Market variation such as changing utility price or demand can lead to non-static operation of chemical plants, such as in cases where new objectives must be met, or varying operating conditions can be taken advantage of to increase operational profit. Integrated design and control (where both the design and operation are considered simultaneously when the design of the plant is being formulated) can be used to address the challenges that plants that operate under uncertainty may face. When considering demand uncertainty for a plant, not only must the different realizations of demand be considered, but how the plant transitions from one demand to another is also of interest. Ideally when transitioning, the plant must quickly and feasibly transition from one operating point to another. In the first study of this thesis we consider the benefit of taking into account the demand transitions a plant must undergo; compared to only considering the final operating states. In this study we examine an air separation unit (ASU) since in industrial practice ASUs can often be subject to demand uncertainty. Assessing the impact that an ASU design has on its dynamic response characteristics motivates us to include plant dynamics in the design formulation. Using a two-stage stochastic optimization framework, the optimal design parameters are found for a nitrogen plant operating under uncertain demand. Three design paradigms are explored and compared - a nominal steady-state design, a flexible design that maintains steady-state feasibility, and a dynamically operable design that enforces feasibility of dynamic transitions. The designs obtained are subjected to random demand changes to evaluate the expected economic return under transitions not directly designed for. From this study we see that when the dynamically operable design and the flexible design are both subject to dynamic transitions, the dynamically operable design in certain cases can provide more economic benefit during transition and at the final steady state. In the second study of this thesis we address an important issue that arises when uncertainty in the plant operation is captured by utilizing two-stage stochastic optimization. If the optimization formulation can see the uncertainty profile in its entirety, then it can make control moves and design decisions based on the fact that the optimization problem knows what operating conditions it transitions between. This may not however be a realistic assumption to operate under, as future uncertainty is unknown. Given lack of foresight into future uncertainties it is logical to currently operate at the the optimal steady-state. This poses a bilevel problem for the design and control of a plant because the feasible region is determined by an inner optimization. In this study the Karush-Kuhn-Tucker conditions are incorporated into a two-stage stochastic optimization formulation for a dynamic model of chemical plant to generate a design. This design is then compared to a design generated from an optimization formulation where future knowledge of uncertainty is assumed and it is seen that the former design can provide greater economic benefit. / Thesis / Master of Applied Science (MASc)

This dissertation focuses on the introduction and development of an integrated model-based design and optimization platform to solve the optimal design and optimal control, or hardware and software co-design, problem for hybrid electric propulsion systems. Specifically, the hybrid and plug-in hybrid electric powertrain systems with diesel and natural gas (NG) fueled compression ignition (CI) engines and large Li-ion battery energy storage system (ESS) for propelling a hybrid electric marine vessel are investigated. The combined design and control optimization of the hybrid propulsion system is formulated as a bi-level, nested optimization problem. The lower-level optimization applies dynamic programming (DP) to ensure optimal energy management for each feasible powertrain system design, and the upper-level global optimization aims at identifying the optimal sizes of key powertrain components for the powertrain system with optimized control. Recently, Li-ion batteries became a promising ESS technology for electrified transportation applications. However, these costly Li-ion battery ESSs contribute to a large portion of the powertrain electrification and hybridization costs and suffer a much shorter lifetime compared to other key powertrain components. Different battery performance modelling methods are reviewed to identify the appropriate degradation prediction approach. Using this approach and a large set of experimental data, the performance degradation and life prediction model of LiFePO4 type battery has been developed and validated. This model serves as the foundation for determining the optimal size of battery ESS and for optimal energy management in powertrain system control to achieve balanced reduction of fuel consumption and the extension of battery lifetime. In modelling and design of different hybrid electric marine propulsion systems, the life cycle cost (LCC) model of the cleaner, hybrid propulsion systems is introduced, considering the investment, replacement and operational costs of their major contributors. The costs of liquefied NG (LNG), diesel and electricity in the LCC model are collected from various sources, with a focus on present industrial price in British Columbia, Canada. The greenhouse gas (GHG) and criteria air pollutant (CAP) emissions from traditional diesel and cleaner NG-fueled engines with conventional and optimized hybrid electric powertrains are also evaluated. To solve the computational expensive nested optimization problem, a surrogate model-based (or metamodel-based) global optimization method is used. This advanced global optimization search algorithm uses the optimized Latin hypercube sampling (OLHS) to form the Kriging model and uses expected improvement (EI) online sampling criterion to refine the model to guide the search of global optimum through a much-reduced number of sample data points from the computationally intensive objective function. Solutions from the combined hybrid propulsion system design and control optimization are presented and discussed. This research has further improved the methodology of model-based design and optimization of hybrid electric marine propulsion systems to solve complicated co-design problems through more efficient approaches, and demonstrated the feasibility and benefits of the new methods through their applications to tugboat propulsion system design and control developments. The resulting hybrid propulsion system with NG engine and Li-ion battery ESS presents a more economical and environmentally friendly propulsion system design of the tugboat. This research has further improved the methodology of model-based design and optimization of hybrid electric marine propulsion systems to solve complicated co-design problems through more efficient approaches, and demonstrated the feasibility and benefits of the new methods through their applications to tugboat propulsion system design and control developments. Other main contributions include incorporating the battery performance degradation model to the powertrain size optimization and optimal energy management; performing a systematic design and optimization considering LCC of diesel and NG engines in the hybrid electric powertrains; and developing an effective method for the computational intensive powertrain co-design problem. / Graduate