This paper aimed to monitor the exhaust gas temperature (EGT) from the end of the low-pressure turbine (LPT) of a gas turbine for period of 6 months. To achieve this, 16 thermocouples were strategically placed to gather data at different points in the exhaust system. This comprehensive approach allowed for a detailed analysis of the exhaust gas temperature, which is a critical factor in the health of hot section of gas turbines. The results of this study provide valuable insights that can be used to optimize the periodic inspections of gas turbines and improve their decisions. The investigation of thermal fluctuations that can cause damage to hot components has been carried out using two statistical methods - Standard deviation and Skewness. By analyzing the standard deviation, the degree to which the temperature values vary from the mean and relative normal condition of each unit can be determined. Skewness helps to identify whether the temperature data is skewed towards the high or low values, indicating the presence of potential anomalies. The application of these statistical methods is aimed at understanding the impact of temperature fluctuations on hot components and developing maintenance strategies to mitigate their effects. In order to verify the accuracy of the statistical results, a thorough borescope inspection of the gas turbine is carried out in accordance with the maintenance manual. These inspections were conducted at three distinct intervals to ensure a comprehensive evaluation of the gas turbine condition performance and condition. The results of this inspection serve as a critical component in determining the optimal maintenance and repair plan for the gas turbine.

Gas turbine vibration data may exhibit considerable differences under time-varying conditions, which poses challenges for neural network anomaly detection. We first propose a framework for a gas turbine vibration frequency spectra process under time-varying operation conditions, assisting neural networks\' ability to capture weak information. The framework involves scaling spectra for aligning all frequency components related to rotational speed and normalizing frequency amplitude in a self-adaptive way. Degressive beta variational autoencoder is employed for learning spectra characteristics and anomaly detection, while a multi-category anomaly index is proposed to accommodate various operating conditions. Finally, a dataset of blade Foreign Object Damage (FOD) fault occurring under time-varying operating conditions was used to validate the framework and anomaly detection. The results demonstrate that the proposed method can effectively reduce the spectra differences under time-varying conditions, and also detect FOD fault during operation, which are challenging to identify using conventional methods.

Thermoelectric (TE) waste heat recovery has attracted significant attention over the past decades, owing to its direct heat-to-electricity conversion capability and reliable operation. However, methods for application-specific, system-level TE design have not been thoroughly investigated. This work provides detailed design optimization strategies and exergy analysis for TE waste heat recovery systems. To this end, we propose the use of TE system equipped on the exhaust of a gas turbine power plant for exhaust waste heat recovery and use it as a case study. A numerical tool has been developed to solve the coupled charge and heat current equations with temperature-dependent material properties and convective heat transfer at the interfaces with the exhaust gases at the hot side and with the ambient air at the heat sink side. Our calculations show that at the optimum design with 50% fill factor and 6 mm leg thickness made of state-of-the-art Bi2Te3 alloys, the proposed system can reach power output of 10.5 kW for the TE system attached on a 2 m-long, 0.5 × 0.5 m2-area exhaust duct with system efficiency of 5% and material cost per power of 0.23 $/W. Our extensive exergy analysis reveals that only 1% of the exergy content of the exhaust gas is exploited in this heat recovery process and the exergy efficiency of the TE system can reach 8% with improvement potential of 85%.

Considering the persistent human need for electricity and fresh water, cogeneration systems based on the production of these two products have attracted the attention of researchers. This study investigates a cogeneration system of electricity and fresh water based on gas turbine (GT) as the prime mover. The wasted energy of the GT exhaust gases is absorbed by a heat recovery steam generator (HRSG) and supplies the superheat steam required by the steam turbine (ST). In order to produce fresh water, a multi-effect desalination (MED) system is applied. The motive steam required is provided by extracting steam from the ST. In order to reduce the environmental pollution of this cogeneration system, the steam injection method is proposed in the GT\'s combustion chamber (CC). This system is optimized by a multi-objective optimization tool based on the Genetic Algorithm (GA). The design variables include pressure ratio of compressor (CPR), inlet temperature of gas turbine (TIT), steam injection mass flow rate in the CC, HRSG operating pressure, HRSG evaporator pinch point temperature difference (PPTD), steam pressure of the MED ejector, ejector motive steam flow rate, number of MED effects, and return effect. The goals are to minimize the total cost rate (TCR), which includes the cost of initial investment and maintenance of the system, the cost of consumed fuel, and the cost of disposing of CO and NO pollutants, as well as maximizing the exergy efficiency. In the end, it is observed that the steam injection in the CC leads to the reduction of the mentioned pollutant index, and it is proposed as a suitable solution to reduce the pollution of the proposed cogeneration system.

