Search Results (55)

Heuristic optimization is used to find sustainable cogeneration steam power plants with steam reheat and supercritical main steam parameters. Design solutions are analyzed for steam consumer (SC) pressures of 3.6 and 40 bar and a heat flow rate of 40% of the fuel heat flow rate. The objective functions consisted in simultaneous maximization of global and exergetic efficiencies, power-to-heat ratio in full cogeneration mode, and specific investment minimization. For 3.6 bar, the indicators improve with the increase in the ratio between reheating and main steam pressure. The increase in SC pressure worsens the performance indicators. For an SC steam pressure of 40 bar and 9 feed water preheaters, the ratio between reheating and main steam pressure should be over 0.186 for maximum exergetic efficiency and between 0.10 and 0.16 for maximizing both global efficiency and power-to-heat ratio in full cogeneration mode. The average global efficiency for an SC requiring steam at 3.6 bar is 4.4 percentage points higher than in the case with 40 bar, the average specific investment being 10% lower. The Pareto solutions found in this study are useful in the design of sustainable cogeneration supercritical power plants. Full article

The operational efficiency of the main steam pipelines at thermal power plants is reduced due to several factors, including operating temperature, pressure, service life, and the frequency of process shutdowns, which contribute to the degradation of heat-resistant steels. The study aims to identify the features of changes in the sizes of grains and carbides along their boundaries, as well as mechanical properties (hardness, strength, plasticity and fracture toughness) along the wall thickness of both pipes in the initial state and after operation with block shutdowns. Preliminary electrolytic hydrogenation of specimens (before tensile tests in air) showed even more clearly the negative consequences of operational degradation of steel. The degradation of steel was also assessed using fracture toughness (J_IC). The value of J_IC for operated steel with a smaller number of shutdowns decreased by 32–33%, whereas with a larger number of shutdowns, its decrease in the vicinity of the outer and inner surfaces of the pipe reached 65 and 61%, respectively. Fractographic signs of more intense degradation of steel after a greater number of shutdowns were manifested at the stage of spontaneous fracture of specimens by changing the mechanism from transgranular cleavage to intergranular, which indicated a decrease in the cohesive strength of grain boundaries. Full article

Biomass units can play a certain role in peak summer and winter due to their advantages in terms of their environmental and short-term peak ability. To analyze the peak ability of biomass units, this paper focuses on the dynamic modeling of biomass unit peak ability. Firstly, the process of biomass feeding amount to power output is divided into a feed–heat module, heat–main steam pressure module and main steam pressure–power module. A two-input and two-output dynamic model is established where the feeding amount and turbine valve opening serve as inputs, and the main steam pressure and power serve as outputs. Then the effectiveness of the established model is validated by actual operation data of a 30 MW biomass unit. This dynamic model can provide a mechanistic model for analyzing the impact of fuel calorific value on the power output, and provide support for fuel management and scheduling strategies during the peak period of biomass units. Full article

Soil organic pollution poses a significant threat to agricultural safety in China, underscoring the critical importance of developing efficient remediation technologies for soil environmental protection. Steam injection, a promising method for removing organic pollutants from soil, has yet to be thoroughly investigated in terms of its energy efficiency. A novel steam injection system with horizontal wells is proposed to remediate soil xylene pollution, and a corresponding numerical model is established and solved through TOUGH2-T2VOC codes. The energy efficiency characteristics and main influencing factors of the system are analyzed. The results demonstrate that steam injection is an effective method to remediate xylene pollution. It is evaluated that during the first 1.5 years of the 5-year operation period, production xylene saturation gradually decreases from 0.3 to 0.05, and the production xylene mass flow rate gradually decreases from 0.179 kg/s to 2.448 × 10⁻⁴ kg/s. Pump power consumption gradually increases from 17.23 kW to 30.67 kW, while energy efficiency gradually decreases from 7.73 × 10⁻⁴ kg/kJ to 1.00 × 10⁻⁶ kg/kJ. Sensitivity analyses indicate that the main factors affecting the xylene mass flow rate are formation permeability, production pressure and the initial xylene saturation, and the main factors affecting energy efficiency are the steam injection flow rate, formation permeability, production pressure and initial xylene saturation. This has significant practical significance for the optimal design of the steam injection remediation scheme for soil organic pollution. Full article

