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The flue gas associated with hydrogen fueled gas turbines has enhanced steam content and different thermophysical properties as compared to that of natural gas fuel case. The enhanced steam might lead to a rigorous corrosion degradation in the hot gas path components of the gas turbines such as turbine blades. In addition to this, high heat transfer rate can contribute to erosion, thermal fatigue, and creep damages. Hydrogen fueled gas turbines are also susceptible to some common routine faults such as fouling in the compressor section. Consequently, the health and performance of a hydrogen fueled gas turbines are degraded. Therefore, health monitoring in terms of remaining useful life (RUL) estimation of such turbines is of greater interest for the gas turbines OEMs and operators to ensure an enhanced availability and reliability in line with industry 4.0. The current study, therefore, develops a performance-based RUL estimation model for a 100-kW micro gas turbine that was recently retrofitted with hydrogen compliant FLOX burner. The validated performance model was further utilized for synthesizing run to failure data for fault diagnosis and RUL estimation. The study further incorporated linear and polynomial regression approaches and compared the end of life of gas turbines running on natural gas and hydrogen fuels. It became evident from the study that RUL of a gas turbine running on hydrogen fuel is approximately 6.47% lower than that of natural gas fueled gas turbines. These findings underline the necessity of using strong prediction models, as well as targeted maintenance actions, to limit the consequences of turbine corrosion in hydrogen powered micro gas turbines. The findings of the present study further provide new horizons for design modification and effective health monitoring of hydrogen fueled gas turbines.

Integrating performant waste-to-energy solutions in composting facilities is essential to mitigate the pressure of increasing amounts of residual municipal solid waste. Hence, this paper investigates the integration of residual municipal solid waste gasification with a bottoming organic Rankine cycle (ORC) unit, a biomass pyrolysis reactor and a wastewater treatment plant from an energy point of view. Two layouts are compared to evaluate the system's flexibility in a district cooling and heating network with varying load demand. Results: The analyses show that pyrolysis can reduce the ORC partialisation and obtain a bio-crude oil annual production in the range of 9617 t⋅y−1 – 12718 t⋅y−1. When the wastewater treatment is decoupled from the ORC condenser (second layout), it is possible to treat all 16000 t⋅y−1 of wastewater produced. On the contrary, in the first layout, the amount of wastewater treated is affected by the ORC working fluid. Toluene (9391 t⋅y−1) treats more wastewater than cyclopentane (8762 t⋅y−1) since the operability of the treatment line is extended by 275 h. In both layouts, the electricity production is higher with toluene, ranging between 341 and 5185 kWe. However, the highest natural gas savings, 1783990 m3⋅y−1, is obtained with cyclopentane.

Hydro power plants are among the most mature technologies for power production. To optimally manage possible overgeneration from non-programmable renewable energy sources, such as photovoltaic power plants and wind power plants, a Pumped Hydro Storage (PHS) plant can be employed as both a storage device (pumping mode) and a power production technology (turbine mode). To this aim, this paper deals with the optimization of the sizing and operation of a PHS plant that interacts with a power generation system consisting of different power production technologies. The national power production system and electric energy demand of Sweden are used as a case study and a PHS plant is sized and managed to fit conventional hydraulic sites as well as abandoned mines to be used as reservoirs. First, this paper develops a methodology suitable to identify the optimal size and operation strategy of the PHS plant, by means of the simultaneous use of two algorithms: surrogate modeling optimization algorithm and mixed integer linear programming algorithm. Then, this paper analyzes different present and future scenarios of electricity production, demand, and cost, in order to assess the energy and economic feasibility of PHS plants. The analyses carried out in this paper demonstrate that PHS plants are highly recommended with high overgeneration from photovoltaic power plants or wind power plants. This situation is a likely scenario thanks to green energy transition strategies. The return on investment of the most cost-effective solutions in the future scenarios ranges from 8.5 % to 11.6 %. In all the investigated scenarios, the results indicate that PHS is mainly employed to meet domestic electricity demand.