In this article, in order to achieve a sustainable environment, the optimization of a GT equipped with intercooling of the compression process is discussed. To limit the exergy destruction in intercooling cooling process and also to reduce the heat dissipation in the environment, an ORC system is applied for heat recovery and more power generation. Decision variables include CPR, first stage CPR, TIT, intercooler effectiveness, HRVG pressure, and superheating degree. During a parametric study, the effect of decision variables on operating factors including exergy efficiency, TCR, and the normalized emission rate of environmental pollutants are investigated. Finally, by performing bi-objective optimization and considering exergy efficiency and TCR as OFs, optimal performance conditions are determined. Finally, it is observed that in optimum conditions, exergy efficiency is 33% and TCR is 0.9 $/s.

Achieving power generation systems with high efficiency and low emission of environmental pollutants is one of the requirements of a sustainable environment. In this study, a hybrid power generation system based on gas turbine (GT) with regenerator configuration is introduced. A supercritical carbon dioxide (sCO2) cycle is used to recover the waste heat from GT exhaust gases. In order to reduce the emission of environmental pollutants, the combination of hydrogen and methane is used as GT fuel. In order to investigate the effect of using hydrogen in the fuel composition, the fraction of hydrogen is changed between 0 and 50% and its effect on performance, environmental, and economic factors is investigated. Also, the effect of design parameters such as compressor pressure ratio (CPR) and turbine inlet temperature (TIT) of GT and sCO2 cycles on exergy efficiency, total cost rate (TCR), and normalized pollutants emission index are investigated. Then, by performing a bi-objective optimization process with the aim of achieving maximum exergy efficiency and minimum TCR, the optimal operating point is extracted. At the optimal operating point, exergy efficiency is 44% and TCR is 6 dollars/second.

Combined cooling, heating and power (CCHP) is one of methods for enhancing the efficiency of the energy conversion systems. In this study a CCHP system consisting of a gas turbin (GT) as the topping cycle, and an organic Rankine cycle (ORC) associated with double-effect absorbtion chiller (DEACH) is decisioned as the bottoming cycle to recover the waste heat from GT exhaust gas. The considered CCHP system is investigated to maintain electricity, heating and cooling demand of a town. A parametric study is investigated and the effect decision variables on the performance indicators including exergy efficiency, total cost rate (TCR), cooling capacity, and ORC power generation is examined. Decision variables of the ORC system consist of HRVG pressure, and condenser pressure and the DEACH including evaporator pressure, condseser pressure, concentration of the concentrated solution, concentration of the weak solution, and solution mass flow rate. Finally a multi-objective optimization performed using Genetic Algorithm (GA) and the optimal design point is selected. It is observed at the optimum point the exergy efficiency, TCR, and sustainability index are 17.56%, 74.49 $/h, and 1.21, respectively.

Ion Transport Membrane (ITM) is an emerging technology for producing O2 by separating air in its membrane. To decrease energy loss in air separation unit and to increase the overall efficiency of a power generation unit ITM is added with the gasification unit in this model. Ceramic materials are generally used to make the ion transport membrane that produces oxygen by conducting oxygen ions at a specified temperature. Potential advantages can be gained by integrating ITM technology with power generation units as 99% pure oxygen is produced from ITM. Using ITM air separator is more beneficial compared to cryogenic air separation as ITM technology helps to improve IGCC overall efficiency and also reduces plant auxiliaries than that of power generation systems integrated with cryogenic. This paper proposed a novel and effective integration of ITM, gas turbine, HRSG system, gas clean up system and gasification unit to produce sustainable energy. Environmental impacts are considered to design this integrated power generation unit. The proposed model achieved a high gross electric efficiency of 47.58% and high net power of 296730 kW which revealed its potentiality compared to available cryogenic ASU-based combine cycle power plants.

The application of identification techniques using artificial intelligence to the gas turbine (GT), whose nonlinear dynamic behavior is difficult to describe through differential equations and the laws of physics, has begun to gain importance for a little more than a decade. NARX (Nonlinear autoregressive network with exogenous inputs) is one of the models used to identify GT because it provides good results. However, existing studies need to show a systematic method to generate robust NARX models that can identify a GT with satisfactory accuracy. In this sense, a systematic method is proposed to design NARX models for identifying a GT, which consists of nine precise steps that go from identifying GT variables to obtaining the optimized NARX model. To validate the method, it was applied to a case study of a 215 MW SIEMENS TG, model SGT6-5000F, using a set of 2305 real-time series data records, obtaining a NARX model with an MSE of 1.945 × 10-5, RMSE of 0.4411% and a MAPE of 0.0643.

The power demand from gas turbines in electrical grids is becoming more dynamic due to the rising demand for power generation from renewable energy sources. Therefore, including the transient data in the fault diagnostic process is important when the steady-state data are limited and if some component faults are more observable in the transient condition than in the steady-state condition. This study analyses the transient behaviour of a three-shaft industrial gas turbine engine in clean and degraded conditions with consideration of the secondary air system and variable inlet guide vane effects. Different gas path faults are simulated to demonstrate how magnified the transient measurement deviations are compared with the steady-state measurement deviations. The results show that some of the key measurement deviations are considerably higher in the transient mode than in the steady state. This confirms the importance of considering transient measurements for early fault detection and more accurate diagnostic solutions.