On the design for flexibility of manufacturing systems : a stochiastic approach

Flexibility has emerged as one of the most strategic imperatives for company viability in today\u27s fast paced economy. This realization has stimulated extensive research efforts in this area most of which have focused mainly on defining flexibility and its attributes, the need for flexibility and how to measure it. Nevertheless, despite the considerable amount of publications regarding flexibility and its related subjects, insufficient attention has been given to the optimality of the design for flexibility and the inherent needs to meet uncertainty. Bridging this gap is the intent of this work. In this dissertation, developed analytical models are for the optimum design of flexible systems. The models introduced are based on extensions of the single period stochastic inventory model and real option theory to determine the optimum level of the various flexibility attributes that are required to meet the needs of a concern in an uncertain environment. Our premise stems from the fact that flexibility does not come at no cost. That is, when designing a system, the more flexibility built in it, the more the cost that will be incurred to maintain it. On the other hand, if the system is designed with low levels of flexibility, it may not be able to meet the uncertain demand, therefore causing loss of future revenue. The developed models, then, are applied to examples where data are obtained from machine tool manufacturers to show how to strike a balance between the two conflicting scenarios of over and under-flexible designs

Smart electric vehicle charging strategy in direct current microgrid

This thesis proposes novel electric vehicle (EV) charging strategies in DC microgrid (DCMG) for integrating network loads, EV charging/discharging and dispatchable generators (DGs) using droop control within DCMG. A novel two-stage optimization framework is deployed, which optimizes power flow in the network using droop control within DCMG and solves charging tasks with a modified Djistra algorithm. Charging tasks here are modeled as the shortest path problem considering system losses and battery degradation from the distribution system operator (DSO) and electric vehicles aggregator (EVA) respectively. Furthermore, a probabilistic distribution model is proposed to investigate the EV stochastic behaviours for a charging station including time-of-arrival (TOA), time-of-departure(TOD) and energy-to-be-charged (ETC) as well as the coupling characteristic between these parameters. Markov Chain Monte Carlo (MCMC) method is employed to establish a multi-dimension probability distribution for those load profiles and further tests show the scheme is suitable for decentralized computing of its low burn-in request, fast convergent and good parallel acceleration performance. Following this, a three-stage stochastic EV charging strategy is designed to plug the probabilistic distribution model into the optimization framework, which becomes the first stage of the framework. Subsequently, an optimal power flow (OPF) model in the DCMG is deployed where the previous deterministic model is deployed in the second stage which stage one and stage two are combined as a chance-constrained problem in stage three and solved as a random walk problem. Finally, this thesis investigates the value of EV integration in the DCMG. The results obtained show that with smart control of EV charging/discharging, not only EV charging requests can be satisfied, but also network performance like peak valley difference can be improved by ancillary services. Meanwhile, both system loss and battery degradation from DSO and EVA can be minimized.Open Acces

Design of Degradation-Conscious Control Schemes for Energy Storage Systems in Grid-connected Microgrid of High PV Generation

The integration of high PV-penetrated prosumers into the distribution system is not without challenges due to the uncertain PV power. This investigation examines a hierarchical HESS scheme that incorporates both distributed and centralized storages. The primary objective is to present a direct methodology for determining the capacities and control strategies of centralized and distributed hybrid storage scheme. Thus, the thesis proposes a degradation-conscious battery control for ESS scheme while the grid constraints are sufficiently met

Adipic Acid Sonocrystallization in Continuous Flow Microchannels

Crystallization is widely employed in the manufacture of pharmaceuticals during the intermediate and final stages of purification and separation. The process defines drug chemical purity and physical properties: crystal morphology, size distribution, habit and degree of perfection. Particulate pharmaceuticals are typically manufactured in conventional batch stirred tank crystallizers that are still inadequate with regard to process controllability and reproducibility of the final crystalline product. Variations in crystal characteristics are responsible for a wide range of pharmaceutical formulation problems, related for instance to bioavailability and the chemical and physical stability of drugs in their final dosage forms. This thesis explores the design of a novel crystallization approach which combines in an integrated unit continuous flow, microreactor technology, and ultrasound engineering. By exploiting the various benefits deriving from each technology, the thesis focuses on the experimental characterization of two different nucleation systems: a droplet-based system and a single-phase system. In the former, channel fouling is avoided using a carrier fluid to segment the crystallizing solution in droplets, thus avoiding the contact with the walls. In the latter channel blockage is prevented using larger channel geometries and employing higher flow rates. The flexibility of the developed setup also allows performing stochastic nucleation studies to estimate the nucleation kinetics under silent and sonicated conditions. The experiments reveal that very high nucleation rates, small crystal sizes, narrow size distributions and high crystal yields can be obtained with both setups when the crystallizing solution is exposed to high pressure field as compared to silent condition. It is concluded that transient cavitation of bubbles and its consequences are a significant mechanism for enhancing nucleation of crystals among several proposed in the literature. A preliminary study towards the development and design of a growth stage is finally performed. Flow pulsation is identified as a potential method to enhance radial mixing and narrow residence time distribution therefore achieving optimal conditions for uniform crystal growth. The results suggest that increasing values of Strouhal number as well as amplitude ratio improve axial dispersion. Helically coiled tubes are identified as potential structures to further improve fluid dynamic dispersion

