Steam Turbine System Research Articles

The Simulator of China General Nuclear Power Group (CGNPC) Delingha 50 MW Parabolic Trough Solar Thermal Power Plant

CGN Delingha 50MW parabolic trough solar power plant is China's first commercial trough solar power station. There are 190 heat collecting loops in the solar field of the power plant. Due to the mutual coupling of heat collecting loops layout location, environmental conditions, medium flow, working medium physical property changes and other factors, the hydrodynamic characteristics of the solar field are complex. In practical operation, balancing the flow of the heat absorbing medium in each loop of the large-scale trough solar thermal power plant and stabilizing the outlet medium temperature are difficult problems. Taking CGN Delingha 50MW parabolic trough solar power plant as the research object, this paper adopts the idea of modular modeling to establish the mathematical model of the main equipment in different systems of the power plant, to build the dynamic model of its solar field, steam generation system, heat storage system and steam turbine system. Based on the parallel computing function of the real-time dynamic simulation platform STAR-90, the real-time coupling calculation between different systems is realized. The error of the built model in steady-state is less than 2%, and the dynamic results of the model are in good agreement with the actual operation data. The modeling method in this paper is universal and can be a reference for dynamic modeling of other large-scale trough solar thermal power plants.

Comprehensive Insight into the Failure Mechanisms, Modes, and Material Selection of Steam Turbine Blades

Addressing the critical issue of turbine blade failures, particularly in steam turbine systems, this comprehensive review delves into examining and analyzing various failure mechanisms associated with steam turbine blades. The discussion extends to the mode utilized in investigating these failures, with a specific focus on the crucial role of steam turbines in power generation. The review synthesizes various studies that have explored suitable blade materials for optimized performance and reduced failure risks. Findings reveal common failures such as thermal stress, mechanical stress, corrosion, erosion, fatigue, and creep in turbine blades and hubs. Consequently, the review serves as a valuable resource, providing insights into failure mechanisms and advocating for the implementation of suitable materials to enhance the reliability and performance of steam turbine blades.

A Reduced-Order Model of a Nuclear Power Plant with Thermal Power Dispatch

This paper presents reduced-order modeling of thermal power dispatch (TPD) from a pressurized water reactor (PWR) for providing heat to nearby heat consuming industrial processes that seek to take advantage of nuclear heat to reduce carbon emissions. The reactor model includes the neutronics of the reactor core, thermal–hydraulics of the primary coolant cycle, and a three-lump model of the steam generator (SG). The secondary coolant cycle is represented with quasi-steady state mass and energy balance equations. The secondary cycle consists of a steam extraction system, high-pressure and low-pressure turbines, moisture separator and reheater, high-pressure and low-pressure feedwater heaters, deaerator, feedwater and condensate pumps, and a condenser. The steam produced by the SG is distributed between the turbines and the extraction steam line (XSL) that delivers steam to nearby industrial processes, such as production of clean hydrogen. The reduced-order simulator is verified by comparing predictions with results from separate validated steady-state and transient full-scope PWR simulators for TPD levels between 0% and 70% of the rated reactor power. All simulators indicate that the flow rate of steam in the main steam line and turbine systems decrease with increasing TPD, which causes a reduction in PWR electric power generation. The results are analyzed to assess the impact of TPD on system efficiency and feedwater flow control. Due to the simplicity of the proposed reduced-order model, it can be scaled to represent a PWR of any size with a few parametric changes. In the future, the proposed reduced-order model will be integrated into a power system model in a digital real-time simulator (DRTS) and physical hardware-in-the-loop simulations.

Forest resource distribution and transport cost optimization-based economic evaluation of gasification and steam-turbine biomass power generation systems

The economics of decentralized gasification and centralized steam turbine systems with optimized harvesting, chipping, and transportation costs of unutilized forest biomass were analyzed within the context of the spatial distribution of forest biomass resources using a geographic information system in the Fuji area of Shizuoka Prefecture, Japan. The optimized harvesting, chipping, and transportation costs were 62.50 USD/dry-t for the decentralized system and 65.86 USD/dry-t for the centralized system, confirming that the decentralized system was less expensive than the centralized system. However, electricity production costs for centralized systems, ranging from 0.137 USD/kWh (capacity: 5,000 kW), were found to be more economical than that for decentralized systems, at 0.151–0.165 USD/kWh (capacity: 482 kW), resulting in the higher costs of decentralized systems because of expensive initial investment than that of the centralized system. The rate of heat sold, which lowers the electricity production costs of gasification below those of centralized systems, was 20–40 % for all such plants. To lower the electricity production cost of gasification plants below that of steam-turbine systems, a heat sales rate of approximately 10% and a minimum staff of 0.2 persons for remote operation of the gasification system. Higher heat sales rates further decrease the cost of gasification, making it cheaper than steam-turbine systems even at lower operating rates. To achieve this cost advantage at current low utilization rates, a heat sales rate of 50% and a utilization rate of 90%, based on a unit price of 0.05 USD/kWh, are required.

