Gas Turbine Generator Research Articles

Analisis Efektivitas Gas Turbine Generator pada PT Mitra Energi Batam

PT Mitra Energi Batam is one of the Gas Power Plants (PLTG) and regulates the electricity supply of Batam city with PT PLN-Batam. According to the tabulation of PT Mitra Energi Batam's production data from September 2022 to September 2023, the rolls-royce Gas Turbine Generator production engine has a downtime value of 805.60 hours. The purpose of the study is to find out the factors that cause high downtime and know how to minimize it. Using the proposed Overall Equipment Effectiveness method as a way to measure Losses due to production disruptions by taking six big loss and Fishbone diagrams to find the cause and effect of Losses that cause failure of machine or equipment operations. The results of the study of the highest OEE value in August 2023 were 98.54%, the lowest OEE value was in April 2023 which was 0%, the cause was severe damage to the power turbine blade which required engine replacement, but was constrained by the time of the engine spare delivery process. The six big Losses factor that contributed the most to the OEE value was idling minor stoppage with an average value of 8.83% and equipment failure Losses with an average value of 8.81%.

Renewable hydrogen requirements and impacts for network balancing: A Queensland case study

Hydrogen is the gas of the moment: an abundant element that can be created using renewable energy, transported in gaseous or liquid form, and offering the ability to provide energy with only water vapour as an emission. Hydrogen can also be used in a fuel blend in electricity generation gas turbines providing a low carbon option for providing the peak electricity to cover high demand and firming.While the electricity grid is itself transforming to decarbonising, hard-to-abate industries such as cement and bauxite refineries are slower to reduce emissions, constrained by their high temperature process requirements. Hydrogen offers a solution allowing onsite production, process heat, with waste heat recovery supporting blended gas turbine generation for onsite electricity supply.This article builds on decarbonisation pathway simulation results from an ANEM model of the electricity grid identifying the amount of peak demand energy required from gas turbines. The research then examines the quantity, flow rate, storage requirements and emissions reduction if this peak generation were supplied by open cycle hydrogen capable gas turbines.

Proposal and optimization of a novel biomass-based tri-generation system using energy, exergy and exergoeconomic analyses and design of experiments method

A novel trigeneration system of cooling, heating and power based on biomass is designed in current study. This system consists of five parts: biomass gasification subsystem, steam turbine subsystem (Rankin cycle), gas turbine subsystem (Brayton cycle), Kalina subsystem and ejector refrigeration subsystem. Energy, exergy and exergy-economic analysis were performed on the whole system and the effects of different key parameters on energy efficiency and exergy and economic factors are investigated. The designed cycle has been qualitatively optimized using DOE method and the interaction effects of parameters on cycle performance has been investigated. The results show that the highest exergy destruction is related to the combustion chamber with 56.14 %. Also, the lowest exergy destruction is assigned to the separator of the Kalina cycle with a nearly zero value. The highest investment, operating and maintenance costs are related to air compressor, gas turbine and steam generator, respectively. The results of parametric analysis showed that with increasing the mixing ratio of natural gas to syngas the thermal and exergy efficiencies, the rate of total system cost and the unit cost of power generation increase. Also, with increasing gasification temperature, the thermal and exergy efficiencies of this system vary slightly and can be considered constant, but the total cost of system increases. The results of DOE analysis showed that among the main effects of the parameters, the isentropic efficiency of the gas turbine and the temperature of the air entering the combustion chamber have the greatest effect on the system efficiency, and the GMR has the greatest effect on the LCOE of the system, and among the interaction effects of the parameters, the interaction between the temperature of the air entering the combustion chamber has the greatest effect on the system efficiency and LCOE.