An integrated model for asset reliability, risk and production efficiency management in subsea oil and gas operations

PhD ThesisThe global demand for energy has been predicted to rise by 56% between 2010 and 2040 due to industrialization and population growth. This continuous rise in energy demand has consequently prompted oil and gas firms to shift activities from onshore oil fields to tougher terrains such as shallow, deep, ultra-deep and arctic fields. Operations in these domains often require deployment of unconventional subsea assets and technology. Subsea assets when installed offshore are super-bombarded by marine elements and human factors which increase the risk of failure. Whilst many risk standards, asset integrity and reliability analysis models have been suggested by many previous researchers, there is a gap on the capability of predictive reliability models to simultaneously address the impact of corrosion inducing elements such as temperature, pressure, pH corrosion on material wear-out and failure. There is also a gap in the methodology for evaluation of capital expenditure, human factor risk elements and use of historical data to evaluate risk. This thesis aims to contribute original knowledge to help improve production assurance by developing an integrated model which addresses pump-pipe capital expenditure, asset risk and reliability in subsea systems. The key contributions of this research is the development of a practical model which links four sub-models on reliability analysis, asset capital cost, event risk severity analysis and subsea risk management implementation. Firstly, an accelerated reliability analysis model was developed by incorporating a corrosion covariate stress on Weibull model of OREDA data. This was applied on a subsea compression system to predict failure times. A second methodology was developed by enhancing Hubbert oil production forecast model, and using nodal analysis for asset capital cost analysis of a pump-pipe system and optimal selection of best option based on physical parameters such as pipeline diameter, power needs, pressure drop and velocity of fluid. Thirdly, a risk evaluation method based on the mathematical determinant of historical event magnitude, frequency and influencing factors was developed for estimating the severity of risk in a system. Finally, a survey is conducted on subsea engineers and the results along with the previous models were developed into an integrated assurance model for ensuring asset reliability and risk management in subsea operations. A guide is provided for subsea asset management with due consideration to both technical and operational perspectives. The operational requirements of a subsea system can be measured, analysed and improved using the mix of mathematical, computational, stochastic and logical frameworks recommended in this work

Occupant-Centric Simulation-Aided Building Design Theory, Application, and Case Studies

This book promotes occupants as a focal point for the design process

Occupant-Centric Modeling and Control for Low-Carbon and Resilient Communities

Global climate change and resulting frequent extreme weather events have highlighted the significance of energy sustainability and resilience. Communities, which refer to a group of buildings located geographically together, are important units for energy generation and consumption. Hence, the research of community energy sustainability and resilience has drawn much attention during the past decades. However, there remain many challenges surrounding community energy modeling and control to achieve the low-carbon and resilient goals. First, few tools are readily available for community-scale dynamic modeling and control-based studies. To address this gap, a community emulator was developed, which was designed to be hierarchical, scalable, and suitable for various applications. Data-driven stochastic building occupancy prediction was integrated into the emulator using logistic regression methods. Based on this work, we publicly released a library for net-zero energy community modeling using the object-oriented equation-based modeling language Modelica. Second, building load control informed by real-time carbon emission signals is underdeveloped as utility price-driven control has so far been dominant. To better facilitate community energy sustainability through decarbonization, we proposed four rule-based carbon emission responsive building control algorithms to reduce the annual carbon emissions through thermostatically controllable loads. The impact of carbon net-metering, as well as the evolvement of the future energy generation mix, is analyzed on top of both momentary and predictive rules. Based on the simulation results, the average annual household carbon emissions are decreased by 6.0% to 20.5% compared to the baseline. The average annual energy consumption is increased by less than 6.7% due to more clean hours over the year. The annual energy cost change lies between -4.1% and 3.4% on top of the baseline. Third, the enhancement of community resilience in an islanded mode through optimal operation strategies is often faced with computational challenges given the large number of controllable loads. To tackle this, we proposed a two-layer model predictive control-based resource allocation and load scheduling framework for community resilience enhancement. Within this framework, the community operator layer optimally allocates the available PV generation to each building, while the building agent layer optimally schedules controllable loads to minimize the unserved load ratio while maintaining thermal comfort. We found that the allocation process is mostly constrained by the building load flexibility. More specifically, buildings with less load flexibility tend to be allocated more PV generation than other buildings. Further, we identified the competitive relationship between the objectives of minimizing unserved load ratio and maximize comfort. Therefore, it is necessary for the building agent to have multi-objective optimization. Finally, to account for the uncertainties of occupant behavior and its impact on resilient community load scheduling, we developed a preference-aware scheduler for resilient communities. Stochastic occupant thermostat-changing behavior models were introduced into the deterministic load scheduling framework as a source of uncertainty. KRIs such as the unserved load ratio, the required battery size, and the unmet thermal preference hours were adopted to quantify the impacts. Uncertainties from occupants&rsquo; thermal preferences and their impact on load scheduling are then studied and addressed through chance constraints. Generally, the proposed controller performs better in terms of the unmet thermal preference hours and the battery sizes compared to the deterministic controller.</p