Search Results (63)

Exhaust Gas Temperature (EGT) is a critical parameter in Gas Turbines (GTs) in terms of performance monitoring, fault detection, and operational optimization. In this study, a comprehensive and data-driven modeling approach was developed to predict EGT under variable load conditions and different Inlet Guide Vane (IGV) positions in a 401 MW GT unit located in a Combined Cycle Power Plant (CCPP) with a single-shaft design. A large-scale dataset obtained from a total of 18,334 h of real operating conditions was used in the study. Operational parameters such as Gas Turbine Power Output (GTPO), IGV, Compressor Inlet Temperature (CIT), Fuel Gas Flow (FGF), and Lower Heating Value (LHV), together with environmental parameters such as Atmospheric Pressure (AP) and Relative Humidity (RH), were evaluated simultaneously, and the combined effect of these variables on EGT was investigated. In order to model the nonlinear relationships between EGT and the input variables, six different tree-based ensemble learning methods, namely Bagged Trees, Random Forest, Gradient Boosting, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost), were applied and compared. The results showed that all models were able to predict EGT with high accuracy. The most successful model was LightGBM, which achieved the best overall prediction performance with a Coefficient of Determination (R²) of 0.9703 and a Root Mean Square Error (RMSE) of 1.5280. The analyses revealed that the most influential parameters affecting EGT were GTPO, CIT, FGF, and IGV, whereas the environmental variables had secondary but still significant effects. The proposed approach provides a reliable and computationally efficient tool for sensor validation, fault detection, and predictive maintenance applications. Full article

For complex tasks such as search and recovery in uncharted maritime areas, the use of heterogeneous unmanned swarms (UAVs and USVs) is highly promising, yet effective cross-domain cooperative trajectory planning remains a key challenge, often leading to mission delays. This paper proposes a rapid Cooperative Cross-domain Path Planning framework (CCPP) and its associated algorithm for heterogeneous UAV–USV swarms. The framework first establishes a visual-fusion modeling pipeline, converting visual pose estimation, uncertainties, and semantic dynamic obstacles into a planning representation with robust safety margins and time-varying risk fields. A hybrid velocity-path co-optimization algorithm is then designed to simultaneously generate curvature-feasible trajectories and speed profiles under heterogeneous kinematics and explicit temporal constraints. In the end, an adaptive interpretable decision tree acts as a meta-strategy for online replanning and real-time adjustment of modes and weights. To address the critical issue of uneven arrival time distribution, this paper introduces, inspired by economic inequality analysis, a normalized Gini coefficient-based arrival time consistency index to quantify and optimize coordination timing. Comprehensive experiments validate the effectiveness of the proposed approach in enhancing cooperative efficiency and real-time adaptability. Full article

Respiratory diseases induced by Mycoplasma species, mainly Mycoplasma capricolum subsp. capripneumoniae (Mccp) and Mycoplasma ovipneumonia (Mo), pose a major threat to goat/sheep farming. This study investigated the biological characteristics and antimicrobial susceptibility of Mccp isolates that suddenly spread extensively in China in the first half of 2024. A total of 34 Mccp isolates were obtained from goats with suspected contagious caprine pleuropneumonia (CCPP) across multiple provinces during 2024–2025. All isolates were purified and confirmed via a PCR targeting the arcD gene and exhibited characteristic “fried egg” colony morphology. Phylogenetic analysis based on the arcD gene and multi-locus sequence analysis (MLSA) of eight genetic loci revealed that the circulating strains shared high homology and belonged to Group 1 within Lineage 1, which showed a close genetic relationship with isolates from Qatar and the United Arab Emirates, while differing from previously reported strains in China. Antimicrobial susceptibility testing against nine antimicrobial drugs indicated that the Mccp isolates generally exhibited low resistance levels. However, some strains showed reduced susceptibility to florfenicol and lincomycin. These findings highlight the emergence of a genetically distinct Mccp lineage in China and underscore the importance of ongoing surveillance, strain characterization, and prudent antimicrobial use in CCPP control. Full article