Heat transport and load response characteristics of a molten salt solar tower power station engaged in peak regulation

Power system flexibility can be improved effectively, if the advantages of the peak shaving ability of molten salt solar tower power (STP) plant can be developed and utilized. In this paper, the heat transport and load response characteristics of the molten salt STP plant in the regulation process are studied, aiming at serving the development of the regulation method in the rapid peak regulation process. The steam generation system and turbine system models of a 50 MW STP station are established. Two load regulation methods and operation modes are used to investigate the inertia and delay effects of heat transfer and evaporation, as well as load response characteristics during the actual regulation operation, including steam generation system following (SF), turbine following (TF), constant pressure operation (CP), and sliding pressure operation (SP). The results show that when the plant load is reduced by 10% Pe at the rate of 3% Pe/min, the heat transfer of salt/water and the evaporation process of water in the whole regulation process have a 250 s delay compared with the load. The larger heat storage characteristics of the plant help to improve the load response speed. The load response speed of the plant is faster in SF mode. TF mode is beneficial to the safe operation of the plant. Considering the operational safety of thick-wall component, the recommended running mode is the collocation of SF and SP mode, TF and CP mode. The results show that the load change rate of 10% Pe/min is permittable under TF and CP modes. The conclusion of this paper lays a foundation for the formulation of a peak regulation strategy for molten salt STPs.

Turkey’s Municipal Solid Waste and Urban Waste Water Treatment Sludge Electrical Energy Potential

The role of indigenous and renewable energy resources is crucial for countries that want to be less dependent on foreign countries. Renewable energy resource power plants have many advantages when compared with other resources. They make countries less energy dependent and they also have environmentally friendly technologies when compared with other resources. Turkey is a country that has an abundance of renewable energy resources and biomass is one of the most promising energy resources in renewables. The goal of this paper is to determine Turkey’s electricity potential based on industrial wastes i.e. municipal solid waste (MSW) and urban wastewater treatment sludge potential, using applicable electricity generation methods. In this attempt, electrical energy potential of these resources has been found by using applicable waste to energy methods, technical and economical parameters. Electrical energy potential of Biogas (Landfill gas included) based on these resources is an amount of 6.73 billion kWh/year. Municipal solid waste (MSW) and dried municipal wastewater treatment sludge electrical energy potential is an amount of 23.81 billion kWh/year. Based on the resources stated; using the biogas and biomass energy values, calculations resulting in primary energy potentials; the installed capacity for gas-motor systems, gas turbine systems (Simple cycle), gas & steam turbine systems (combined) and boiler – steam turbine systems have been determined.

Gain Scheduled PI controller design using Multi-Objective Reinforcement Learning

Gain scheduling, a widely adopted control design approach, performs tasks by decomposing into sub-problems. It has found successful applications across diverse fields, including aerospace and industrial process control (Leith and Leithead (2000)). Reinforcement Learning (RL) has become increasingly significant in process control applications, thanks to the advancement of sophisticated algorithms and the rise in computational speeds. An attempt has been made to enhance the performance of a Proportional-Integral (PI) controller by integrating the benefits of both gain scheduling and RL. The manuscript focuses on designing a Gain Scheduled PI controller using Multi-Objective RL (GS-MORL). Proposing the training of multiple RL-based PI controllers with distinct objectives with customized reward functions to derive tuning parameters for each goal. The switching between these parameter sets is facilitated by a gain schedule variable. The controller parameters are adjusted based on the error between actual and desired responses. The aim is to enhance overall performance across various performance indices, including the integral of absolute error and time response specifications. The performance improvement from the proposed work has been demonstrated using two simulation case studies, one involving an underdamped second-order system and an integrating type steam turbine system. Comparative analysis against existing literature revealed superior performance across all indices.

