Performance based diagnostics of a twin shaft aeroderivative gas turbine: water wash scheduling

Aeroderivative gas turbines are used all over the world for different applications as Combined Heat and Power (CHP), Oil and Gas, ship propulsion and others. They combine flexibility with high efficiencies, low weight and small footprint, making them attractive where power density is paramount as off shore Oil and Gas or ship propulsion. In Western Europe they are widely used in CHP small and medium applications thanks to their maintainability and efficiency. Reliability, Availability and Performance are key parameters when considering plant operation and maintenance. The accurate diagnose of Performance is fundamental for the plant economics and maintenance planning. There has been a lot of work around units like the LM2500® , a gas generator with an aerodynamically coupled gas turbine, but nothing has been found by the author for the LM6000® . Water wash, both on line or off line, is an important maintenance practice impacting Reliability, Availability and Performance. This Thesis aims to select and apply a suitable diagnostic technique to help establishing the schedule for off line water wash on a specific model of this engine type. After a revision of Diagnostic Methods Artificial Neural Network (ANN) has been chosen as diagnostic tool. There was no WebEngine model available of the unit under study so the first step of setting the tool has been creating it. The last step has been testing of ANN as a suitable diagnostic tool. Several have been configured, trained and tested and one has been chosen based on its slightly better response. Finally, conclusions are discussed and recommendations for further work laid out

Artificial Neural Network and its Applications in the Energy Sector – An Overview

In order to realize the goal of optimal use of energy sources and cleaner environment at a minimal cost, researchers; field professionals; and industrialists have identified the expediency of harnessing the computational benefits provided by artificial intelligence (AI) techniques. This article provides an overview of AI, chronological blueprints of the emergence of artificial neural networks (ANNs) and some of its applications in the energy sector. This short survey reveals that despite the initial hiccups at the developmental stages of ANNs, ANN has tremendously evolved, is still evolving and have been found to be effective in handling highly complex problems even in the areas of modeling, control, and optimization, to mention a few

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

A multiscale strategy for fouling prediction and mitigation in gas turbines

Gas turbines are one of the primary sources of power for both aerospace and land-based applications. Precisely for this reason, they are often forced to operate in harsh environmental conditions, which involve the occurrence of particle ingestion by the engine. The main implications of this problem are often underestimated. The particulate in the airflow ingested by the machine can deposit or erode its internal surfaces, and lead to the variation of their aerodynamic geometry, entailing performance degradation and, possibly, a reduction in engine life. This issue affects the compressor and the turbine section and can occur for either land-based or aeronautical turbines. For the former, the problem can be mitigated (but not eliminated) by installing filtration systems. For what concern the aerospace field, filtration systems cannot be used. Volcanic eruptions and sand dust storms can send particulate to aircraft cruising altitudes. Also, aircraft operating in remote locations or low altitudes can be subjected to particle ingestion, especially in desert environments. The aim of this work is to propose different methodologies capable to mitigate the effects of fouling or predicting the performance degradation that it generates. For this purpose, both hot and cold engine sections are considered. Concerning the turbine section, new design guidelines are presented. This is because, for this specific component, the time scales of failure events due to hot deposition can be of the order of minutes, which makes any predictive model inapplicable. In this respect, design optimization techniques were applied to find the best HPT vane geometry that is less sensitive to the fouling phenomena. After that, machine learning methods were adopted to obtain a design map that can be useful in the first steps of the design phase. Moreover, after a numerical uncertainty quantification analysis, it was demonstrated that a deterministic optimization is not sufficient to face highly aleatory phenomena such as fouling. This suggests the use of robust or aggressive design techniques to front this issue. On the other hand, with respect to the compressor section, the research was mainly focused on the building of a predictive maintenance tool. This is because the time scales of failure events due to cold deposition are longer than the ones for the hot section, hence the main challenge for this component is the optimization of the washing schedule. As reported in the previous sections, there are several studies in the literature focused on this issue, but almost all of them are data-based instead of physics-based. The innovative strategy proposed here is a mixture between physics-based and data-based methodologies. In particular, a reduced-order model has been developed to predict the behaviour of the whole engine as the degradation proceeds. For this purpose, a gas path code that uses the components’ characteristic maps has been created to simulate the gas turbine. A map variation technique has been used to take into account the fouling effects on each engine component. Particularly, fouling coefficients as a function of the engine architecture, its operating conditions, and the contaminant characteristics have been created. For this purpose, both experimental and computational results have been used. Specifically for the latter, efforts have been done to develop a new numerical deposition/detachment model.Le turbine a gas sono una delle pricipali fonti di energia, sia per applicazioni aeronautiche che terrestri. Proprio per questa ragione, esse sono spesso costrette ad operare in ambienti non propriamente puliti, il che comporta l’ingestione di contaminanti solidi da parte del motore. Le principali implicazioni di questo problema sono spesso sottovalutate. Le particelle solide presenti nel flusso d’aria che il motore ingerisce durante il suo funzionamento possono depositarsi o erodere le superfici interne della macchina, e portare a variazioni alla sua aerodinamica, quindi a degrado di performance e, molto probabilmente, alla diminuzione della sua vita utile. Questo problema aflligge sia la parte del compressore che la parte della turbina, e si manifesta sia in applicazioni terrestri che aeronautiche. Per quanto riguarda la prima, la questione può essere mitigata (ma non eliminata) dall’installazione di sistemi di filtraggio all’ingresso della macchina. Per le applicazioni aeronautiche invece, i sistemi di filtraggio non possono essere utilizzati. Questo implica che il particolato presente ad alte quote, magari grazie ad eventi catastrofici quali eruzioni vulcaniche, o a basse quote, quindi ambienti deseritic, entra liberamente nella turbina a gas. Lo scopo principale di questo lavoro di tesi, è quello di proporre differenti metodologieallo scopo di mitigare gli effetti dello sporcamento o predirre il degrado che esso comporta nelle turbine a gas. Per questo scopo, sia la parte del compressore che quella della turbina sono state prese in considerazione. Per quanto riguarda la parte turbina, saranno presentate nuove guide progettuali volte al trovare la geometria che sia meno sensibile possibile al problema dello sporcamento. Dopo di ciò, i risultati ottenuti verranno trattati tramite tecniche di machine learning, ottenendo una mappa di progetto che potrà essere utile nelle prime fasi della progettazione di questi componenti. Inoltre, essendo l’analisi fin qui condotta di tipo deterministico, un’analisi delle principali fonti di incertezza verrà eseguita con l’utilizzo di tecniche derivanti dall’uncertainty quantification. Questo dimostrerà che l’analisi deterministica è troppo semplificativa, e che sarebbe opportuno spingersi verso una progettazione robusta per affrontare questa tipologia di problemi. D’altro canto, per quanto concerne la parte compressore, la ricerca è stata incentrata principalmente sulla costruzione di uno strumento predittivo, questo perchè la scala temporale del degrado dovuto alla deposizione a "freddo" è molto più dilatata rispetto a quella della sezione "calda". La trategia proposta in questo lavoro di tesi è un’insieme di modelli fisici e data-driven. In particolare, si è sviluppato un modello ad ordine ridotto per la previsione del comportamento del motore soggetto a degrado dovuto all’ingestione di particolato, durante un’intera missione aerea. Per farlo, si è generato un codice cosiddetto gas-path, che modella i singoli componenti della macchina attraverso le loro mappe caratteristiche. Quest’ultime vengono modificate, a seguito della deposizione, attraverso opportuni coefficienti di degrado. Tali coefficienti devono essere adeguatamente stimati per avere una corretta previsione degli eventi, e per fare ciò verrà proposta una strategia che comporta l’utilizzo sia di metodi sperimentali che computazionali, per la generazione di un algoritmo che avrà lo scopo di fornire come output questi coefficienti