To address the excessive headland space occupation and pronounced vehicle body roll caused by traditional U-shaped turning paths during plant protection sprayer operations in soybean–maize intercropping systems, particularly in fragmented and irregular plots, this study proposes a two-way operation scheme for unmanned sprayers. An improved Reeds–Shepp (RS) curve-based hybrid coverage path planning (CCPP) method is developed to optimize headland turning in non-perpendicular boundary scenarios and generate full-coverage paths for irregular fields. Simulation and field experiments conducted on four plots with an average area of 0.42 had demonstrated that, compared with the conventional U-shaped path, the proposed method reduces the average reserved headland width by 35.21% and shortens the non-operational path length by 21.76%. Under the same path-tracking controller, the turning heading deviation and roll amplitude are reduced by 21.38% and 31.73%, respectively. The results indicate that the improved RS-based path planning method can effectively reduce headland space occupation and enhance the stability and operational efficiency of plant protection sprayers. Full article

Long-term excessive fat intake can easily induce metabolic diseases such as fatty liver and hyperlipidemia. As a natural active ingredient, polysaccharides exhibit notable lipid-lowering effects and can serve as effective lipid regulators. Nevertheless, the lipid-lowering effect of Arabica coffee cherry peel polysaccharides (CCPPs) and the underlying regulatory mechanism remain poorly understood. This study isolated polysaccharides from coffee cherry peel, and their functional properties and the lipid-lowering effects and mechanisms on hyperlipidemic mice. In high-fat diet-fed (HFD-fed) mice, CCPP administration had significant regulatory effects on various metabolic parameters. In laboratory mice where hyperlipidemia is induced by a high-fat diet, CCPP administration improved serum lipid levels and demonstrated anti-inflammatory and antioxidant effects. These benefits were achieved by reducing pro-inflammatory cytokine expression, enhancing antioxidant enzyme activity, and lowering overall oxidative stress. Additionally, it effectively decreased fat area in liver tissues and adipocytes. Specifically, compared with the control group, after high-dose CCPP intervention, the adipocyte area of mice on a high-fat diet was significantly reduced by 41.3%. Notably, CCPP intervention resulted in a shift in the gut microbiota composition. At the phylum level, the model group showed a significant increase in Bacillota and a concomitant reduction in Bacteroidetes in comparison with the control group. Compared with the model group, CCPP intervention, especially in the CCPP-H group, resulted in an increase in the proportion of Bacteroidetes and a decrease in Bacillota. At the genus level, CCPP modulated the abundances of key bacterial genera; for instance, the relative abundance of Lachnospiraceae_NK4A136_group increased from 2.64% in the model group to 11.9% in CCPP-H group, while Faecalibaculum decreased from 62.69% to 41.27% in CCPP-L group and 25.29% in CCPP-H group. These shifts suggest that CCPP has a reparative effect on the gut microbial composition, potentially contributing to the promotion of gut health. Taken together, these factors highlight the promise of CCPP as a functional food ingredient for dietary interventions to ameliorate obesity and hyperlipidemia. Full article