Energy, exergy and thermo-economics analyses of hybrid solar, steam turbine and biomass gasification system for hydrogen production by polymer membrane electrolyzer

This study presents an evaluation of the energy exergy and thermo-economics of a hybrid power generation system that simultaneously produces power, heat, and hydrogen using solar and biomass energy. The system employs a polymer membrane electrolyzer for hydrogen production, with heat exchangers utilized to attain the desired water temperature instead of conventional heaters. The steam turbine section is heated using exhaust gas from the combustion chamber and heated water from the condenser. Simulation of the system's thermodynamic, exergy, and exergy-economic analyses was conducted using the ESS tool. Analytical assessments were performed based on the provided data. The system achieves a power output of 25 MW, marking the highest level for solar-based systems. The results showed that the cost of the solar section, which constitutes around 68 % of the overall cost, rises with the number of mirrors. The exergy destruction rate of the entire system decreases from 25.71 MW to 24.95 MW. The total cost of operating the system increases by 175.5 dollars per hour at the highest temperature and decreases by 173 dollars per hour at the lowest temperature. The power consumption of electrolyzers directly affects the overall cost of the system, with a range of 0.05 to 0.07 MW resulting in total cost changes of 1.52 USD per hour. The transition to the work requiring the most energy by the electrolyzer causes a loss of 0.27 MW of exergy.

Digital twin modeling and operation optimization of the steam turbine system of thermal power plants

The increasing deployment of renewable energy sources necessitates peak regulation services from thermal power plants, impacting their energy efficiency. Central to these plants, the steam turbine system significantly influences their operational efficiency. A digital twin model of this system was developed, integrating mechanism-driven and data-driven modeling methods. The neural network data-driven approach was specifically utilized for parameters such as feedwater pump speed and steam flow rate to the pump turbine. Other parameters were modeled with mechanism data hybrid driven modeling method. This model computes vital metrics such as low-pressure turbine exhaust steam enthalpy, work done and heat absorption per unit mass of steam, system efficiency, feedwater mass flow rate, and water-coal ratio—key for evaluating and enhancing the system's energy efficiency. An investigation into a reference case showed a decline in efficiency below design levels due to aging. By optimizing the live steam pressure and the cold-end system, relative improvements in energy efficiency of 0.35 % and 0.14 %, respectively, were achievable.

Reduction of CO2 Emissions from Offshore Combined Cycle Diesel Engine-Steam Turbine Power Plant Powered by Alternative Fuels

Abstract Diverse forms of environmental pollution arise with the introduction of materials or energy that exert adverse effects on human health, climate patterns, ecosystems, and beyond. Rigorous emission regulations for gases resulting from fuel combustion are being enforced by the European Union and the International Maritime Organization (IMO), directed at maritime sectors to mitigate emissions of SOx, NOx, and CO2. The IMO envisions the realisation of its 2050 targets through a suite of strategies encompassing deliberate reductions in vessel speed, enhanced ship operations, improved propulsion systems, and a transition towards low and zero-emission fuels such as LNG, methanol, hydrogen, and ammonia. While the majority of vessels currently depend on heavy fuel or low-sulphur fuel oil, novel designs integrating alternative fuels are gaining prominence. Technologies like exhaust gas purification systems, LNG, and methanol are being embraced to achieve minimised emissions. This study introduces the concept of a high-power combined ship system, composed of a primary main engine, a diesel engine, and a steam turbine system, harnessing the energy contained within the flue gases of the main combustion engine. Assumptions, constraints for calculations, and a thermodynamic evaluation of the combined cycle are outlined. Additionally, the study scrutinises the utilisation of alternative fuels for ship propulsion and their potential to curtail exhaust emissions, with a specific focus on reducing CO2 output.

Deep Q‐learning recommender algorithm with update policy for a real steam turbine system

AbstractIn modern industrial systems, diagnosing faults in time and using the best methods becomes increasingly crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning (DL) have proposed various methods for data‐based fault diagnosis, and the authors are looking for the most reliable and practical ones. A framework based on DL and reinforcement learning (RL) is developed for fault detection. The authors have utilised two algorithms in their work: Q‐Learning and Soft Q‐Learning. Reinforcement learning frameworks frequently include efficient algorithms for policy updates, including Q‐learning. These algorithms optimise the policy based on the predictions and rewards, resulting in more efficient updates and quicker convergence. The authors can increase accuracy, overcome data imbalance, and better predict future defects by updating the RL policy when new data is received. By applying their method, an increase of 3%–4% in all evaluation metrics by updating policy, an improvement in prediction speed, and an increase of 3%–6% in all evaluation metrics compared to a typical backpropagation multi‐layer neural network prediction with comparable parameters is observed. In addition, the Soft Q‐learning algorithm yields better outcomes compared to Q‐learning.

