Analysis, design and optimization of offshore power system network

Ph.DDOCTOR OF PHILOSOPH

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Mathematical Models of Seaside Operations in Container Ports and their Solution

Operational Research and Optimization are fundamental disciplines which, for decades, provided the real-world with tools for solving practical problems. Many such problems arise in container ports. Container terminals are important assets in modern economies. They constitute an important means of distributing goods made overseas to domestic markets in most countries. They are expensive to build and difficult to operate. We describe here some of the main operations which are faced daily by decision makers at those facilities. Decision makers often use Operational Research and Optimization tools to run these operations effectively. In this thesis, we focus on seaside operations which can be divided into three main problems: 1- the Berth Allocation Problem (BAP), 2- the Quay Crane Assignment Problem (QCAP), 3- the Quay Crane Scheduling Problem (QCSP). Each one of the above is a complex optimization problem in its own right. However, solving them individually without the consideration of the others may lead to overall suboptimal solutions. For this reason we will investigate the pairwise combinations of these problems and their total integration In addition, several important factors that affected on the final solution. The main contributions of this study are modelling and solving of the: 1- Robust berth allocation problem (RBAP): a new efficient mathematical model is formulated and a hybrid algorithm based on Branch-and-Cut and the Genetic Algorithm is used to find optimal or near optimal solutions for large scale instances in reasonable time. 2- Quay crane assignment and quay crane scheduling problem (QCASP): a new mathematical model is built to simultaneously solve QCASP and a heuristic based on the Genetic Algorithm is developed to find solutions to realistic instances in reasonable time. 3- Berth allocation, quay crane assignment and quay crane scheduling problem (BACASP): an aggregate model for all three seaside operations is proposed and to solve realistic instances of the problem, an adapted variant of the Genetic Algorithm is implemented. Keywords: berth allocation; quay crane assignment; quay crane scheduling; terminal operations; genetic algorith

Assessing the suitability of ship design for human factors issues associated with evacuation and normal operations

Evaluating ship layout for human factors (HF) issues using simulation software such as maritimeEXODUS can be a long and complex process. The analysis requires the identification of relevant evaluation scenarios; encompassing evacuation and normal operations; the development of appropriate measures which can be used to gauge the performance of crew and vessel and finally; the interpretation of considerable simulation data. In this paper we present a systematic and transparent methodology for assessing the HF performance of ship design which is both discriminating and diagnostic. The methodology is demonstrated using two variants of a hypothetical naval ship

Methodology for managing shipbuilding projectby integrated optimality

PhD ThesisSmall to medium shipyards in developing shipbuilding countries face a persistent challenge to contain project cost and deadline due mainly to the ongoing development in facility and assorted product types. A methodology has been proposed to optimize project activities at the global level of project planning based on strength of dependencies between activities and subsequent production units at the local level. To achieve an optimal performance for enhanced competitiveness, both the global and local level of shipbuilding processes must be addressed. This integrated optimization model first uses Dependency Structure Matrix (DSM) to derive an optimal sequence of project activities based on Triangularization algorithm. Once optimality of project activities in the global level is realized then further optimization is applied to the local levels, which are the corresponding production processes of already optimized project activities. A robust optimization tool, Response Surface Method (RSM), is applied to ascertain optimum setting of various factors and resources at the production activities. Data from a South Asian shipyard has been applied to validate the fitness of the proposed method. Project data and computer simulated data are combined to carry out experiments according to the suggested layout of Design of Experiments (DOE). With the application of this model, it is possible to study the bottleneck dynamics of the production process. An optimum output of the yard, thus, may be achieved by the integrated optimization of project activities and corresponding production processes with respect to resource allocation. Therefore, this research may have a useful significance towards the improvement in shipbuilding project management

The impact of automation on the efficiency and cost effectiveness of the quayside and container yard cranes and the selection decision for the yard operating systems

This research evaluates the impact of automated and semi-automated devices on the process of loading, discharging, stacking and un-stacking of containers using Quayside Cranes (QSCs), Straddle Carriers (SCs), Rubber Tyred Gantry cranes (RTGs) and Rail Mounted Gantry cranes (RMGs) in container terminals. The emphasis of study is on the assessment of performance and cost effectiveness of the existing automated quayside and yard cranes. The study in this thesis examines the economic implications of reducing QSCs' cycle-times brought about by automatic features installed on the post-Panamax cranes. It demonstrates that a considerable increase in the productivity of QSCs is related directly or indirectly to an expected reduction of crane cycle-times. The concept offered by the proposed improvements distinguishes between the traditional system of loading and discharging of containers and the automated methods. It implies that automation devices installed on conventional QSCs significantly reduce the total turnaroundtime and hence the cost of containerships' waiting-times. It argues, however, that there should be a balance between the cost of containerships' waiting-times and the cost of automated berths' unproductive-times (idle-times). This study uses the elements of queuing theories and proposes a novel break-even method for calculating such a balance. The number of container Ground Slots (GSs) and the annual throughput of container terminals expressed in Twenty-foot Equivalent Units (TEUs) have been used as the efficiency and performance measure for many years. The study in this thesis introduces appropriate container yard design layouts and provides a generic model for calculating the annual throughput for container terminals using semiautomated SC and RTG and automated and semi-automated RMG operating systems. The throughput model proposed in this study incorporates the dynamic nature, size, type and capacity of the automated container yard operating systems and the average dwell-times, transhipment ratio, accessibility and stacking height of the containers as the salient factors in determining a container terminal throughput. Further, this thesis analyses the concept of cost functions for container yard operating systems proposed. It develops a generic cost-based model that provides the basis for a pair-wise comparison, analysis and evaluation of the economic efficiency and effectiveness of automated and semi-automated container yard stacking cranes and helps to make rational decisions. This study proposes a Multiple Attribute Decision-Making (MADM) method for evaluating and selecting the best container yard operating system amongst alternatives by examining the most important operating criteria involved. The MADM method proposed enables a decision-maker to study complex problems and allows consideration of qualitative and qualitative attributes that are heterogeneous in nature. An Analytical Hierarchy Process (AHP) technique has been employed as a weighting method to solve the MADM problem. The AHP allows for the decomposition of decision problem into a hierarchical order and enables a pair-wise comparison of the attributes and alternatives. The results of the AHP analysis provide the basis for a pair-wise comparison, judgement and selection of the best automated or semi-automated container yard operating system

Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri