Utilization of Integer Programming for Scheduling Maintenance at Nuclear Power Plants

This thesis develops a thought that naturally explores three specific motifs for solving the complexities of scheduling maintenance at Nuclear Power Plants (NPP). The first chapter of this paper will develop the initial thought around creating a schedule for a given work week, including all the various constraints inherent to this problem. Such constraints include but are not limited to personnel availability, allowable component out-of-service time, and the Plant Risk Assessment. The objective function being to minimize the total cost of worker’s compensation for that given week. The second chapter addresses the question of whether this simple schedule can be implemented with a long time horizon as the goal. This section delves into the concept of utilizing maintenance task frequencies and extended preventive maintenance frequencies to once again minimize the objective function of cost due to compensation. The third chapter focuses on the ability of the program to respond to adaptive circumstances. One major obstacle in running any large commercial facility is unplanned downtime of required systems or components. Simulating failures of certain components that shorten the overall allowable out-of-service time, the program will be required to still minimize the objective function while navigating these changing timelines

Multi-objective optimization for time-based preventive maintenance within the transport network: a review

Preventive maintenance in transportation is essential not only to safeguard billions in business and infrastructure investment, but also to guarantee safety, reliability and efficacy within the network. Government, industry and society have been increasingly recognising the importance of keeping transport units condition well-preserved. The challenge, however, is to achieve optimal performance of the existing transport systems within acceptable costs, effective workforce use and minimum disruption. Those are generally conflicting objectives. Multi-objective optimisation approaches have served as powerful tools to assist stakeholders to properly deploy preventive maintenance in industry. In this study, we review the research conducted in the application of multi-objective optimisation for preventive maintenance in transport-related activities. We focus on time-based preventive maintenance for production, infrastructure, rail and energy providers. In our review, we are interested in aspects such as the types of problems addressed, the existing objectives, the approaches to solutions, and how the outcomes obtained support decision

Markov-based performance evaluation and availability optimization of the boiler–furnace system in coal-fired thermal power plant using PSO

The appropriate maintenance strategy is essential for maintaining the thermal power plant highly reliable. The thermal power plant is a complex system that consists of various subsystems connected either in series or parallel configuration. The boiler–furnace (BF) system is one of the most critical subsystems of the thermal power plant. This paper presents availability based simulation modeling of the boiler–furnace system of thermal power plant with capacity (500MW). The Markov based simulation model of the system is developed for performance analysis. The differential equations are derived from a transition diagram representing various states with full working capacity, reduced capacity, and failed state. The normalizing condition is used for solving the differential equations. Furthermore, the performance of the system is analyzed for a possible combination of failure rate and repair rate, which revealed that failure of the boiler drum affects the system availability at most, and the failure of reheater affects the availability at least. Based on the criticality ranking, the maintenance priority has been provided for the system.The availability of the boiler–furnace system is optimized using particle swarm optimization method by varying the number of particles. The study results revealed that the maximum system availability level of 99.9845% is obtained. In addition, the optimized failure rate and repair rate parameters of the subsystem are used for suggesting an appropriate maintenance strategy for the boiler–furnace system of the plant. The finding of the study assisted the decision-makers in planning the maintenance activity as per the criticality level of subsystems for allocating the resources

Nuclear Power

The world of the twenty first century is an energy consuming society. Due to increasing population and living standards, each year the world requires more energy and new efficient systems for delivering it. Furthermore, the new systems must be inherently safe and environmentally benign. These realities of today's world are among the reasons that lead to serious interest in deploying nuclear power as a sustainable energy source. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. The goal of the book is to show the current state-of-the-art in the covered technical areas as well as to demonstrate how general engineering principles and methods can be applied to nuclear power systems

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems

Digital-Twins towards Cyber-Physical Systems: A Brief Survey

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS

Market and Economic Modelling of the Intelligent Grid: End of Year Report 2009

The overall goal of Project 2 has been to provide a comprehensive understanding of the impacts of distributed energy (DG) on the Australian Electricity System. The research team at the UQ Energy Economics and Management Group (EEMG) has constructed a variety of sophisticated models to analyse the various impacts of significant increases in DG. These models stress that the spatial configuration of the grid really matters - this has tended to be neglected in economic discussions of the costs of DG relative to conventional, centralized power generation. The modelling also makes it clear that efficient storage systems will often be critical in solving transient stability problems on the grid as we move to the greater provision of renewable DG. We show that DG can help to defer of transmission investments in certain conditions. The existing grid structure was constructed with different priorities in mind and we show that its replacement can come at a prohibitive cost unless the capability of the local grid to accommodate DG is assessed very carefully.Distributed Generation. Energy Economics, Electricity Markets, Renewable Energy

A Smart Grid Approach to Sustainable Power System Integration

Many factors can be identified for faster incorporation of renewable energy resources to displace the traditional fossil fuel energy sources. These factors are divided into three different aspects. First is the rapid decline of the cost of renewable energy sources and their associated components. The second factor can be attributed to the increasing pressure to transition from fossil-fuel energy sources which have detrimental environmental effects towards more sustainable energy source. A third aspect can be introduced in countries which are blessed with an enormous amount of fossil fuel resources, where the preservation of these limited natural resources is of paramount importance to the country that holds it. The dissertation includes the Kingdom of Saudi Arabia as the primary case study. However, the algorithm developed is applicable for other geographical locations which share similarities to the kingdom. The kingdom is considered to be one of the countries with an abundance of fossil-fuel reserves. The unique features of Saudi Arabia are primarily the availability of solar radiation and wind speed as well as high percentage of electrical loads which can be controlled such as energy-intensive desalination plants. This feature, in particular, provides a significant driver for renewables to penetrate the electricity generation mixture. With loads that are deferrable, the issue of renewable sources variability can be mitigated and reduced with an optimized operation strategy. Therefore, the research tends to define and model electrical loads by how susceptible they are to the time of service. The types of loads considered are summarized as non-deferrable such as typical electrical loads in which the demand must be satisfied instantly, semi-deferrable loads which they share the same features as the non-deferrable, however, a storage medium is available to store energy products for later usage. This category of loads is represented by a water desalination plant with a water tank storage. The final load model is the fully deferrable load which is flexible in regarding time of service, and this type of load can be represented by an industrial production factory, such as a steel or aluminum plants. The concept of value storage is introduced, where energy can be stored in different forms which are quite different from a typical storage component (i.e., batteries). The justification to start increasing the penetration of renewable sources into the existing grid in countries which have abundant fossil fuel might not be evident. However, the dissertation provides both economical as well as environmental justifications and incentives to approach more sustainable energy sources. The economical and technical evaluation is referred to as the Generation Expansion Planning (GEP). This type of problem is associated with high complexity and non-linearity. Therefore, computational intelligence based optimization methods are used to resolve these issues. Heuristic optimization methodologies are utilized to solve the developed problem which provides a fixable approach to solve optimization problems