Integration of Process Design, Scheduling, and Control Via Model Based Multiparametric Programming

The conventional approach to assess the multiscale operational activities sequentially often leads to suboptimal solutions and even interruptions in the manufacturing process due to the inherent differences in the objectives of the individual constituent problems. In this work, integration of the traditionally isolated process design, scheduling, and control problems is investigated by introducing a multiparametric programming-based framework, where all decision layers are based on a single high fidelity model. The overall problem is dissected into two constituent parts, namely (i) design and control, and (ii) scheduling and control problems. The proposed framework was first assessed on these constituent subproblems, followed by the implementation on the overall problem. The fundamental steps of the framework consists of (i) developing design dependent offline control and scheduling strategies, and (ii) exact implementation of these offline rolling horizon strategies in a mixed-integer dynamic optimization problem for the optimal design. The design dependence of the offline operational strategies allows for the integrated problem to consider the design, scheduling, and control problems simultaneously. The proposed framework is showcased on (i) a binary distillation column for the separation of toluene and benzene, (ii) a system of two continuous stirred tank reactor, (iii) a small residential heat and power network, and (iv) two batch reactor systems. Furthermore, a novel algorithm for large scale multiparametric programming problems is proposed to solve the classes of problems frequently encountered as a result of the integration of rolling horizon strategies

Preview-based techniques for vehicle suspension control: a state-of-the-art review

Abstract Automotive suspension systems are key to ride comfort and handling performance enhancement. In the last decades semi-active and active suspension configurations have been the focus of intensive automotive engineering research, and have been implemented by the industry. The recent advances in road profile measurement and estimation systems make road-preview-based suspension control a viable solution for production vehicles. Despite the availability of a significant body of papers on the topic, the literature lacks a comprehensive and up-to-date survey on the variety of proposed techniques for suspension control with road preview, and the comparison of their effectiveness. To cover the gap, this literature review deals with the research conducted over the past decades on the topic of semi-active and active suspension controllers with road preview. The main formulations are reported for each control category, and the respective features are critically analysed, together with the most relevant performance indicators. The paper also discusses the effect of the road preview time on the resulting system performance, and identifies control development trends

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

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