Dynamic membranes are considered to be an attractive anti-fouling remedy for membrane filtration, because once fouled, they can be removed and reformed in-situ, thereby prolonging the support membrane???s lifetime. However, large-scale application of dynamic membranes has been limited due to the numerous formation parameters that influence their properties. This thesis provides better understanding of the mechanisms of the dynamic membrane formation process through fundamental formation and characterisation studies of dynamically formed titanium dioxide membranes in laboratory scale dead-end and crossflow systems. The dynamic membranes exhibited water fluxes ranging from 30-1147 L/m2h and dextran (500 kDa) rejections as high as 99.9%. Of the six formation parameters studied, the pH and constant flux conditions had the greatest influence on dynamic membrane properties. The pH affects dynamic membrane properties by changing particle aggregation prior to dynamic membrane formation, while constant flux conditions affect the drag force on particles during deposition thereby altering cake compressibility. The advantage of using the novel concept of constant flux formation over traditional constant pressure formation is that it enables greater control of particle deposition during dynamic membrane formation. Dextran rejection data also suggested the existence of a critical mass loading, above which dynamic membrane flux and rejection properties are reduced. This thesis also demonstrated the utility of a factorial design experiment for preliminary identification and evaluation of the critical factors affecting dynamic membrane formation, a method which could be invaluable for tailoring dynamic membranes for use in specific applications. In addition, cake removal data suggested that more than 80% of the dried cake could be removed providing a high potential for membrane regeneration. For the formation conditions studied, it was concluded that convection was the dominant mechanism governing particle transport during dynamic membrane formation. The fluxes and cake properties of the dynamic membranes were best described by the resistance-in-series model for simple dead-end microfiltration. Furthermore, the higher cake void fraction required to fit the experimental data (at low formation pressure or constant flux conditions) with model predictions suggested that the ratio of shear to convection was an important mechanistic parameter determining dynamic membrane properties.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2008. / Thesis Advisor(s): Royset, Johannes O. "September 2008." Description based on title screen as viewed on November 5, 2008. Includes bibliographical references (p. 37-38). Also available in print.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 92-101). Also available in electronic version.

The dynamic precipitation of ferrite during austenite deformation above the Ae3 temperature, was first reported by Yada et al. in the 1980s. These observations were later confirmed for a wide range of steel compositions and processing conditions. The aim of this work is to clarify the mechanism by which ferrite formation takes place above the Ae3 temperature. To that end, a simple model for ferrite growth as a result of the movement of disconnections under applied stress is established. This motion, opposed by the back stress, increases the Gibbs energy as a result of deformation of ferrite above Ae3. Preliminary calculation suggests that the dynamic transformation is possible near the Ae3 temperature for large stress. The results are discussed in terms of experiments using a deformation dilatometer. / Thesis / Master of Applied Science (MASc)

The basic oxygen furnace (BOF) is responsible for over 60% of the steel production worldwide. Despite being an old and intensively studied process, the complex dynamics of the phenomena taking place in the BOF still challenge researchers and fuel debates. Moreover, the severe operational conditions often prevent direct/continuous measurements of the states, making the process operation largely dependent on past experience and operators' knowledge. In this work, a dynamic model and optimization framework that can aid operators with the decision-making process are developed. Through several case studies, it is shown that the developed framework can potentially be used to reduce operational costs and increase productivity. / Thesis / Master of Chemical Engineering (MChE)

No description available.

Integrated robust planning systems that cover all levels of SC hierarchy have become increasingly important. Strategic, tactical, and operational SC plans should not be generated in isolation to avoid infeasible and conflicting decisions. On the other hand, enterprise planning systems contain over millions of records that are processed in each planning iteration. In such enterprises, the ability to generate robust plans is vital to their success because such plans can save the enterprise resources that may otherwise have to be reserved for likely SC plan changes. A robust SC plan is valid in all circumstances and does not need many corrections in the case of interruption, error, or disturbance. Such a reliable plan is proactive as well as reactive. Proactivity can be achieved by forecasting the future events and taking them into account in planning. Reactivity is a matter of agility, the capability of keeping track of system behaviour and capturing alarming signals from its environment, and the ability to respond quickly to the occurrence of an unforeseen event. Modeling such a system behaviour and providing solutions after such an event is extremely important for a SC. This study focuses on integrated supply chain planning with stochastic dynamic considerations. An integrated tactical-operational model is developed and then segregated into two sub-models which are solved iteratively. A SC is a stochastic dynamic system whose state changes over time often in an unpredictable manner. As a result, the customer demand is treated as an uncertain parameter and is handled by exploiting scenario-based stochastic programming. The increase in the number of scenarios makes it difficult to obtain quick and good solutions. As such, a Branch and Fix algorithm is developed to segregate the stochastic model into isolated islands so as to make the computationally intractable problem solvable. However not all the practitioners, planners, and managers are risk neutral. Some of them may be concerned about the risky extreme scenarios. In view of this, the robust optimization approach is also adopted in this thesis. Both the solution robustness and model robustness are taken into account in the tactical model. Futhermore, the dynamic behaviour of a SC system is handled with the concept of Model Predictive Control (MPC).