Технології електрогенерації та теплопостачання для підвищення енергетичної незалежності окремих територіальних громад

Russian attacks on Ukraine's energy system, which led to the blackout of entire cities and the termination of district heating, highlighted the risks and disadvantages of relying on large-scale district energy supply for communities. In this context, it is important to consider the development of the energy supply of communities in the direction of their energy independence. The purpose of the article is to form a list of electricity generation and heat supply technologies for increasing the energy independence of individual territorial communities, which are appropriate for implementation in Ukraine. The article considers the criteria for the analysis and selection of technologies for distributed power generation and heat supply. An analytical overview of the following power generation and heat supply technologies is presented: solar and wind power plants, gas piston and gas turbine plants, generators, batteries, co- and trigeneration plants, biofuel power plants and heat-generating boilers, heat pumps and electric boilers, and their grouping is performed according to the established criteria. All the considered technologies contribute to reducing the external supply of communities with energy resources and reducing losses during the transmission and distribution of electricity. Technologies that use renewable energy sources and local fuels are appropriate for implementation in communities in accordance with the availability of sufficient amounts of resources. Still, in most cases, they are not able to fully provide communities with electrical and thermal energy due to their insufficient quantity and high cost of equipment, such as batteries and heat pumps. Decisions regarding the implementation of a specific technology of distributed generation should be made after a thorough analysis of the conditions and possibilities of the energy supply of each individual community. This article is aimed at discussing the options of increasing energy independence and ensuring the reliability of the energy supply of territorial communities in Ukraine. Keywords: distributed generation, power generation, heat supply, renewable energy sources, cogeneration, biofuels, heat pumps.

Fault diagnosis of gas turbine generator bearings using enhanced valuable sample strategy and convolutional neural network

Gas turbine bearings operate continuously under complex and harsh conditions such as high temperatures, high pressures and high speeds. Bearing fault monitoring data often exhibits anomalies, noise, missing values, and strong coupling and non-linearity due to real-world random factors. In addition, the traditional convolutional neural network (CNN) is still limited by the scarcity of labeled samples in real-world conditions and cannot fully extract fault features. To address the complexities of strongly coupled fault data, inconsistent data quality, and the shortage of authentic labeled samples, this paper presents a novel method for bearing fault diagnosis. This method utilizes an enhanced valuable sample strategy in conjunction with CNNs, integrating data enhancement and active learning (AL) for preprocessing to create valuable training sets for the networks. At the outset, random dropout and scaling jitter operations are applied to the original data, effectively removing anomalies and gaps in the fault signals. This process simultaneously emulates variations in the frequency spectrum and amplitude of vibration signals under real operating conditions, thereby enhancing the quality of gas turbine bearing fault data. Subsequently, the method incorporates AL techniques, iteratively selecting unlabeled data samples with the highest information value, and annotating them using support vector machines. This strategy creates a valuable training dataset that further enhances the fault diagnosis capabilities of the model. In the subsequent phase, a three-dimensional CNN is employed to extract fault feature information efficiently from the fault data, culminating in precise fault classification. In order to validate the effectiveness and superiority of the algorithm, validation and comparison analyses were carried out using the Case Western Reserve University and XJTU open datasets. To evaluate the practical effectiveness, a gas turbine main bearing dataset collected in a real environment, named the BaiChuan dataset, is used for the engineering validation of the proposed method.

Application of deep learning modelling of the optimal operation conditions of auxiliary equipment of combined cycle gas turbine power station

Reasonable condition adjustment of the auxiliary machinery can optimize the operation performance of the Combined Cycle Gas Turbine (CCGT) power station. In this paper an optimization method for the operation control of the auxiliary equipment based on deep learning is proposed and applied to an F-class gas turbine generation unit. A database of the optimal operating conditions is established to achieve the best economic benefit based on the analysis of two years of historical operation data. The control optimization models are built using the machine learning algorithms including the Multi-Layer Perceptron (MLP), Supporting Vector Machine (SVM), Gaussian process regression and linear regression methods and their accuracy has been compared. The input parameters of these models consist of the load rate, the heating output and the ambient temperature, with the output parameters including the electrical currents for the mechanical draft cooling tower, the high pressure feed pump, the medium pressure feed pump and the condensate pump. The MLP model is designed with different network hidden layer structures and provides the highest calculation accuracy with the least average error of 1.82 %. Different training data sizes are compared and the optimal control trajectory is analyzed. The result can provide useful references for the optimization of auxiliary equipment operations.

