Search Results (626)

The study presents a comprehensive assessment of the combustion efficiency of low-grade coal from the Karazhyra deposit in small- and medium-capacity boiler units of the energy workshops operated by Vostokenergo LLP (East Kazakhstan Region, Kazakhstan). It was found that the average annual thermal energy output amounts to 2,387,348.85 GJ with a coal consumption of 164,328.5 tons. Based on operational data from 2016 to 2017, the average thermal efficiency (boiler efficiency) was 66.03%, with a maximum value of 75% recorded at the Zhezkent energy workshop. The average lower heating value (LHV) of the coal was 19.41 MJ/kg, which is below the design value of 20.52 MJ/kg, indicating the use of coal with reduced energy characteristics and elevated ash content (21.4%). The unburned carbon content in the ash and slag waste (ASW) was determined to be between 14 and 35%, indicating incomplete combustion. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses revealed the presence of microspheres, porous granules, and coal residues, with silicon and aluminum oxides dominating the composition (up to 70.49%). Differences in the pollutant potential of ash from different boiler units were identified. Recommendations were substantiated regarding the adjustment of the air–fuel regime, modernization of combustion control systems, and utilization of ASW. The results may be used to develop measures aimed at improving the energy efficiency and environmental safety of coal-fired boiler plants. Full article

Vehicle emissions, as a source of air pollution and greenhouse gases, have a significant impact on the environment and climate change. Battery electric vehicles (BEVs) have the potential to reduce air pollution and GHGs. However, BEVs often attract the criticism that their benefits are minimal as the power plant emissions compensate for emissions from the tailpipes of vehicles. This study compared two scenarios: scenario A considers all vehicles as internal combustion engine vehicles (ICEVs), and scenario B considers all vehicles as BEVs. The study used the City of San Antonio, Texas, as the study area. The study also focused on the seasonal and spatial variation in ICEV emissions. The results indicate that scenario A has a considerably higher volume of emissions than scenario B. For ICEVs, PM2.5 emissions were up to 50% higher in rural areas than urban areas, but 45% lower for unrestricted versus restricted conditions. CO₂ emissions were highly affected by seasonal variations, with a 51% decrease from winter to summer. The full adoption of BEVs could reduce CO₂ and N₂O emissions by 99% and 58% per km, especially for natural gas power resources. Therefore, BEVs play a significant role in reducing emissions from the transportation sector. Full article

Co-firing large-sized straw biomass in pulverized coal boilers is a potential pathway for carbon emission reduction in China’s thermal power plants. However, experimental data on large-sized straw combustion under pulverized coal boiler combustion conditions are critically lacking. This study selected typical large-sized wheat straw particles. Employing a two-mode experimental setup in a drop tube furnace (DTF) system simulating pulverized coal boiler conditions, we systematically investigated the combustion behavior and alkali metal release characteristics of this large-sized straw biomass, with combustion processes summarized for diverse particle types. The findings reveal asynchronous combustion progression across particle surfaces due to heterogeneous mass transfer and gas diffusion; unique behaviors distinct from denser woody biomass, including bending deformation, fiber branching, and fragmentation, occur; significant and morphology-specific deformations occur during devolatilization; fragmentation universally produces particles of varied shapes (needle-like, flaky, blocky, semi-tubular) during char combustion; and potassium release exceeds 35% after complete devolatilization and surpasses 50% at a burnout degree exceeding 80%. This work provides essential experimental data on the fundamental combustion characteristics and alkali metal release of large-sized wheat straw particles under pulverized coal boiler combustion conditions, offering engineering application guidance for the direct co-firing of large-sized flexible straw biomass in pulverized coal boilers. Full article

