Model Predictive Control Embedding a Parallel Hybrid Modeling Strategy

This work addresses the problem of implementing a model predictive control (MPC) scheme that embeds a parallel hybrid subspace model as the predictive component of the control strategy. The hybrid model considered here is inspired by the framework proposed by Ghosh et al. ( Hybrid Modeling Approach Integrating First-Principles Models with Subspace Identification. Ind. Eng. Chem. Res. 2019, 58, 13533−13543 ), but it is adapted to make it amenable to online control. In particular, the framework uses a first-principles model and a subspace-based residual model (built with error between the process measurement data and the first-principles output of historical batches) in a parallel fashion. The present manuscript adapts this framework in a way that retains the linearity of the model utilized within the MPC. This is achieved by first building a subspace model (built with output data of the first-principles model) and then appending it with the residual model to have the same parallel hybrid model structure. This linear hybrid MPC is applied on a seeded batch crystallization process to reduce the volume of fines or crystals generated due to nucleation during the crystallization process, while maintaining a desired product quality at batch termination. The closed-loop results using the proposed control methodology are compared with a purely data-driven subspace-based model predictive controller.