Driven by the global energy transition and carbon-neutrality goals, virtual power plants (VPPs) are expected to aggregate distributed energy resources and participate in multiple electricity markets while achieving economic efficiency and low carbon emissions. However, the strong volatility of wind and photovoltaic generation, together with the coupling between electric and thermal loads, makes real-time VPP scheduling challenging. Existing deep reinforcement learning (DRL)-based methods still suffer from limited predictive awareness and insufficient handling of physical and carbon-related constraints. To address these issues, this paper proposes an improved model, termed SAC-LAx, based on the Soft Actor–Critic (SAC) deep reinforcement learning algorithm for intelligent VPP scheduling. The model integrates an Attention–xLSTM prediction module and a Linear Programming (LP) constraint module: the former performs multi-step forecasting of loads and renewable generation to construct an extended state representation, while the latter projects raw DRL actions onto a feasible set that satisfies device operating limits, energy balance, and carbon trading constraints. These two modules work together with the SAC algorithm to form a closed perception–prediction–decision–control loop. A campus integrated-energy virtual power plant is adopted as the case study. The system consists of a gas–steam combined-cycle power plant (CCPP), battery storage, a heat pump, a thermal storage unit, wind turbines, photovoltaic arrays, and a carbon trading mechanism. Comparative simulation results show that, at the forecasting level, the Attention–xLSTM (Ax) module reduces the day-ahead electric load Mean Absolute Percentage Error (${\mathrm{M}}_{\mathrm{A}\mathrm{P}\mathrm{E}}$) from 4.51% and 5.77% obtained by classical Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to 2.88%, significantly improving prediction accuracy. At the scheduling level, the SAC-LAx model achieves an average reward of approximately 1440 and converges within around 2500 training episodes, outperforming other DRL algorithms such as Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO). Under the SAC-LAx framework, the daily net operating cost of the VPP is markedly reduced. With the carbon trading mechanism, the total carbon emission cost decreases by about 49% compared with the no-trading scenario, while electric–thermal power balance is maintained. These results indicate that integrating prediction enhancement and LP-based safety constraints with deep reinforcement learning provides a feasible pathway for low-carbon intelligent scheduling of VPPs. Full article

With the goal of achieving carbon neutrality, promoting the clean and low-carbon transformation of energy assets, as exemplified by existing thermal power units, has emerged as a pivotal challenge in addressing climate change and achieving sustainable development. Arrangements and technologies such as the electricity–carbon–certificate multi-market, microgrids with direct green power connections, and carbon capture and storage (CCS) retrofitting provide favorable conditions for facing the aforementioned challenge. Based on an analysis of how liquid-storage CCS retrofitting affects the flexibility of thermal power units, this manuscript proposes a bi-level optimization model and solution method for capacity allocation for grid-connected microgrids, while considering CCS retrofits under multi-markets. This approach overcomes two key deficiencies in the existing research: first, neglecting the relationship between electricity–carbon coupling characteristics and unit flexibility and its potential impacts, and second, the significant deviation of scenarios constructed from real policy and market environments, which limits its ability to provide timely and relevant references. A case study in southern China demonstrates that first, multi-market implementation significantly boosts microgrids’ investment in and absolute consumption of renewable energy. However, its effect on reducing carbon emissions is limited, and renewable power curtailment may surge, potentially deviating from the original intent of carbon neutrality policies. In this case study, renewable energy installed capacity and consumption rose by 17.09% and 22.64%, respectively, while net carbon emissions decreased by only 3.32%, and curtailed power nearly doubled. Second, introducing liquid-storage CCS, which decouples the CO₂ absorption and desorption processes, into the capacity allocation significantly enhances microgrid flexibility, markedly reduces the risk of overcapacity in renewable energy units, and enhances investment efficiency. In this case study, following CCS retrofits, renewable energy unit installed capacity decreased by 24%, while consumption dropped by only 7.28%, utilization hours increased by 22%, and the curtailment declined by 78.05%. Third, although CCS retrofitting can significantly reduce microgrid carbon emissions, factors such as current carbon prices, technological efficiency, and economic characteristics hinder large-scale adoption. In this case study, under multi-markets, CCS retrofitting reduced net carbon emissions by 86.16%, but the annualized total cost rose by 3.68%. Finally, based on the aforementioned findings, this manuscript discusses implications for microgrid development decision making, CCS industrialization, and market mechanisms from the perspectives of research directions, policy formulation, and practical work. Full article