The pressure fluctuation and the acoustic power generated inside the main steam line (MSL) of a BWR nuclear power plant were estimated. For this purpose, a model with a scale of 1:8 (branch–main steam line ratio) was considered. A methodology with a low computational cost was proposed in this case. It is based on the fluid–structure interaction (one-way type), using computational fluid dynamics, the finite element method, and MATLAB R2023a code. It was possible to obtain the acoustic response generated inside the MSL for different operating conditions using these three tools. These results were used to develop a prediction model with a scale of 1:8. It was validated with experimental data. The frequency of the first mode of acoustic resonance was close to 195 Hz and the peak pressure was between 1590 Pa and 1568 Pa for the experimental and numerical models, respectively. For this case, the conditions were the original license thermal operating. Finally, the predictions of the results for the pressure in conditions of extended power uprate (110% and 120%) were 1890 Pa and 2240 Pa, respectively. Full article

In order to deeply absorb the power generation of new energy, coal-fired circulating fluidized bed units are widely required to participate in power grid dispatching. However, the combustion system of the units faces problems such as decreased control performance, strong coupling of controlled signals, and multiple interferences in measurement signals during flexible operation. To this end, this paper proposes a model predictive control (MPC) scheme based on the extended state Kalman filter (ESKF). This scheme optimizes the MPC control framework. The ESKF is used to filter the collected output signals and jointly estimate the state and disturbance quantities in real time, thus promptly establishing a prediction model that reflects the true state of the system. Subsequently, taking the minimum output signal deviation of the main steam pressure and bed temperature and the control signal increment as objectives, a coordinated receding horizon optimization is carried out to obtain the optimal control signal of the control system within each control cycle. Tracking, anti-interference, and robustness experiments were designed to compare the control effects of ESKF-MPC, ID-PI, ID-LADRC, and MPC. The research results show that, when the system parameters had a ±30% perturbation, the adjustment time range of the main steam pressure and bed temperature loops of this method were 770~1600 s and 460~1100 s, respectively, and the ITAE indicator ranges were 0.615 × 10⁵~1.74 × 10⁵ and 3.9 × 10⁶~6.75 × 10⁶, respectively. The overall indicator values were smaller and more concentrated, and the robustness was stronger. In addition, the test results of the actual continuous variable condition process of the unit show that, compared with the PI strategy, after adopting the ESKF-MPC strategy, the overshoot of the main steam pressure loop of the combustion system was small, and the output signal was stable; the fluctuation range of the bed temperature loop was small, and the signal tracking was smooth; the overall control performance of the system was significantly improved. Full article

The main steam pipe elbow is a critical metallic component in thermal power plants. Due to prolonged exposure to high temperatures and pressures, it experiences microstructural degradation and creep damage, thereby affecting its service life. Currently, there is debate regarding the location of the weakest region within the elbow, with uncertainty over whether it lies in the inner arc or neutral plane area. This study investigates the microstructure and creep properties of both the inner arc and neutral surface regions of an elbow that has been in operation for 183,088 h, aiming to identify the actual weak region and explore the underlying creep damage mechanisms. The results indicate that under identical temperature and stress conditions, samples from the neutral plane region exhibit significantly higher creep rates and shorter creep rupture times compared to those from the inner arc region. This suggests that the creep life in the vicinity of the inner surface in the neutral plane is markedly lower than that in the inner arc region. Microstructural analysis before and after creep fracture reveals that key factors influencing the creep performance of 12Cr1MoV elbows include carbide size, precipitation amount and distribution, grain size and morphology, as well as the stability and uniformity of grain orientation. Specifically, the growth of intragranular precipitates, the accumulation and non-uniform distribution of grain boundary carbides, and the non-uniform distribution of grain sizes all contribute to the rapid formation of creep cracks and premature material failure. This study concludes that the weakest region in the elbow is located at the inner surfaces of the neutral plane. Future inspections and life assessments of thermal power plant elbows should therefore focus on this area to enhance the accuracy of life evaluations and ensure the safety of thermal power plants. Full article

The real-time stress assessment of metal pressure components is one of the key factors in ensuring the safe operation of thermal power plants. To address the challenge of real-time prediction of stress in the key areas of complex special-shaped metal pressure-bearing components in a certain domestic 300 MW thermal power plant, three typical complex metal pressure-bearing components, the main steam pipe tee (MSPT), the steam drum downcomer joint (DDJ) and the header ligament (HL), were taken as research objects. The stress distribution of the three complex metal pressure-bearing components under different conditions was analyzed through the finite element method, and the stress results at the dangerous points were used as samples to establish training sample data. Subsequently, different machine learning methods were employed to train the sample data. The training results indicate that neural networks (NNs) and the Auto-Sklearn Regression (ASR) models can accurately predict the stress of the key parts of complex metal pressure-bearing components in real time. The ASR method demonstrates better performance in stress prediction of the main steam pipe tee, with a prediction accuracy of ≥96%. The NN model shows better prediction for the header ligament, with a prediction accuracy of ≥94%. These research findings provide effective support for the high-temperature lifespan assessment and safe operation of thermal power plants. Full article