Integrated performance prediction and quality control in manufacturing systems

textPredicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab.Mechanical Engineerin

Gas Turbine Engine Prognostics Using Time-Series Based Approaches

In todays market, the increasing demand on utilizing gas turbine engines can be quite costly if users rely only on traditional time-based maintenance schedules. Meeting both the safety and the economical aspects of such systems could be realized by using an appropriate maintenance strategy in which the prediction of the engine health condition is employed to ensure that the system is maintained only if necessary. Towards this end, in this thesis the prognosis problem in the gas turbine engines is investigated. As in every rotational mechanical equipment, gas turbine rotating components also degrade during the engine operation which may deteriorate their performance. The engine degradation may originate from different sources such as aging, erosion, fouling, corrosion, etc. Hard particles mixed with the air can remove the materials from the flow path components (erosion) and cause aerodynamic changes in the blades, which can consequently reduce the affected components performance. Accumulated particles on the flow path components and annulus surfaces of the gas turbine (fouling) can also reduce the flow rate of the gas and consequently decrease the power and efficiency of the affected components. Among different degradation sources in the engine, erosion and fouling are considered as two well-known degradation phenomena and their effects on the engine system prognostics are studied in this thesis. Towards the above end, a controller is designed to control the thrust level of the engine and a Matlab/Simulink platform is employed to incorporate the effects of the above degradation factors and the engine dynamic model. The engine performance degradation trends are modeled by using three types of time-series based techniques namely, the autoregressive integrated moving average (ARIMA), the vector autoregressive (VAR) and the hybrid fuzzy autoregressive integrated moving average (hybrid fuzzy ARIMA) models. One of the challenges associated with time-series approaches is selecting a proper model which represents the structure of the time-series and is employed for prediction and prognosis purposes. Two widely used criteria namely, the Akaike’s information criterion (AIC) and the Bayesian information criterion (BIC) are used in order to select the best model. The challenges of coping with the uncertainties due to variety of sources such as measurement noise, insufficient data and changing operating conditions are inevitable factors. Taking the above facts into account, it may not be practical to obtain or be concerned with an exact prediction information. Therefore, we construct instead confidence bounds that provide a realistic boundary for the prediction and this is applied to all our proposed approaches in this thesis. The first method in this thesis deals with modeling a measurable parameter using its historical data which is a fine-tuned version of the ARMA model for non-stationary time series analysis. The second method, VAR model, models the measurable parameters by fusing historical data with the current and past data of some other engine measurable data in a vector form so that one can get benefit of more measurement parameters of the engine. The third method deals with fusing two measurable parameters using a Takagi-Sugeno fuzzy inference engine. In this thesis we are focused on modeling the engine performance degradations due to the fouling and the erosion which are the two main causes of gas turbine engine deterioration. In order to evaluate the performance of the proposed methods, they are applied to three different scenarios. These scenarios include the compressor fouling, turbine erosion phenomena and their combination with different severities. Our numerical simulation results show that the performance of the hybrid fuzzy ARIMA model is superior to that of the ARIMA and VAR methods

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science