To compensate for the instability of renewable energy sources during China’s energy transition, large thermal power plants must provide critical operational flexibility, primarily through deep peaking. To investigate the combustion performance and wear and tear of a 600 MW pulverized coal boiler under deep peaking, the gas–solid flow characteristics and distributions of flue gas temperature, wall heat flux, and wall wear rate in a 600 MW tangentially fired pulverized coal boiler under variable loads (353 MW, 431 MW, 519 MW, and 600 MW) are investigated in this study employing computational fluid dynamics numerical simulation method. Results demonstrate that increasing the boiler load significantly amplifies gas velocity, wall heat flux, and wall wear rate. The maximum gas velocity in the furnace rises from 20.9 m·s⁻¹ (353 MW) to 37.6 m·s⁻¹ (600 MW), with tangential airflow forming a low-velocity central zone and high-velocity peripheral regions. Meanwhile, the tangential circle diameter expands by ~15% as the load increases. The flue gas temperature distribution exhibits a “low-high-low” profile along the furnace height. As the load increases from 353 MW to 600 MW, the primary combustion zone’s peak temperature rises from 1750 K to 1980 K, accompanied by a ~30% expansion in the coverage area of the high-temperature zone. Wall heat flux correlates strongly with temperature distribution, peaking at 2.29 × 10⁵ W·m⁻² (353 MW) and 2.75 × 10⁵ W·m⁻² (600 MW) in the primary combustion zone. Wear analysis highlights severe erosion in the economizer due to elevated flue gas velocities, with wall wear rates escalating from 3.29 × 10⁻⁷ kg·m⁻²·s⁻¹ (353 MW) to 1.23 × 10⁻⁵ kg·m⁻²·s⁻¹ (600 MW), representing a 40-fold increase under full-load conditions. Mitigation strategies, including ash removal optimization, anti-wear covers, and thermal spray coatings, are proposed to enhance operational safety. This work provides critical insights into flow field optimization and wear management for large-scale coal-fired boilers under flexible load operation. Full article

Nodular Cast Iron (NCI, also known as ductile iron) is widely used in important components such as crankshafts for automotive engines and internal combustion engines, as well as storage and transportation containers for spent fuel in nuclear power plants, due to its good comprehensive mechanical properties such as strength, toughness, and wear resistance. The effect of temperature on the fracture behavior of NCI was investigated using compact tensile (CT) specimens at different temperatures. The results showed that the conditional fracture toughness parameter (K_Q) of the NCI specimens firstly increased and then decreased with decreasing temperature. The crack tip opening displacement δ_m shows a significant ductile–brittle transition behavior with the decreasing of temperature. δ_m remains constant in the upper plateau region but sharply decreases in the ductile–brittle region (−60 °C to −100 °C) and stabilizes at a smaller value in the lower plateau region. Multiscale fractographic analysis indicated that the fracture mechanism changed from ductile fracture (above −60 °C) to ductile–brittle mixed (−60 °C to −100 °C) and then to completely brittle fracture (below −100 °C). As the temperature decreased, the fracture characteristics changed from ductile dimples to dimple and cleavage mixed and then to brittle cleavage. Full article

The internal combustion process in a boiler in power plants has a direct impact on boiler efficiency and NOx generation. The objective of this study is to propose an intelligent deep learning modeling and multi-objective optimization approach that considers NOx emission concentration and boiler thermal efficiency simultaneously for boiler combustion in power plants. Firstly, a hybrid deep learning model, namely, convolutional neural network–bidirectional gated recurrent unit (CNN-BiGRU), is employed to predict the concentration of NOx emissions and the boiler thermal efficiency. Then, based on the hybrid deep prediction model, variables such as primary and secondary airflow rates are considered as controllable variables. A single-objective optimization model based on an improved flow direction algorithm (IFDA) and a multi-objective optimization model based on NSGA-II are developed. For multi-objective optimization using NSGA-II, the average NOx emission concentration is reduced by 5.01%, and the average thermal efficiency is increased by 0.32%. The objective functions are to minimize the boiler thermal efficiency and the concentration of NOx emissions. Comparative analysis of the experiments shows that the NSGA-II algorithm can provide a Pareto optimal front based on the requirements, resulting in better results than single-objective optimization. The effectiveness of the NSGA-II algorithm is demonstrated, and the obtained results provide reference values for the low-carbon and environmentally friendly operation of coal-fired boilers in power plants. Full article