High-Temperature heat pumps (HTHPs) are becoming increasingly relevant in the industry as they represent a promising solution for decarbonizing industrial heat. These technologies can enable the electrification of industrial processes by exploiting electricity from renewables to provide process heat at temperatures above 250 °C, as in the case of emerging Brayton-based HTHPs. To succeed in this purpose, HTHPs must also ensure operational flexibility, which entails the ability to operate safely under varying loads and promptly respond to fluctuations in demand, while maintaining high efficiencies. Moreover, the ability to provide large flexible electric loads to transmission system operators has the potential to unlock innovative business cases and further promote the use of these systems. Common control strategies for achieving this include employing bypass mechanisms, fluid inventory control, and adjusting turbomachinery rotational speeds. Despite their variety, the simultaneous use of such control strategies is often limited as they may lead to significantly different system behaviors, both in terms of transient and steady performance. In this paper, rotational speed and fluid inventory control are examined from a transient perspective to maintain the desired sink temperature while regulating the thermal load of a novel Brayton-based HTHP. A comprehensive dynamic model of the system is proposed and leveraged to numerically test the two control approaches, aiming to provide insights for forthcoming experimental investigation. Results indicate that rotational speed control leads to negligible sink temperature residuals, while fluid inventory control better preserves the HTHP performances for varying temperature glides. 

This research aims to determine the primary thermofluidic correlations describing the thermosiphon effect under idealized steady-state conditions, considering water-in-glass evacuated-tube geometry, tilt angle, and heat flux. A numerical model based on Computational Fluid Dynamics (CFD) was developed to obtain these correlations for water-in-glass evacuated-tube solar collectors. Initial validation against experimental velocity and temperature profiles was necessary. With a validated CFD model, thermofluidic correlations were determined, expressed as dimensionless parameters such as Re, Gr, and Pr, water-in-glass evacuated-tube dimensions, and tilt angle. Symmetry was exploited in the water-in-glass evacuated-tube geometry for both validation simulations and the development of thermofluidic correlations. Contrary to correlations recorded in the literature, the correlations obtained in this study indicate an increase in water flow and a decrease in mean temperature with increasing tilt angle. These correlations are crucial for the energy-exergy balance formulations used in the analysis and design of such thermal systems.

Global energy transition efforts towards decarbonization requires significant advances within the energy sector. In this regard, hydrogen is envisioned as a long-term alternative fuel for gas turbines. Accordingly, the gas turbine industry has expedited their efforts in developing 100% hydrogen compliant burners and associated auxiliary components for retrofitting the existing gas turbines. The utilization of hydrogen in gas turbines has some underlying challenges such as corrosion mainly originating from increased steam content in the hot gas path. In addition to corrosion, the gas turbine compressor is vulnerable to fouling which is the most commonly occurring fault in gas turbine operating over certain time window. Both faults are susceptible to performance and health degradation. To avoid expensive asset loss caused by unexpected downtimes and shutdowns, timely maintenance decision making is required. Therefore, simple, accurate and computationally efficient fault detection and diagnostics models become crucial for timely assessment of health status of the gas turbines. The present study encompassed development of a physics-based performance model of a 100-kWe micro gas turbine running on 100% hydrogen fuel. The model is validated with experimental data acquired from test campaigns at the University of Stavanger. Data synthesized from experimentally validated performance model are utilized further for training machine learning algorithms. To identify an accurate algorithm, various algorithms such as support vector machine, decision tree, random forest algorithm, k-nearest neighbors, and artificial neural network were tested. The findings from fault diagnostics process (classification) revealed that ANN outperformed its counterpart algorithm by giving accuracy of 94.55%. Similarly, ANN also showed higher accuracy in performance degradation estimation process (regression) by showing the MSE of training loss as low as ~0.14. The comparative analysis of all the chosen algorithms in the present study revealed ANN as the most accurate algorithm for fault diagnostics of hydrogen fueled gas turbines. However, there is need to further implement the ensemble machine learning models or deep learning model to explore and expedite the real time fault diagnostic accuracy to avoid false alarms and missed detections in context of hydrogen fuel.

