Use, Operation and Maintenance of Renewable Energy Systems:Experiences and Future Approaches

The aim of this book is to put the reader in contact with real experiences, current and future trends in the context of the use, exploitation and maintenance of renewable energy systems around the world. Today the constant increase of production plants of renewable energy is guided by important social, economical, environmental and technical considerations. The substitution of traditional methods of energy production is a challenge in the current context. New strategies of exploitation, new uses of energy and new maintenance procedures are emerging naturally as isolated actions for solving the integration of these new aspects in the current systems of energy production. This book puts together different experiences in order to be a valuable instrument of reference to take into account when a system of renewable energy production is in operation

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

Artificial intelligence for digital twins in energy systems and turbomachinery: development of machine learning frameworks for design, optimization and maintenance

The expression Industry4.0 identifies a new industrial paradigm that includes the development of Cyber-Physical Systems (CPS) and Digital Twins promoting the use of Big-Data, Internet of Things (IoT) and Artificial Intelligence (AI) tools. Digital Twins aims to build a dynamic environment in which, with the help of vertical, horizontal and end-to-end integration among industrial processes, smart technologies can communicate and exchange data to analyze and solve production problems, increase productivity and provide cost, time and energy savings. Specifically in the energy systems field, the introduction of AI technologies can lead to significant improvements in both machine design and optimization and maintenance procedures. Over the past decade, data from engineering processes have grown in scale. In fact, the use of more technologically sophisticated sensors and the increase in available computing power have enabled both experimental measurements and highresolution numerical simulations, making available an enormous amount of data on the performance of energy systems. Therefore, to build a Digital Twin model capable of exploring these unorganized data pools collected from massive and heterogeneous resources, new Artificial Intelligence and Machine Learning strategies need to be developed. In light of the exponential growth in the use of smart technologies in manufacturing processes, this thesis aims at enhancing traditional approaches to the design, analysis, and optimization phases of turbomachinery and energy systems, which today are still predominantly based on empirical procedures or computationally intensive CFD-based optimizations. This improvement is made possible by the implementation of Digital Twins models, which, being based primarily on the use of Machine Learning that exploits performance Big-Data collected from energy systems, are acknowledged as crucial technologies to remain competitive in the dynamic energy production landscape. The introduction of Digital Twin models changes the overall structure of design and maintenance approaches and results in modern support tools that facilitate real-time informed decision making. In addition, the introduction of supervised learning algorithms facilitates the exploration of the design space by providing easy-to-run analytical models, which can also be used as cost functions in multi-objective optimization problems, avoiding the need for time-consuming numerical simulations or experimental campaings. Unsupervised learning methods can be applied, for example, to extract new insights from turbomachinery performance data and improve designers’ understanding of blade-flow interaction. Alternatively, Artificial Intelligence frameworks can be developed for Condition-Based Maintenance, allowing the transition from preventive to predictive maintenance. This thesis can be conceptually divided into two parts. The first reviews the state of the art of Cyber-Physical Systems and Digital Twins, highlighting the crucial role of Artificial Intelligence in supporting informed decision making during the design, optimization, and maintenance phases of energy systems. The second part covers the development of Machine Learning strategies to improve the classical approach to turbomachinery design and maintenance strategies for energy systems by exploiting data from numerical simulations, experimental campaigns, and sensor datasets (SCADA). The different Machine Learning approaches adopted include clustering algorithms, regression algorithms and dimensionality reduction techniques: Autoencoder and Principal Component Analysis. A first work shows the potential of unsupervised learning approaches (clustering algorithms) in exploring a Design of Experiment of 76 numerical simulations for turbomachinery design purposes. The second work takes advantage of a nonsequential experimental dataset, measured on a rotating turbine rig characterized by 48 blades divided into 7 sectors that share the same baseline rotor geometry but have different tip designs, to infer and dissect the causal relationship among different tip geometries and unsteady aero-thermodynamic performance via a novel Machine-Learning procedure based on dimensionality reduction techniques. The last application proposes a new anomaly detection framework for gensets in DH networks, based on SCADA data that exploits and compares the performance of regression algorithms such as XGBoost and Multi-layer Perceptron

Monitoring of the piston ring-pack and cylinder liner interface in diesel engines through acoustic emission measurements

Investigation of novel condition monitoring systems for diesel engines has received much recent attention due to the increasing demands placed upon engine components and the limitations of conventional techniques. This thesis documents experimental research conducted to assess the monitoring capabilities of Acoustic Emission (AE) analysis. In particular it focuses on the possibility of monitoring the piston ring-pack and cylinder liner interface, a critical engine sub-system for which there are currently few practical monitoring options. A series of experiments were performed on large, two-stroke and small, four-stroke diesel engines. Tests under normal operating conditions developed a detailed understanding of typical AE generation in terms of both the source mechanisms and the characteristics of the resulting activity. This was supplemented by specific tests to investigate possible AE generation at the ring-pack/liner interface. For instance, for the small engines measures were taken to remove known AE sources in order to accentuate any activity originating at the interface whilst for the large engines the interfacial conditions were purposely deteriorated through the removal of the lubricating oil supply to one cylinder. Interpretation of the results was based mainly upon comparisons with published work encompassing both the expected ring-pack behaviour and AE generation from tribological processes. This provided a strong indication that the source of the ring-pack/liner AE activity was the boundary frictional losses. The ability to monitor this process may be of significant benefit to engine operators as it enhances the diagnostic information currently available and may be incorporated into predictive maintenance strategies. A further diagnostic technique considered was the possibility of using AE parameters combined with information of crankshaft speed fluctuations to evaluate engine balance and identify underperforming cylinders.EU Competitive and Sustainable Growth Programme, Project no: GRD2-2001-5001