Adaptive back-stepping Control of Electro-hydraulic Servo system of Steam Turbine

The parameters such as oil density and bulk elastic modulus of hydraulic oil in steam turbine electro-hydraulic servo system vary with ambient temperature, pressure, and other factors, and there are unmodeled dynamics in the system, so obtaining a satisfactory following effect is a challenging task for the electro-hydraulic servo(EHS) system. According to the principle of force balance and flow continuity, the mathematical model of a bilateral oil intake motor is established in this paper. On this basis, a back-stepping controller is designed, and an adaptive method is used to estimate uncertain parameters and disturbances. The designed control system is simulated and verified, and compared with the RBF control system after parameter optimization. The results show that the controller designed in this paper has good tracking performance and adaptability to uncertain parameters and external load disturbances.

Performance Analysis of Combined Gas-Electric Steam Turbine System as Main Propulsion for Small-scale LNG Carrier Ships

Performance Analysis of Combined Gas-Electric Steam Turbine System as Main Propulsion for Small-scale LNG Carrier Ships

Repowering a Coal Power Plant Steam Cycle Using Modular Light-Water Reactor Technology

This article presents the results of a techno-economic analysis of repowering a coal-fired power plant’s steam turbine system to instead accept heat produced by a pressurized water reactor-type small modular nuclear system (PWR SMR). This type of repowering presents a challenge due to the significantly lower steam pressure and temperature produced by the nuclear system. A 460 MW supercritical power unit with steam parameters of 28 MPa/560 °C/580 °C, operated in the Łagisza Power Plant in Poland, was selected for the analysis. After repowering, the turbine system would be fed with saturated steam from the steam generators of the SMRs at a pressure of 7 MPa and a temperature of 285 °C. In total, four options for repowering were analyzed. In all cases, the existing high-pressure section of the turbine was disconnected, and the existing low-pressure stages of the turbine, as well as all auxiliary and outward components (feedwater heaters, pumps, generator, condenser, condenser cooling, etc.), are re-used in their existing configurations, except for a feedwater-heater pump that needs to be replaced. In three cases, the existing intermediate pressure turbine section acts as the high-pressure stage of the repowered system. These cases include repowering without an additional reheater (case A), with an added single-stage reheater (B) and with an added two-stage reheater (C). In the fourth case (D), the existing intermediate pressure section was replaced by a new high-pressure turbine stage suited to the SMR live steam conditions. While all four repowering options are technically possible and may represent an economic advantage compared to a complete greenfield SMR installation, option D with a new high-pressure stage is clearly the best option available, with significant cost savings, leading to a lower levelized cost of electricity (LCOE) and a higher net present value (NPV) and net present value ratio (NPVR) than the greenfield case and all other repowering. For relatively new coal power plants with equipment in good condition, this type of repowering may present a cost optimal near-term pathway.

Research on optimal scheduling of thermal power system in thermal power plant based on deep reinforcement learning

Optimal dispatch is one of the key technologies to realize the efficient and economical operation of the thermal power system in thermal power plants. In order to reduce the energy consumption of thermal power system in thermal power plants, ensure the optimal dispatching effect and improve the efficiency of optimal dispatching, this paper introduces deep reinforcement learning to design a new optimal dispatching method for thermal power system in thermal power plants. The thermal power system structure of thermal power plant is analyzed, and the models of boiler, steam turbine and temperature and pressure reducer are established. The optimal scheduling problem of steam turbine and boiler thermal system is studied. By setting the objective function and determining the constraint function, the relevant optimal scheduling model is constructed. The SAC algorithm in deep reinforcement learning is used to solve the model to achieve the important goal of optimal scheduling. The experimental results show that the total fuel consumption of the proposed method is small, and the proposed method has a better optimal scheduling effect of thermal power system in thermal power plants, and can effectively improve the optimal scheduling efficiency.

Study on the sealing test method of hydraulic solenoid valve in nuclear power station

In order to improve the reliability of tripping solenoid valve and switching solenoid valve of steam turbine valve in nuclear power plant and reduce abnormal valve closing. According to the working principle of valves in nuclear power plant, this paper designs a kind of electromagnetic valve sealing test system, including test base, measurement and control system and hydraulic system. The detection system is an offline system. By simulating the real load condition of the steam turbine system, the tightness of the tripping solenoid valve and the switching solenoid valve is tested to improve the reliability of the solenoid valve. The actual application results show that the system operates stably and reliably, is simple to operate, has high detection efficiency and accuracy, and has good use effect.