Aircraft electrification for propulsion is a promising way to alleviate the negative environmental impact of conventional carbon-powered aviation. Inclusion of the electrical powertrain aims to enhance design freedom allowing for more efficient power systems and operational schemes. In this work a design space exploration is performed aiming to derive power management guidelines based on aircraft environmental performance. A 19-passenger commuter aircraft employing the series/parallel partial hybrid-electric architecture is examined. Two underwing-mounted turboprop engines are combined with a boundary layer ingestion fan mounted in the aircraft aft and powered by an electrical drive. The primary electrical energy source is a battery system. A multi-disciplinary framework is utilized, comprising modelling approaches for multi-point thermal engine design, physics-based electrical component sizing and performance, aircraft sizing, mission design, and environmental assessment. The investigation revealed that the reference designed hybrid-electric configuration with entry-into-service 2035-assumed technologies yields roughly 18% improvement in block consumption and emissions, but an 8% increase in maximum take-off weight, compared to its 2014 conventional counterpart. The design space exploration for an optimal power management scheme indicated a minimum ratio of 1:1.35 between cruise and design point hybridization power. However, even the optimally operated hybrid aircraft showcases worse environmental performance compared to the conventional design of same entry-into-service date. The investigation has revealed that the complex powertrain and hybrid architecture selected may be more suitable for larger class aircraft, with the accumulated performance benefits reaching the order of 5% for the hybrid designs explored under relaxed top-level constraints.

The utilization of hydrogen fuel in gas turbines brings significant changes to the thermophysical properties of flue gas, including higher specific heat capacities and an enhanced steam content. Therefore, hydrogen-fueled gas turbines are susceptible to health degradation in the form of steam-induced corrosion and erosion in the hot gas path. In this context, the fault diagnosis of hydrogen-fueled gas turbines becomes indispensable. To the authors' knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study, however, develops an artificial neural network (ANN)-based fault diagnosis model using the MATLAB environment. Prior to the fault detection, isolation, and identification modules, physics-based performance data of a 100 kW micro gas turbine (MGT) were synthesized using the GasTurb tool. An ANN-based classification algorithm showed a 96.2% classification accuracy for the fault detection and isolation. Moreover, the feedforward neural network-based regression algorithm showed quite good training, testing, and validation accuracies in terms of the root mean square error (RMSE). The study revealed that the presence of hydrogen-induced corrosion faults (both as a single corrosion fault or as simultaneous fouling and corrosion) led to false alarms, thereby prompting other incorrect faults during the fault detection and isolation modules. Additionally, the performance of the fault identification module for the hydrogen fuel scenario was found to be marginally lower than that of the natural gas case due to assumption of small magnitudes of faults arising from hydrogen-induced corrosion.

In recent years, developing Artificial Intelligence (AI) models for complex system has become a popular research area. There have been several successful AI models for predicting the Selective Non-Catalytic Reduction (SNCR) system in power plants and large boilers. However, all these models are in essence black box models and lack of explainability, which are not able to give new knowledge. In this study, a novel explainable AI (XAI) model that combines the polynomial kernel method with Sparse Identification of Nonlinear Dynamics (SINDy) model is proposed to find the governing equation of SNCR system based on 5-year operation data from a power plant. This proposed model identifies the system's governing equation in a simple polynomial format with polynomial order of 1 and only 1 independent variable among original 68 input variables. In addition, the explainable AI model achieves a considerable accuracy with less than 21 % deviation from base-line models of partial least squares model and artificial neural network model.

Pumped hydro storage (PHS) is the most mature and widely used technology for large-scale energy storage. Hydropower plants are in fact also employed for this aim. However, most hydraulic sites suitable for this purpose have been already exploited. Therefore, the use of abandoned mines represents an alternative solution to take advantage of the availability of underground volumes as hydro storages. This paper investigates the potential of PHS plants integrated within a power generation system that comprises both programmable (e.g., hydropower and nuclear power plants) and non-programmable (e.g., wind and solar power plants) energy systems. All systems are connected with the power grid. To this purpose, this paper develops a methodology aimed at identifying the optimal sizing of the PHS plant as well as the optimal operation of the whole power generation system at Country level, with the goal of minimizing the imported energy. The methodology is validated by using Sweden as the case study, to assess the energy and economic feasibility of PHS plants in 2050. Different future scenarios of electricity production, demand, and cost are analyzed. The analyses carried out in this paper demonstrate that PHS plants are highly recommended if the cost of imported energy is expected to increase. In such a scenario, PHS is mainly employed to meet domestic electricity demand.