Combined Cycle Power Plants (CCPP) suffer from drops in power and efficiency due to summer time ambient conditions. This power reduction is especially important in regions with extreme summer ambient conditions. Given the substantial investment and labor involved in the establishment and operation of these power plants, mitigating power loss using various methods emerges as a promising solution. In this context, the integration of Concentrated Solar Power (CSP) technologies has been proposed in this research not primarily to improve the overall performance efficiency of power plants as other recent studies entail, but to ensure continuous power generation throughout summer days, improving stability. This research aims to address this issue by conducting an extensive study covering the different scenarios in which Concentrated Solar Power (CSP) can be integrated into the power plant. Multiple scenarios for integration were defined including CSP integration in the Heat Recovery Steam Generator, CSP-powered chiller for Gas Turbine Compressor Cooling and Gas Turbine Combustion Chamber Preheating using CSP, and scenarios with inlet air fog cooling and hybrid scenarios were studied. This systematic analysis resulted in the selection of the scenario where the CSP is integrated into the combined cycle power plant in the HRSG section as the best case. The selected scenario was benchmarked against its equivalent model operating in Seville’s ambient conditions. By comparing the final selected model, both Yazd and Seville experience a noticeable boost in power and efficiency while reaching the maximum integration capacity at different reflector lengths (800 m for Seville and 900 m for Yazd). However, both cities reach their minimum fuel consumption at an approximate 300 m total reflector length. Full article

This study develops an innovative framework that utilizes real-time operational data to forecast electrical power output (EPO) in Combined Cycle Power Plants (CCPPs) by employing a temperature segmentation-based modeling approach. Instead of using a single general prediction model, which is commonly seen in the literature, three separate prediction models were created to explicitly capture the nonlinear effect of ambient temperature (AT) on efficiency (AT < 12 °C, 12 °C ≤ AT < 20 °C, AT ≥ 20 °C). Linear Ridge, Medium Tree, Rational Quadratic Gaussian Process Regression (GPR), Support Vector Machine (SVM) Kernel, and Neural Network methods were applied. In the modeling, the variables considered were AT, relative humidity (RH), atmospheric pressure (AP), and condenser vacuum (V). The highest performance was achieved with the Rational Quadratic GPR method. In this approach, the weighted average Mean Absolute Error (MAE) was found to be 2.225 with seasonal segmentation, while it was calculated as 2.417 in the non-segmented model. By applying seasonal prediction models, the hourly EPO prediction error was reduced by 192 kW, achieving a 99.77% average convergence of the predicted power output values to the actual values. This demonstrates the contribution of the proposed approach to enhancing operational efficiency. Full article

Combined cycle power plants are modern power generation systems that provide an efficient and environmentally friendly way of generating electricity. With the development of smart grids, higher requirements have been put forward for their power prediction. Using a dataset comprising 9568 observations from a combined cycle power plant operating at full load for 6 years, a high-precision power prediction model integrating CatBoost and domain knowledge is proposed. Twenty new features were designed based on domain expertise, and Recursive Feature Elimination was applied to select the most informative features, optimizing model performance. Experimental results demonstrate that CatBoost outperformed six commonly used machine learning algorithms, both with and without domain knowledge integration. And the incorporation of domain knowledge improved the predictive performance of all evaluated models, underscoring the effectiveness and general applicability of the proposed features. Moreover, Recursive Feature Elimination was applied to select 11 features. The optimized CatBoost model achieved the best predictive accuracy with a root mean square error of 2.8545, a mean absolute error of 1.9645, and an R-squared of 0.9702. A comparative analysis with existing literature methods further validated the superior performance of the proposed approach. These findings highlight the effectiveness of integrating domain knowledge with machine learning and its potential for improving power output prediction in combined cycle power plants. Full article