Gas turbine (GT) modeling and optimization have been widely studied at the design level but still lacks focus on real-world operational cases. The concept of a digital twin (DT) allows for the interaction between operation data and the system dynamic performance. Among many DT studies, only a few focus on GT for thermal power plants. This study proposes a digital twin prototype framework including the following modules: process modeling, parameter estimation, and performance optimization. Provided with real-world power plant operational data, key performance parameters such as turbine inlet temperature (TIT) and specific fuel consumption (SFC) were initially unavailable, therefore necessitating further calculation using thermodynamic analysis. These parameters are then used as a target label for developing artificial neural networks (ANNs). Three ANN models with different structures are developed to predict TIT, SFC, and turbine power output (GTPO), achieving high R² scores of 94.03%, 82.27%, and 97.59%, respectively. Physics-informed neural networks (PINNs) are then employed to estimate the values of the air–fuel ratio and combustion efficiency for each time index. The PINN-based estimation resulted in estimated values that align with the literature. Subsequently, an unconventional method of detecting alarms by using conformal prediction were also proposed, resulting in a significantly reduced number of alarms. The developed ANNs are then combined with particle swarm optimization (PSO) to carry out performance optimization in real time. GTPO and SFC are selected as the primary metrics for the optimization, with controllable parameters such as AFR and a fine-tuned inlet guide vane position. The results demonstrated that GTPO could be optimized with the application of conformal prediction when the true GTPO is detected to be higher than the upper range of GTPO obtained from the ANN model with a conformal prediction of a 95% confidence level. Multiple PSO variants were also compared and benchmarked to ensure an enhanced performance. The proposed PSO in this study has a lower mean loss compared to GEP. Furthermore, PSO has a lower computational cost compared to RS for hyperparameter tuning, as shown in this study. Ultimately, the proposed methods aim to enhance GT operations via a data-driven digital twin concept combination of deep learning and optimization algorithms. Full article

Heavy industry is a significant contributor to CO₂ global emissions, accounting for approximately 25% of the total. In Europe, the continent’s largest emitting industries, including steel, cement, and power generation, face significant decarbonization challenges due to multiple interrelated factors. Heavy industry must achieve carbon neutrality by 2050, as outlined in the 13th United Nations Sustainable Goals. One strategy to achieve this goal involves Carbon Capture Utilization and Storage (CCUS) with post-combustion carbon capture (PCC) technologies playing a critical role. Key methods include absorption, which uses chemical solvents like amines; adsorption, employing solid sorbents; cyclic CO₂ capture, such as calcium looping methods; cryogenic separation, which involves chilling flue gas to liquefy CO₂; and membrane separation, leveraging polymeric materials. Each technology offers unique advantages and challenges, necessitating hybrid approaches and policy support for widespread adoption. In this sense, this review provides a comprehensive overview of the existing European pilot and demonstration units and projects, funded by the EU across several industries. It specifically focuses on PCC. This study examines 111 industrial facilities across Europe, documenting the PCC technologies deployed at plants of varying capacities, geographic locations, and operational stakeholders. The review further evaluates the techno-economic performance of these systems, assessing their potential to advance carbon neutrality in heavy industries. Full article

Circulating fluidized bed combustion (CFBC) ash, a byproduct typically generated from coal-fired CFBC power plant boilers, contains high content of free lime and anhydrite. Due to its chemical composition, CFBC ash exhibits self-cementing properties; however, its performance is limited. One approach to enhancing the self-cementing properties of CFBC ash is through the incorporation of mineral admixtures such as gypsum. This study investigated the influence of desulfurization gypsum (DG) on the self-cementing behavior of CFBC ash. To this end, paste and mortar specimens were prepared and evaluated for their hydration and mechanical characteristics. The hydration behavior was analyzed using isothermal calorimetry, thermogravimetric analysis (TGA), setting time measurements, and X-ray diffraction (XRD) analysis. Mechanical properties were assessed by measuring the compressive strength at various curing ages. Additionally, changes in microstructure were examined by evaluating the pore size distribution through mercury intrusion porosimetry (MIP). The experimental results indicate that the appropriate incorporation of DG enhances the hydraulic reactivity of CFBC ash and significantly improves the compressive strength. Full article