Power generation gas turbine performance enhancement in hot ambient temperature conditions through axial compressor design optimization

The output power and efficiency of gas turbines are profoundly influenced by ambient temperatures, with higher temperatures resulting in a notable decline in power output. This issue becomes particularly critical during periods of heightened power demand in hot climates. There are two common ways to solve this problem: incorporating auxiliary systems, such as inlet air cooling, or undertaking a comprehensive redesign of the gas turbine core engine. However, these methods sometimes are costly and unfeasible, particularly in regions characterized by arid conditions or constrained budgets for engine modifications. An alternative strategy has been developed to optimize axial compressor performance in order to improve gas turbine power in high-temperature environments, through an innovative approach. This approach centers on the concept of “re-staggering” the stator blades as a retrofit upgrade for existing gas turbines, enabling an increase in compressor inlet mass flow rate while maintaining peak efficiency at 45 °C. An in-house optimization tool was developed, incorporating a genetic algorithm and artificial neural network, to ascertain the optimal configuration of the re-stagger angles. This optimization process was initially coupled with a mean-line solver and subsequently refined using a through-flow model to enhance the preliminary design. The refined compressor design was then subjected to a comprehensive 3D Computational Fluid Dynamics analysis. Additionally, “end-bend” and “elliptical leading edge” techniques were employed to sustain compressor efficiency and ensure stability across various design and off-design operating conditions. Following a thorough structural analysis, the newly designed compressors were manufactured and tested at two distinct sites in Iran (Yazd and Bampur). The measured data demonstrated the commendable performance and stability of the redesigned compressors, aligning closely with numerical predictions. Notably, the results showcased a substantial increase in gas turbine power output ranging from 7.0% to 8.0% at hot ambient conditions, all while preserving efficiency and stability. Encouragingly, the upgraded compressor also exhibited superior performance even at lower temperatures, such as 15 °C. Consequently, this uprating concept presents a practical and cost-effective solution for power plant operators seeking a significant boost in power output, especially during hot weather conditions or in arid regions prone to drought.

Noise‐robust gas path fault detection and isolation for a power generation gas turbine based on deep residual compensation extreme learning machine

AbstractOne of the major challenges facing fault diagnosis tools is their exposure to noise. The presence of noise may cause false alarms or the inability to detect a progressive fault in the early stages of its occurrence. Continuing previous efforts to address such a problem, in this paper, a noise‐robust diagnosis system for an industrial gas turbine is presented. The proposed structure employs a set of deep residual compensation extreme learning machines (DRCELMs). In this model, an optimal number of compensating blocks are trained to recover some of the lost useful information in the face of noise. Training and testing data required to develop the fault diagnosis model are generated by a performance model of the studied gas turbine. The t‐distributed stochastic neighbor embedding algorithm is employed for visualizing the gas path faults. Furthermore, the performance of the DRCELM is evaluated by comparing it with six other diagnosis models. The results indicate higher robustness of the DRCELM compared to other fault diagnosis systems. The proposed model presents a classification accuracy of >97% in noisy data and an accuracy of >98% in noise‐free data and combined data, while the average of fault positive rate and fault negative rate in noisy data is less than 2.5%.

Power Quality Enhancement of Remote Gas Field Generations with Smart Power Converters

Direct power generation near gas fields offers numerous benefits, including optimized economic efficiency and reduced environmental impact. Moreover, building on-site greenhouses emerges as a promising approach to further minimize carbon emissions and residual heat, greatly promoting resource utilization. However, such power plants generally have access to a weak grid due to their remote locations, and they also contain nonlinear local loads, such as the grow lights in the greenhouses. Consequently, the generation system is susceptible to power quality issues, manifested in overvoltage and harmonics. To address these issues, a smart back-to-back converter is employed to interconnect the gas turbine generator and the utility grid. This smart converter not only enhances power quality but also offers potential ancillary services that contribute to the dynamics of the gas generation system, such as damping low-frequency oscillation among parallel-connected generators. In this paper, three control configurations for the back-to-back converter are developed, enabling the coordinated regulation of exported active power, AC voltage, and DC-link voltage in either a grid-following or grid-forming manner. Furthermore, comparative studies are conducted to provide guidelines for selecting an appropriate control strategy that ensures stable operation under various short circuit ratios. A practical gas cogeneration system is chosen to evaluate the performance of the back-to-back converter, and real-time simulations based on RT-LAB are carried out to validate the effectiveness of the methodology.

