ДОСЛІДЖЕННЯ ЕКСПЛУАТАЦІЙНИХ ПАРАМЕТРІВ АВІАЦІЙНОГО ДВИГУНА, З ВИКОРИСТАННЯМ АЛЬТЕРНАТИВНИХ ПАЛИВ НА ОСНОВІ ВІДНОВЛЮВАНОЇ РОСЛИННОЇ СИРОВИНИ

Дана стаття присвячена оцінці параметрів роботи реактивного двигуна з використанням звичайного та альтернативного реактивного палива, отриманого шляхом сумішшю з біологічними добавками рослинного масла, під час випробувань на стенді. Стандартне реактивне паливо та новостворене альтернативне реактивне паливо було перевірено та порівняно в умовах роботи двигуна. Перевірена та доведена гіпотеза, що альтернативні реактивні палива можуть бути використані як робочий елемент реактивних двигунів, що забезпечують його надійну та ефективну роботу. Було експериментально визначено, що використання альтернативних реактивних палив призводить до покращення характеристик штреку реактивних двигунів та зменшення витрати палива. Вони забезпечують більш енергоефективне функціонування реактивного двигуна. Було також виявлено, що використання альтернативних реактивних палив призводить до зменшення температури газу в струменевій трубі. Це може позитивно впливати на довговічність матеріалів і конструкції двигуна проти високих температур, а також на зменшення викидів NOx. Результати дослідження показують, що експлуатаційні параметри реактивного двигуна, оснащеного новими альтернативними реактивними паливами, повністю задовольняють експлуатаційні норми, встановлені в специфікації для перевіреного двигуна. Альтернативні авіаційні палива, запропоновані в дослідженні, можуть використовуватися як робочий елемент авіаційного двигуна без необхідності внесення змін у його конструкцію. У статті детально представлені результати дослідження щодо таких показників: тяга, витрата палива, тиск на форсунках, температура газу за турбіною, відносна частота обертання ротора, прийомистість. Всі розрахунки представлені графічно та у таблицях. Зроблені висновки щодо кожного з перерахованих параметрів оцінки та проведено порівняння традиційного палива з винайденим альтернативним, згідно до чого було отримано, що використання результаті стендових випробувань було зроблено висновок, що експлуатаційні параметри ГТД за використання нових альтернативних палив повністю задовольняють експлуатаційні норми, встановлені для випробуваного ГТД

Evaluation of jet engine operation parameters using conventional and alternative jet fuels

Jet engine operation parameters using conventional and alternative jet fuels, obtained by blending with plant oil bio-additives, during bench tests were evaluated. Jet fuels were tested and compared in conditions of engine operation. It was determined that using alternative jet fuels improves jet engine thrust characteristics and reduces fuel flow. These provide more energy efficient operation of jet engine. Using alternative jet fuels results in reduction of gas temperature in the jet pipe. This contributes to durability of materials and structure of the engine against high temperature, as well as reducing of NOx emissions. The results of the study show that operational parameters of the jet engine powered with new alternative jet fuels completely satisfy exploitation norms set in specification for tested engine. Alternative jet fuels, proposed in the study, may be used as a working body of the jet engine without a need of making changes in its design

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

Modification of jet fuels composition with renewable bio-additives

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