Improving the reliability and durability of internal combustion engines in marine vessels is a complex issue. The vibrations generated in these engines significantly affect their proper operation. One of the current research challenges is identifying effective methods to reduce, among other things, torsional vibrations generated within the crank–piston system. To mitigate these vibrations, viscous dampers are commonly used. The selection of a viscous damper for a high-power multi-cylinder engine, such as those in marine power plants, requires a thorough understanding of the thermo-hydrodynamic properties of oil films formed in the spaces between the damper housing and the inertial mass. The description of the phenomena involved is complicated by the variable positioning of the inertial mass center relative to the housing during operation. Most previous studies assume a concentric alignment between these components. The main novelty of this work lies in highlighting the combined effect of the eccentric motion of the inertial ring on both hydrodynamic resistance and thermal characteristics, which has not been fully addressed in existing studies. This article defines the oil flow resistance coefficients and develops static characteristics of the dampers. Additionally, it evaluates the impact of the size of the frontal and cylindrical surfaces of the damper on its heat dissipation capacity. The presented characteristics can be utilized to assess the performance parameters of this type of damper. Full article

This study presents an advanced NO_x reduction strategy for a 330 MW lignite-fired boiler using an immunological AI system: the SILO (Stochastic Immune Layer Optimizer) combustion optimizer inspired by artificial immune systems. The immunological AI optimizer adaptively models multi-variable interactions and fireball shape in real time, optimizing fuel–air mixing to reduce NO_x formation at the source. Unlike reactive secondary methods, the combustion optimizer reshapes the combustion process to reduce emissions while improving efficiency. Real-time temperature data from the AGAM acoustic system inform the combustion optimizer’s fireball modeling, ensuring combustion uniformity. A urea-based SNCR system serves as a secondary layer, controlled based on local furnace conditions to target thermal zones. Field results confirmed that SILO reduced NO_x emissions below 200 mg/Nm³, decreased urea consumption by up to 34%, and improved boiler efficiency by 0.29%. The architecture offers a scalable, DCS-integrated solution for aligning fossil-fueled operations with tightening emission standards. Full article

Currently, ecological energy production is one of the most pressing issues in power engineering. In addition, environmental pollution caused by various emissions and the challenge of waste disposal remain significant global concerns. One potential solution to these problems is the conversion of waste into useful energy through combustion. In this study, experimental investigations were carried out on the combustion of municipal solid waste (MSW) in a grate furnace of a 400 kW hot water boiler. The experiments included the combustion of both MSW and traditional brown coal. Data were collected on the concentrations of various substances in the exhaust gases, and thermal imaging was performed to assess heat losses from the boiler surface. When burning waste compared to coal, SO₂ concentrations were significantly lower, ranging from 3.43 to 4.3 ppm, whereas for coal they reached up to 122 ppm. NO_X concentrations during MSW combustion peaked at 106 ppm, while for coal combustion they reached 67.5 ppm. A notable increase in CO concentration was observed during the initial phase of coal combustion, with levels reaching up to 2510 ppm. The thermal efficiency of the boiler plant reached 84.4% when burning waste and 87% when burning brown coal. Full article