Estimating of gases emission from waste sites to generate electrical energy as a case study at Al-Hillah City in Iraq

Methane (CH4) is a greenhouse gas resulting from human activities, especially landfills, and it has many potential environmental issues, such as its major role in global warming. On the other hand, methane can be converted to liquid fuel or electricity using chemical conversion or gas turbine generators. Therefore, reusing such gases could be of great environmental and economic benefit. In this context, this study aims to estimate the emissions of methane gas from the landfills in Al-Hillah City, Iraq, from 2023 to 2070 and the producible electric energy from this amount. The estimating process was carried out using the Land GEM model and compared with traditional models. The obtained results demonstrated that the total estimated landfill methane emissions for 48 years are 875,217 tons, and the average annual methane emission is 18,234 tons based on a yearly waste accumulation rate of 1,046,413 tons and a total waste amount of 50,227,808 tons. The anticipated loads of methane gas can be utilized to generate about 287,442 MW/year of electricity from 2023 to 2070. In conclusion, the results obtained from this study could be evidence of the potential environmental and economic benefits of harvesting and reusing methane gas from landfills.

An evaluation model for wall heat transfer effects on micro centrifugal compressors and its application in aerodynamic design

In micro gas turbine generators, the significant temperature difference within close proximity of the hot and cold components induces a substantial heat influx into the micro centrifugal compressor. This heat influx profoundly modifies the aerodynamic characteristics of the compressor. Ensuring a precise evaluation of the performance decline attributable to wall heat transfer effects and systematically integrating these impacts into the aerodynamic design process assumes paramount importance. Previous evaluation models, which involved unnecessary simplifications, suffered from limitations in accuracy. This paper presented a novel evaluation model for the diabatic isentropic efficiency through entropy analysis of the diabatic ideal compression process and the actual compression process. The rigorous theoretical derivation without any simplification empowers this model to faithfully mirror the authentic aerodynamic performance of diabatic compressors. The experimental and numerical verification results indicate that the proposed model is more reliable than the previous ‘preheating-adiabatic compression’ model when evaluating performance degradation stemming from wall heat transfer. The new model exhibits an error of approximately 3.7% at the design point and a mean absolute error of about 6% along the characteristic line of the design speed. Both deviations are deemed acceptable within the purview of engineering assessments. Furthermore, this paper illustrated an instance of the novel model’s application in the aerodynamic design process. A prototype compressor, impeccably designed under adiabatic conditions but falling short of performance benchmarks during the performance test, was redesigned using the proposed method. The experimental results show that through the incorporation of wall heat transfer effects, the redesigned compressor aligns perfectly with design requirements, yielding an efficiency enhancement of roughly 1.5%.

Active disturbance rejection coordinated control for integrated solar combined cycle system considering system inertia difference

The integrated solar combined cycle system (ISCC) primarily consists of solar thermal power generation equipment and gas turbines. This system effectively mitigates the inherent randomness associated with power generation due to weather conditions while simultaneously providing electric and heat loads. However, due to the difference in response speed of various power generation equipment, it is difficult to respond quickly as the strength of illumination and electricity-heat load demand change, seriously affecting the system's stability operation. This paper proposes an active disturbance rejection control (ADRC) method for the ISCC system considering the different response speeds of generation equipment. Firstly, the mathematical model of the ISCC system is established, in which the system's response speed is improved through the inertial power compensation method. Then, the ADRC method is used to reduce the disturbance caused by uncertain factors, such as solar radiation intensity and load demand change, to ensure the balance between electric and thermal power supply and demand. Finally, based on the measured weather data from National Renewable Energy Laboratory (NREL), the proposed method's performance is evaluated, compared with the hybrid H2/H∞ control method and the auto-coupling proportional integral differential (ACPID) control method. Comparison results show that the ADRC method has better control effectiveness and dynamic response performance, powerfully shortens the system stability time, and reduces overshoot.

