneous ensemble regression model using a state-of-the-art application program interface (API), i.e., Keras. Then, a model predictive control (MPC) formulation that is designed to drive the process output to the desired set-point and suppress the magnitude of the hot-spot temperature to avoid catalyst deactivation is developed using the ensemble regression model. Subsequently, the CFD model, the ensemble regression model and the MPC are combined to create a closed-loop system by linking ANSYS Fluent to SciPy (a Python library used for scientific computing) via a message-passing interface (MPI) with lock synchronization mechanism. Finally, the simulation data generated by the closed-loop system are used to demonstrate the robustness and effectiveness of the proposed approach.