Dynamic modeling and coupling characteristics analysis of biomass power plant integrated with carbon capture process

Biomass direct-fired circulating fluidized bed boiler has distinguished advantages of strong fuel adaptability, high combustion efficiency and low NOX emission, thus has been regarded as one of the important pathways for biomass utilization. Retrofitting the biomass direct-fired circulating fluidized bed boiler power plant (BCFBP) with carbon capture can strongly support the goal of carbon neutrality, because negative CO2 emission is achieved during the power generation. Nevertheless, the biomass direct-fired circulating fluidized bed boiler is sensitive to the combustion condition, high in operating costs, slow in dynamic response and poor in regulating capability. The integration of carbon capture may pose significant changes on the operating safety, efficiency and flexibility. A comprehensive analysis is thus required to understand the interactions between the BCFBP and carbon capture under various operating conditions. To this end, this paper develops a plant-wide dynamic model for a 130 ton/h BCFBP integrated with solvent-based post-combustion carbon capture (PCC) system. Multi-stage models of biomass combustion, steam generation and steam turbine-feedwater heater systems are developed to represent the dynamic interactions among biomass, heat, electricity and carbon, so that the impacts of BCFBP operation on the PCC process can be evaluated. Detailed BCFBP-PCC connecting system model are also developed to reflect the dynamic changes of steam/water flowrate, pressure and temperature in the steam turbine system caused by the integration of PCC. The developed model is then used to analyze the system interactions, operating costs, negative carbon capability and load ramping flexibility of the integrated BCFBP-PCC. Results show that the integration of PCC 1) causes significant impacts on the water/steam parameters of the steam turbine-feedwater heaters behind the reboiler steam extraction point; 2) reduces the power output of BCFBP by 26.12 % under 95 % CO2 capture level condition; 3) expands the power output variation range of the BCFBP from 22.3 to 36.6 MWe and provides the negative CO2 emission capacity from 2.2 to 6.9 kg CO2/s; 4) offers a feasible way to improve the power ramping performance through flexible distribution of the water/steam in the steam turbine-feedwater heater system. This paper provides specific guidance for the design, retrofit and operation of the BCFBP-PCC plant.

Stability analysis of the turbine rotor system with foundation support under impact excitation

Considering the stability changes of the whole steam turbine system with and without shock excitation, based on the combined methods of modeling simulation, multi-point analysis and multi-feature map analysis, the support structure of the whole steam turbine rotor under the action of unbalance and shock excitation is analyzed. Stability changes were analyzed. By analyzing the time-domain waveform, frequency spectrum and axis locus of the model casing, the typical support points of the high-pressure rotor section and the low-pressure rotor section under the sudden shock excitation and unbalanced force, the time-domain waveform, frequency spectrum and axis locus of the axis can be grasped. The influence on the structural stability and vibration characteristics of the steam turbine can provide a reference for the shock resistance design of the whole steam turbine system.

Optimisation of cogeneration system design with extended automated targeting model (ATM)

In this work, a mathematical optimisation technique known as automated targeting model (ATM) was extended for techno-economic evaluation of gas turbine (GT), steam turbine and combined cycle CHP systems. The objective is to introduce a systematic methodology for rigorous targeting of steam and power generation from complex CHP systems. In particular, the non-linear model of GT was linearised and incorporated for use in ATM. The extended ATM was applied to target minimum total annualised cost (TAC) for CHP systems in various scenarios. The extended technique was demonstrated with case studies of glycerine distillation and oleochemical plants. For the glycerine distillation plant, the result showed that combined cycle CHP system is the most cost-effective and environmental friendly scheme for the scenario, with minimum TAC of $ 5,668,000/y and carbon footprint of 69,008 t CO2e/y. In Scenario 1 of the oleochemical plant case study, with constant steam and power demand, the result showed that GT-CHP is the most cost-efficient scheme with minimum TAC and carbon footprint of $ 2,184,000/y and 24,363 t CO2e/y respectively. Meanwhile, for Scenario 2 with fluctuating steam and power demand according to plant production capacity, which is more realistic, its minimum TAC for GT-CHP only increases by 2 %. The new approach allows the identification of the optimum mode of operation at different demand conditions.

Steady-State Operation Characteristics of 100 MW Class Steam Turbine System

Steady-State Operation Characteristics of 100 MW Class Steam Turbine System