The low-carbon economic operation strategy of virtual power plant under different electricity-gas-heat-carbon multi-market synergy scenarios

Studying the grid integration of renewable energy power generation is crucial for achieving the goal of carbon neutrality since it may have a significant influence on the secure and reliable functioning of the power system. In order to solve the problem of deviation impact caused by renewable energy fluctuations and the optimal scheduling of VPP (Virtual Power Plant), the study divides the internal aggregation unit of the virtual power plant into two parts to model. One part is the source equipment, including wind power generation equipment, gas turbine, gas boiler and waste heat boiler. And the other part is the generalized Energy storage, including electric vehicles, air conditioners and alternative response loads. Ultimately, a generalized energy storage-based virtual power plant operation optimization model is developed under multi-market coordination of electricity-gas-heat-carbon. According to the study’s findings, adding more power-to-gas technology boosts revenue in the carbon trading market by 25.24 percent. The energy market’s revenue is equal to that in the absence of a carbon trading market, and the income of the natural gas market increases by $ 32.96. The income of the carbon trading market is $ 181.51, and the final operating cost is reduced by $ 180.80, a drop of 7.81%. To sum up, the suggested approach may more effectively achieve the best distribution of different energy sources, increase the dependability of VPP operation, and make it more low-carbon.

Grain boundary microstructure-property relationships in the cast & wrought Ni-based superalloy René 41 with boron and carbon additions

Cast & wrought Ni-based superalloys are required for manufacturing high-performance components in gas turbine engines. Their excellent high-temperature properties and corrosion resistance are brought about by their complex microstructure of a γ-matrix, intra- and intergranular γ' precipitates, carbides, and borides. The most highly-alloyed superalloy grades provide the high-temperature strength required for the design of next generation gas turbine engines but their processability remains challenging due to grain boundary (GB) cracking. A common mitigation approach is the addition of B and C to improve GB cohesion and strength. However, their detailed roles in GB segregation and precipitation and the link to GB properties during processing remains largely unexplored.We provide a systematic study on decorated GBs in the cast & wrought Ni-based superalloy René 41 with B and C additions. Tensile testing reveals that B additions reduce its ductility due to GB cracking but slow down grain growth during heat treatments more effectively, than C additions. GB-M2B, M6C, M23C6, and GB-γ' precipitates decorating GBs are sequentially precipitated. Their structures and compositions are shown to be unaffected by B and C additions. However, significant differences in the interfacial segregation of solutes are revealed. B is captured by GB-M2B and therefore unavailable to improve GB cohesion. C depletion and enrichment of solutes at interphase boundaries are identified as origin for the reduced ductility and thus, GB cracking. The findings are summarized as microstructural model and advance the detailed understanding of the processing – microstructure – properties relationships of cast & wrought Ni-based superalloys.

Hybrid Power-Generation Concept Incorporates Offshore Wind

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 204901, “Hybrid Offshore Power Generation,” by Izleena Md. Iqbar, SPE, Fauzi O.B. Othman, and Hasmi Taib, Petronas, et al. The paper has not been peer reviewed. _ The operator has developed an offshore hybrid power-generation concept to leverage and optimize wind-turbine systems for power generation in weak-wind areas such as Malaysia. In this concept, one gas-turbine generator is replaced by an offshore wind turbine adapted to low wind speeds. This lowers maintenance costs and carbon exposure. Additionally, the fuel gas will be diverted to sales gas, thereby improving economics and making offshore renewable power generation feasible for oil and gas platforms. Background Malaysia is situated in a weak-wind area. Most of the operator’s oil and gas platforms are in water depths of more than 60 m, which are considered deep water for offshore wind-turbine systems. Offshore wind-turbine systems generally are expensive and not cost-effective for low-wind-speed areas where the annual average wind speed at hub height is less than 6.5 m/s. For economic reasons, offshore wind turbines are multimegawatt turbines designed for sites of International Electrotechnical Commission Class I or II only. However, a need exists to install CO2-effective, renewable electrical generators with high power density to support industrial plants that cannot follow the wind but are bound to local offshore oil and gas resources. In view of these challenges, specific criteria have been identified for this project, namely specific rated power, maintenance costs, risk of hazards, capability of a black start (the ability of generation to recover from a blackout), and offshore capability. Hybrid Offshore Power-Generation Project From preliminary studies, it was concluded that a reasonable value for specific power is less than 200 W/m² to achieve high-capacity factors in a weak-wind region. The need for low maintenance costs calls for a solution with little technical complexity and a minimum of components. Capability for a black start in an island situation requires storage and parameters for the generator or invertor system of the wind power plant. At the time of writing, no wind turbine can be bought off the shelf for the purposes mentioned. A wind turbine found closest to these criteria is provided by a European manufacturer based in Estonia. The offshore wind-turbine system consists of a customized wind turbine adapted to harness the energy from the weak-wind area and a microgrid system to integrate the wind turbine with the turbine generators, which are housed on a tension-leg-platform (TLP) floating structure. This floating platform is held by a gravity anchor.