The combustion of hard coal and lignite in power and combined heat and power plants generates significant amounts of coal fly ash (CFA), a waste material with variable properties. CFA naturally contains radionuclides, specifically naturally occurring radioactive materials (NORMs), which pose potential radiological risks to the environment and human health during their storage and utilization, including their incorporation into building materials. Although global research on the radionuclide content in CFA is available, there is a clear gap in detailed and current data specific to Central and Eastern Europe and notably, a lack of a systematic analysis investigating the influence of installed power plant capacity on the concentration profile of these radionuclides in the generated ash. This study aimed to fill this gap and provide crucial data for the Polish energy and environmental context. The objective was to evaluate the concentrations of selected radionuclides (²³²Th, ²²⁶Ra, and ⁴⁰K) in coal fly ash samples collected between 2020 and 2023 from 19 Polish power and combined heat and power plants with varying capacities (categorized into four groups: S1–S4) and to assess the associated radiological risk. Radionuclide concentrations were determined using gamma spectrometry, and differences between groups were analyzed using non-parametric statistical methods, including PERMANOVA. The results demonstrated that plant capacity has a statistically significant influence on the concentration profiles of thorium and potassium but not radium. Calculated radiological hazard assessment factors (Ra_eq, H_ex, H_in, IAED) revealed that although most samples fall near regulatory limits (e.g., 370 Bq kg⁻¹ for Ra_eq), some exceed these limits, particularly in groups S1 (plants with a capacity less than 300 MW) and S4 (plants with a capacity higher than 300 MW). It was also found that the frequency of exceeding the annual effective dose limits (IAEDs) showed an increasing trend with the increasing installed capacity of the facility. These findings underscore the importance of plant capacity as a key factor to consider in the radiological risk assessment associated with coal fly ash. This study’s outcomes are crucial for informing environmental risk management strategies, guiding safe waste processing practices, and shaping environmental policies within the energy sector in Central and Eastern European countries, including Poland. Full article

Ammonium nitrate (NH₄NO₃) is a major constituent of fine particulate matter (PM_2.5), playing a critical role in air quality and atmospheric chemistry. However, the dual regulatory role of ammonia (NH₃) in both the formation and volatilization of NH₄NO₃ under ambient atmospheric conditions remains inadequately understood. To address this gap, we conducted high-resolution field measurements at a clean tropical coastal site in China using an integrated system of Aerosol Ion Monitor-Ion Chromatography, a Scanning Mobility Particle Sizer, and online OC/EC analyzers. These observations were complemented by thermodynamic modeling (E-AIM) and source apportionment via a Positive Matrix Factorization (PMF) model. The E-AIM simulations revealed persistent thermodynamic disequilibrium, with particulate NO₃⁻ tending to volatilize even under NH_3gas-rich conditions during the northeast monsoon. This suggests that NH₄NO₃ in PM_2.5 forms rapidly within fresh combustion plumes and/or those modified by non-precipitation clouds and then undergoes substantial evaporation as it disperses through the atmosphere. Under the southeast monsoon conditions, reactions constrained by sea salt aerosols became dominant, promoting the formation of particulate NO₃⁻ while suppressing NH₄NO₃ formation despite ongoing plume influence. In scenarios of regional accumulation, elevated NH₃ concentrations suppressed NH₄NO₃ volatilization, thereby enhancing the stability of particulate NO₃⁻ in PM_2.5. PMF analysis identified five source factors, with NO₃⁻ in PM_2.5 primarily associated with emissions from local power plants and the large-scale regional background, showing marked seasonal variability. These findings highlight the complex and dynamic interplay between the formation and evaporation of NH₄NO₃ in NH_3gas-rich coastal atmospheres. Full article

The estimation of available active power in coal-fired thermal power plant units involves considerable complexity and remains a critical task for plant operators. To avoid compromising system stability, operators often operate the thermal unit below its full capacity. To address this issue, the aim of this paper is to facilitate the process of estimating the maximum active electrical power by applying an assistive system based on ANFIS (Adaptive Neuro-Fuzzy Inference System), a method that combines the strengths of neural networks and fuzzy logic. Since the generated electric energy is directly linked to the amount of thermal energy produced, the analysis is focused on the boiler combustion process. It has been shown that the key factors in this process are the coal mills and their achievable capacity, as well as the calorific value of coal. Therefore, the proposed assistive system is based on the estimation of the available capacity of each active mill, which is then combined with the estimated calorific value of the coal to determine the achievable active electrical power of the unit. The conducted analysis and experiments confirm the validity of this approach. Full article