Application of Parametric Optimization and Control in The Smart Manufacturing of Hydrogen Systems

Abstract

The main objective of this dissertation is to develop and deploy and test explicit model predictive control feedback strategy on hydrogen systems using the PARametric Optimization and Control framework (PAROC). In line with the Smart Manufacturing initiative, our endeavor explores a new model based embedded control architecture that can enable the flexibility and adaptability of hydrogen process system to artificial intelligent algorithms. First a hydrogen supply chain model is developed to identify sustainable hydrogen technologies and then explicit model predictive control is developed using the PAROC framework. Both in silico and laboratory implementations are considered towards a smart prototype system application and demonstration. In silico PAROC considerations include the development and validation of high-fidelity models based on which the application of the multi-parametric programming techniques results in the derivation of explicit optimal feedback design strategy through the solution of a receding horizon optimization problem formulation. The derived explicit parametric control strategy is validated first in silico and then in real-time. Thus, laboratory scale experimental prototypes have been designed and built. The prototypes include: (i) a metal hydride hydrogen storage system (MHSS) and (ii) a PEM Water Electrolysis (PEMWE). The MHSS is designed to replicate the refueling process of a Fuel Cell Electric Vehicle (FCEV) in a hydrogen gas station while the PEMWE is designed as a module in a large scale modular hydrogen production process. Integration of the explicit MPC feedback control strategy and the online implementation on the prototype systems create smart hydrogen energy technologies. Both prototypes are tested using the explicit model predictive control strategies developed and the results obtained from the real-time implementation of the explicit feedback strategy demonstrates the potential of the proposed strategy and effective control design that meets the desired control objectives.