Steam thermal power plants represent important energy production systems. Within the energy mix, these could allow flexible generation and the use of hybrid systems by integrating renewables. The optimum design solution and parameters allow higher energy efficiency and lower environmental impact. This paper analyzes single reheat supercritical steam power plants design solutions using a genetic heuristic algorithm. A multi-objective optimization was made to find the Pareto frontier that allows the maximization of the thermal cycle net efficiency and minimization of the specific investment in the power plant equipment. The Pareto population was split and analyzed depending on the total number of preheaters. The mean values and the standard deviations were found for the objective functions and main parameters. For the thermal cycle schemes with eight preheaters, the average optimal thermal cycle efficiency is (48.09 ± 0.16)%. Adding a preheater increases the average optimal thermal cycle efficiency by 0.64%, but also increases the average optimum specific investments by 7%. It emphasized the importance of choosing a proper ratio between the reheating and the main steam pressure. Schemes with eight and nine preheaters have an average optimum value of 0.178 ± 0.021 and 0.220 ± 0.011, respectively. The results comply with data from the literature. Full article

The objective of this study is to perform a thermodynamic analysis on a marine diesel engine waste heat-assisted cogeneration power plant modified with regeneration onboard a ship. The proposed system utilizes the waste heat from the main engine jacket water and exhaust gases to generate electricity and heat, thereby reducing the fuel consumption and CO₂ emissions. The methodology includes varying different turbine inlet pressures, extraction pressures, and fractions of steam extracted from the turbine to evaluate their effects on the efficiency, utilization factor, transformation energy equivalent factor, process heat rate, electrical power output, saved fuel flow rate, saved fuel cost, and reduced CO₂ emissions. The analysis demonstrates that the proposed system can achieve an efficiency of 48.18% and utilization factor of 86.36%, savings of up to 57.325 kg/h in fuel, 65.606 USD/h in fuel costs, and 180.576 kg/h in CO₂ emissions per unit mass flow rate through a steam turbine onboard a ship. Full article

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. Full article

In Nuclear Power Plants (NPPs), online condition monitoring and the fault detection of structures, systems and components (SSCs) can aid in guaranteeing safe operation. The use of data-driven methods for these tasks is limited by the requirement of physically consistent outcomes, particularly in safety-critical systems. Considering the importance of regulating valves (e.g., safety relief valves and main steam isolation valves), this work proposes a multistage Physics-Informed Neural Network (PINN) for fault detection in such components. Two stages of the PINN are built by developing the process model of the regulating valve, which integrates the basic valve sizing equation into the loss function to jointly train the two stages of the PINN. In the 1st stage, a shallow Neural Network (NN) with only one hidden layer is developed to estimate the equivalent flow coefficient (a key performance indicator of regulating valves) using the displacement of the valve as input. In the 2nd stage, a Deep Neural Network (DNN) is developed to estimate the flow rate expected in normal conditions using inputs such as the estimated flow coefficient from the 1st stage, the differential pressure, and the fluid temperature. Then, the residual, i.e., the difference between the estimated and measured flow rates, is fed into a Deep Support Vector Data Description (DeepSVDD) to detect the occurrence of faults. Moreover, the deviation between the estimated flow coefficients of normal and faulty conditions is used to interpret the consistency of the detection result with physics. The proposed method is, first, applied to a simulation case implemented to emulate the operating characteristics of regulating the valves of NPPs and then validated on a real-world case study based on the DAMADICS benchmark. Compared to state-of-the-art fault detection methods, the obtained results from the proposed method show effective fault detection performance and reasonable flow coefficient estimation, thus guaranteeing the physical consistency of the detection results. Full article