Technology Focus: Health, Safety, and Environment (August 2023)

This year’s HSE Technical Focus feature showcases environmental sustainability. Offshore hybrid renewable power generation, electrically driven well abandonment in urban and natural settings, and the use of remotely controlled, low-carbon-footprint underwater intervention/inspection drones demonstrate HSE- and sustainability-conscious planning and successful implementation in the field. Environmental, social, and governance guidelines, as well as regulatory and sustainability-related frameworks, provide guidance to achieve the social license to operate. Risk management and stakeholder engagement practices help predict nontechnical risks and keep the community informed. In paper SPE 204901, which involves using renewable energy sources for offshore oil and gas platform power generation, one operator proposes harnessing wind energy in weak-wind areas by replacing gas-turbine generators with offshore wind turbines adapted to low-wind-speed regions, thereby lowering maintenance costs and carbon exposure. In paper OTC 31394, a service company, acting as project management lead, and an operator collaborate to use fully electrified equipment to abandon wells in sensitive areas such as urban and natural settings where smell and noise are significant issues. In paper OTC 32481, an operator advocates the use of underwater intervention/inspection drones for offshore projects and describes their advantages, including operational risk reduction, lower carbon footprint, lower costs, reduced intervention time, and improved quality and frequency of inspection data. One of the recommended-reading papers, SPE 210788, describes how an operator advocates for mental health or “mental hygiene,” a “preventive measure for sustaining good emotional health” to mitigate workers’ distress in the oil and gas industry and an indication of the growing emphasis upon total wellness. Many more examples of recent notable achievements in HSES can be found in the list of recommended additional reading and in the OnePetro online library. Knowledge sharing, on the local and global levels, encourages collaboration and solutions generation. See the SPE Health, Safety, Environment and Sustainability website for more information and to learn how you could be involved. Recommended additional reading at OnePetro: www.onepetro.org. SPE 210788 Shifting Paradigm of Mental Hygiene—A Novel Approach To Mitigate Workers’ Distress in the Oil and Gas Industry by Adeela Khalid, RPM, et al. SPE 212791 An Improved Work Flow in Mass-Balance Approach for Estimating Regional Methane Emission Rate Using Satellite Measurements by Jeffrey Y. Bian, University of Alberta, et al. SPE 210245 Insights From Benchmarking Methane Emissions of Oil and Natural Gas Production in the United States by Tom Curry, ERM, et al.

Hierarchical control of hybrid energy storage system in shipboard gas turbine power system with multiple pulsed power loads

Transient power fluctuations may occur in shipboard DC microgrids when switching a pulsed power load (PPL). However, shipboard power systems (SPSs) are isolated power grids with relatively low inertia and capacity, making them unable to respond immediately to sudden changes in pulsed loads, thereby resulting in considerable disturbances and even instability of the system. To mitigate such disturbances effectively, it is recommended to introduce energy storage devices to SPSs. In order to better leverage the buffering characteristics of energy storage devices, this paper establishes a simulation model of the SPS, which includes a micro gas turbine generator, a hybrid energy storage system (HESS), multiple PPLs and an energy management system (EMS). The HESS comprises a battery and a flywheel. Furthermore, a hierarchical coordinated control strategy for HESS in shipboard DC microgrids under multiple pulsed load conditions is proposed, which includes both an upper-layer control and a lower-layer control. The upper layer makes logical judgments through voltage difference and selects the appropriate energy storage mode for power supply. The lower layer adopts a series fuzzy logic control. To validate the effectiveness of the proposed control strategy, two scenarios of transient power mutation and total power demand exceeding the rated power of the generator were simulated by connecting different PPLs to the DC bus at different times. The results indicate that the control strategy effectively realizes the coordinated operation of HESS and gas turbine, as well as power allocation between flywheel and battery within HESS. Moreover, it reduces the frequency of charging and discharging of battery, improves battery life, and prevents the generator from running beyond the rated power.