Integrated robust planning systems that cover all levels of SC hierarchy have become increasingly important. Strategic, tactical, and operational SC plans should not be generated in isolation to avoid infeasible and conflicting decisions. On the other hand, enterprise planning systems contain over millions of records that are processed in each planning iteration. In such enterprises, the ability to generate robust plans is vital to their success because such plans can save the enterprise resources that may otherwise have to be reserved for likely SC plan changes. A robust SC plan is valid in all circumstances and does not need many corrections in the case of interruption, error, or disturbance. Such a reliable plan is proactive as well as reactive. Proactivity can be achieved by forecasting the future events and taking them into account in planning. Reactivity is a matter of agility, the capability of keeping track of system behaviour and capturing alarming signals from its environment, and the ability to respond quickly to the occurrence of an unforeseen event. Modeling such a system behaviour and providing solutions after such an event is extremely important for a SC. This study focuses on integrated supply chain planning with stochastic dynamic considerations. An integrated tactical-operational model is developed and then segregated into two sub-models which are solved iteratively. A SC is a stochastic dynamic system whose state changes over time often in an unpredictable manner. As a result, the customer demand is treated as an uncertain parameter and is handled by exploiting scenario-based stochastic programming. The increase in the number of scenarios makes it difficult to obtain quick and good solutions. As such, a Branch and Fix algorithm is developed to segregate the stochastic model into isolated islands so as to make the computationally intractable problem solvable. However not all the practitioners, planners, and managers are risk neutral. Some of them may be concerned about the risky extreme scenarios. In view of this, the robust optimization approach is also adopted in this thesis. Both the solution robustness and model robustness are taken into account in the tactical model. Futhermore, the dynamic behaviour of a SC system is handled with the concept of Model Predictive Control (MPC).

Integrated robust planning systems that cover all levels of SC hierarchy have become increasingly important. Strategic, tactical, and operational SC plans should not be generated in isolation to avoid infeasible and conflicting decisions. On the other hand, enterprise planning systems contain over millions of records that are processed in each planning iteration. In such enterprises, the ability to generate robust plans is vital to their success because such plans can save the enterprise resources that may otherwise have to be reserved for likely SC plan changes. A robust SC plan is valid in all circumstances and does not need many corrections in the case of interruption, error, or disturbance. Such a reliable plan is proactive as well as reactive. Proactivity can be achieved by forecasting the future events and taking them into account in planning. Reactivity is a matter of agility, the capability of keeping track of system behaviour and capturing alarming signals from its environment, and the ability to respond quickly to the occurrence of an unforeseen event. Modeling such a system behaviour and providing solutions after such an event is extremely important for a SC. This study focuses on integrated supply chain planning with stochastic dynamic considerations. An integrated tactical-operational model is developed and then segregated into two sub-models which are solved iteratively. A SC is a stochastic dynamic system whose state changes over time often in an unpredictable manner. As a result, the customer demand is treated as an uncertain parameter and is handled by exploiting scenario-based stochastic programming. The increase in the number of scenarios makes it difficult to obtain quick and good solutions. As such, a Branch and Fix algorithm is developed to segregate the stochastic model into isolated islands so as to make the computationally intractable problem solvable. However not all the practitioners, planners, and managers are risk neutral. Some of them may be concerned about the risky extreme scenarios. In view of this, the robust optimization approach is also adopted in this thesis. Both the solution robustness and model robustness are taken into account in the tactical model. Futhermore, the dynamic behaviour of a SC system is handled with the concept of Model Predictive Control (MPC).

Integrated robust planning systems that cover all levels of SC hierarchy have become increasingly important. Strategic, tactical, and operational SC plans should not be generated in isolation to avoid infeasible and conflicting decisions. On the other hand, enterprise planning systems contain over millions of records that are processed in each planning iteration. In such enterprises, the ability to generate robust plans is vital to their success because such plans can save the enterprise resources that may otherwise have to be reserved for likely SC plan changes. A robust SC plan is valid in all circumstances and does not need many corrections in the case of interruption, error, or disturbance. Such a reliable plan is proactive as well as reactive. Proactivity can be achieved by forecasting the future events and taking them into account in planning. Reactivity is a matter of agility, the capability of keeping track of system behaviour and capturing alarming signals from its environment, and the ability to respond quickly to the occurrence of an unforeseen event. Modeling such a system behaviour and providing solutions after such an event is extremely important for a SC. This study focuses on integrated supply chain planning with stochastic dynamic considerations. An integrated tactical-operational model is developed and then segregated into two sub-models which are solved iteratively. A SC is a stochastic dynamic system whose state changes over time often in an unpredictable manner. As a result, the customer demand is treated as an uncertain parameter and is handled by exploiting scenario-based stochastic programming. The increase in the number of scenarios makes it difficult to obtain quick and good solutions. As such, a Branch and Fix algorithm is developed to segregate the stochastic model into isolated islands so as to make the computationally intractable problem solvable. However not all the practitioners, planners, and managers are risk neutral. Some of them may be concerned about the risky extreme scenarios. In view of this, the robust optimization approach is also adopted in this thesis. Both the solution robustness and model robustness are taken into account in the tactical model. Futhermore, the dynamic behaviour of a SC system is handled with the concept of Model Predictive Control (MPC).