Considering energy conversion efficiency, pollution emissions, and economic benefits, combining biomass with fossil fuels in power generation facilities is a viable approach to address prevailing energy deficits and environmental challenges. This research aimed to investigate the thermodynamic and exergoeconomic performance of a novel power and cooling cogeneration system based on a natural gas–biomass dual fuel gas turbine (DFGT). In this system, a steam Rankine cycle (SRC), a single-effect absorption chiller (SEAC), and an organic Rankine cycle (ORC) are employed as bottoming cycles for the waste heat cascade utilization of the DFGT. The effects of main operating parameters on the performance criteria are examined, and multi-objective optimization is accomplished with a genetic algorithm using exergy efficiency and the sum unit cost of the product (SUCP) as the objective functions. The results demonstrate the higher energy utilization efficiency of the proposed system with the thermal and exergy efficiencies of 75.69% and 41.76%, respectively, while the SUCP is 13.37 $/GJ. The exergy analysis reveals that the combustion chamber takes the largest proportion of the exergy destruction rate. The parametric analysis shows that the thermal and exergy efficiencies, as well as the SUCP, rise with the increase in the gas turbine inlet temperature or with the decrease in the preheated air temperature. Higher exergy efficiency and lower SUCP could be obtained by increasing the SRC turbine inlet pressure or decreasing the SRC condensation temperature. Finally, optimization results indicate that the system with an optimum solution yields 0.3% higher exergy efficiency and 2.8% lower SUCP compared with the base case. Full article

In this study, a comprehensive examination was conducted to explore the technology involved in the recovery of waste heat from flue gas emitted by a 1000 MW unit. Traditional methods are constrained in their ability to harness waste heat from flue gas solely for the purpose of generating medium-temperature water. The system being examined not only recovers waste heat but also utilizes it to generate steam, thereby greatly improving resource efficiency. The process entails utilizing the flue gas to heat water to a certain temperature, followed by subjecting it to flash evaporation. This process leads to the generation of low-pressure waste heat steam. Within the steam ejector, the waste heat steam combines with high-pressure motive steam extracted from the source, resulting in the formation of medium-pressure steam. Within the steam ejector, the waste heat steam blends with high-pressure motive steam drawn from the source, forming medium-pressure steam that eventually feeds into the A8 steam extraction pipe (low-pressure turbine pumping pipe). The present study examines the fluctuation patterns in motive steam flow, suction coefficient, waste heat steam volume, and outlet temperature of the flue water heat exchanger when different motive steam sources are used. Additionally, the research calculates the reduction in CO₂ emissions, the coal consumption for power supply, and the cost savings in fuel for the retrofitted system. The findings indicate that maximizing energy utilization can be achieved by operating the retrofitted unit at the lowest feasible waste heat steam pressure. The implementation of the new system has resulted in a substantial decrease in coal consumption for power supply. When employing main steam as the extraction steam source, the consumption of coal for power generation decreases in proportion to the decrease in waste heat steam pressure while maintaining a constant unit load. When the waste heat steam pressure reaches 0.0312 MPa, the recorded coal consumption for power generation varies between 289.43 g/kWh at 100% turbine heat acceptance (THA) and 326.94 g/kWh at 30%THA. When comparing this performance with the initial thermal power plant (TPP) unit, it demonstrates reductions of 2.26 g/kWh and 1.52 g/kWh, respectively. After implementing modifications to this 1000 MW unit, it is projected that the annual CO₂ emissions can be effectively reduced by 6333.97 tons, resulting in significant cost savings of approximately USD 0.23 million in fuel expenses. This system exhibits considerable potential in terms of emission reduction and provides valuable insights for thermal power plants aiming to decrease unit energy consumption. Full article

Super-critical Carbon dioxide (s-CO₂) power plants are considered to be efficient and environmentally friendly compared to the traditional Rankine cycle-based steam power plants and Brayton cycle-based gas turbine power plants. In this work, the system design of a coal-fired 100 MWe double reheat s-CO₂ power plant is presented. The system is also optimized for efficiency with turbine inlet pressures and the recompression ratio as the variables. The components needed, mass flow rates of various streams and their pressures at various locations in the system have been established. The plant has been studied based on 1st and 2nd laws at full load and at part loads of 80%, 60% and 40%. Operating parameters such as mass flow rate, pressure and temperature have considerably changed in comparison to full load operation. It was also observed that the 1st law efficiency is 53.96%, 53.93%, 52.63% and 50% while the 2nd law efficiency is 51.88%, 51.86%, 50.61% and 48.1% at 100%, 80%, 60% and 40% loads, respectively. The power plant demonstrated good performance even at part loads, especially at 80% load, while the performance deteriorated at lower loads. At full load, the highest amount of exergy destruction is found in the main heater (36.6%) and re-heaters (23.2% and 19.6%) followed by the high-temperature recuperator (5.7%) and cooler (4.1%). Similar trends were observed for the part load operation. It has been found that the recompression ratio should be kept high (>0.5) at lower loads in order to match the performance at higher loads. Combustion and heat exchange due to finite temperature differences are the main causes of exergy destruction, followed by pressure drop. Full article