A novel approach for optimal hybrid energy decarbonization using multi-criteria decision analysis: Abu Rudeis, Egypt as a case study

Converting from a complete fossil fuel energy system to a decarbonized one is crucial to mitigating climate change and protecting human health. Hybrid energy sources are better than producing energy from a single technology. The combination of renewable energy and fuel generators allows users to cover seasonal fluctuations of resources and protects them from the unpredictability of fuel prices and supply. Nonetheless, the large-scale industrial demand presents a real challenge due to its consistency throughout the day and the intermittent nature of renewable sources. This research proposes a novel approach for optimal hybrid energy decarbonization for any demand type in general and industrial demand in particular. The proposed framework is developed by integrating the Hybrid Optimization of Multiple Energy Resources (HOMER) for the simulations of Hybrid Configurations (HCs), the Electrical Transient Analysis Program (ETAP) for the stability studies, and the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method to rank the resulting configurations.An industrial oil and gas complex with high wind and solar resource availability is adopted as a study case. It is located in Abu Rudeis, Egypt, and currently utilizes only 11 Gas Turbine Generators (GTGs) to generate electricity. Five different HCs are investigated, including PV and wind systems. The proposed approach considers technical, environmental, economic, and socio-political criteria, with a total of 21 sub-criteria, and reveals that incorporating a wind farm of nine 2-MW wind turbines with the GTGs is the Optimal Hybrid Configuration (OHC).

Thermal-Fluid-Solid coupling mechanism of a novel friction reduction cooling structure for gas turbine

To supply the higher safety design demands of the new generation of gas turbines, it is urgent to solve the difficulties in mid-chord region of high-temperature turbine blades, including excessive friction loss of traditional two-pass channel, lack of experimental data, lack of stress-strain characteristics in the solid domain and so on. Therefore, this research aims to propose an integrated method of the high-efficiency and low-friction cooling design and Thermo-Fluid-Solid (TFS) coupling analysis. Firstly, a turbine blade rotating cooling experimental system is designed and built independently. Then, a novel friction reduction method with cross bridge structure is proposed. The friction reduction mechanism and the effect mechanism of the cooling performance on the solid strain are revealed through experimental test and TFS coupling numerical study. The results show that the cross brige has an excellent friction reduction effect on the channel. The influence of ‘peak cutting and valley filling’ effectively enhances the heat transfer uniformity as well as successfully reduces the overall strain. The average Nu/Nu0 of the leading edge surface decreases by 14.7% (stationary condition) and 16.4% (rotating condition), respectively. And the Nu/Nu0 of the trailing edge surface achieves a maximum improvement effect of 3.3%. The f/f0 of each channel decreases initially and followed by an increase as Ro increasing. Among them, the channel with two and three cross bridges can achieve friction reduction effects of 24.7–37.3% and 32.2–40.9%, respectively. The strain is related to the Nu/Nu0 of each region, and the value of the rotating trailing edge surface is generally higher than that of the rotating leading edge surface.

Dimensioning Air Reactor and Fuel Reactor of a Pressurized CLC Plant to Be Coupled to a Gas Turbine: Part 2, the Fuel Reactor

Bioenergy with Carbon Capture and Storage (BECCS) technologies are fundamental to reach negative CO2 emissions by removing it from the atmosphere and storing it underground. A promising solution to implement BECCS is pressurized Chemical Looping Combustion (CLC), which involves coupling a pressurized CLC reactor system to a turboexpander. The typical configuration chosen is to have an air reactor and a fuel reactor based on coupled circulating fluidized beds. The fluidization regime in both reactors is preferred to be fast fluidization to enhance gas particle contact and solids circulation among reactors. To design the two reactors, Aspen Plus software was used, given that the new version has a module for fluidized bed modeling. At first, the oxygen carrier was designed ex novo, but given that it is a composite compound mainly made by nickel oxide freeze-granulated on alumina (Ni40Al-FG), the molecular structure has been inserted in Aspen Plus. Then, based on the power of the gas turbine, the power output per kg of evolving fluid (in this case, depleted air) is calculated using Aspen Plus. Once the nitrogen content in the depleted air is known, the total air at the inlet of the air reactor is calculated. The fuel reactor is modeled by inserting the reduction reactions for nickel-based oxygen carriers. The paper presents a useful methodology for developing pressurized Chemical Looping Combustors to be coupled to gas turbines for power generation. The provided data will be cross-validated with 0D-models and experimental results.