Multi-energy microgrids (MEMs) can achieve efficient and low-carbon energy utilization by relying on the coordination, complementarity, and coupling conversion of different energy sources, which is of great significance for new energy consumption and energy cascade utilization. In this paper, a low-carbon economic dispatch model of a multi-energy microgrid that uses a joint carbon capture–CHP-P2G operation is proposed. Firstly, the basic structure of the power–electrolysis–methanol energy (PEME) is established. Secondly, a flexible mechanism for the joint operation of CCPPs and CHP is analyzed, and a flexible joint operation model for carbon capture–CHP-P2G is proposed. Finally, considering the system’s low-carbon operation and economy, a low-carbon economic dispatch model for a multi-energy microgrid in a park is established, with the goal of minimizing the total operating cost of PEME in the park. The results illustrate that the introduction of a liquid storage tank reduces the total cost and carbon emissions of the MEM by 4.04% and 8.49%, respectively. The application of an electric boiler and ORC effectively alleviates the problem of peak–valley differences in the electric heating load. Our joint operation model realizes the dual optimization of the MEM’s flexibility and low-carbon requirement through the collaboration of multiple pieces of technology. Full article

To achieve shorter path length and lower repetition rate for robotic complete coverage path planning, a complete-coverage path-planning algorithm based on transition probability and learning perturbation operator (CCPP-TPLP) is proposed. Firstly, according to the adjacency information between nodes, the distance matrix and transition probability matrix of the accessible grid are established, and the optimal initialization path is generated by applying greedy strategy on the transition probability matrix. Secondly, the population is divided into four subgroups, and different degrees of learning perturbation operations are carried out on subgroups to update each path in the population. CCPP-TPLP was tested against five algorithms in different map environments and in the working map environment of electric tractors with height information The results show that CCPP-TPLP can optimize the selection of path nodes, reduce the total length and repetition rate of the path, and significantly improve the planning efficiency and quality of complete coverage path planning. Full article

The critical consequence of climate change resulting from global warming is the increase in temperature. In combined cycle power plants (CCPPs), the Electric Power Output (PE) is affected by changes in both Ambient Temperature (AT) and Sea Surface Temperature (SST), particularly in plants utilizing seawater cooling systems. As AT increases, air density decreases, leading to a reduction in the mass of air absorbed by the gas turbine. This change alters the fuel–air mixture in the combustion chamber, resulting in decreased turbine power. Similarly, as SST increases, cooling efficiency declines, causing a loss of vacuum in the condenser. A lower vacuum reduces the steam expansion ratio, thereby decreasing the Steam Turbine Power Output. In this study, the effects of increases in these two parameters (AT and SST) due to global warming on the PE of CCPPs are investigated using various regression analysis techniques, Artificial Neural Networks (ANNs) and a hybrid model. The target variables are condenser vacuum (V), Steam Turbine Power Output (ST Power Output), and PE. The relationship of V with three input variables—SST, AT, and ST Power Output—was examined. ST Power Output was analyzed with four input variables: V, SST, AT, and relative humidity (RH). PE was analyzed with five input variables: V, SST, AT, RH, and atmospheric pressure (AP) using regression methods on an hourly basis. These models were compared based on the Coefficient of Determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The best results for V, ST Power Output, and PE were obtained using the hybrid (LightGBM + DNN) model, with MAE values of 0.00051, 1.0490, and 2.1942, respectively. As a result, a 1 °C increase in AT leads to a decrease of 4.04681 MWh in the total electricity production of the plant. Furthermore, it was determined that a 1 °C increase in SST leads to a vacuum loss of up to 0.001836 bar_a. Due to this vacuum loss, the steam turbine experiences a power loss of 0.6426 MWh. Considering other associated losses (such as generator efficiency loss due to cooling), the decreases in ST Power Output and PE are calculated as 0.7269 MWh and 0.7642 MWh, respectively. Consequently, the combined effect of a 1 °C increase in both AT and SST results in a 4.8110 MWh production loss in the CCPP. As a result of a 1 °C increase in both AT and SST due to global warming, if the lost energy is to be compensated by an average-efficiency natural gas power plant, an imported coal power plant, or a lignite power plant, then an additional 610 tCO₂e, 11,184 tCO₂e, and 19,913 tCO₂e of greenhouse gases, respectively, would be released into the atmosphere. Full article

Sheep and goats are an important source of livelihood for smallholder farmers in sub-Saharan Africa (SSA). These livestock are almost entirely managed by resource-poor, smallholder farmers and pastoralists. Despite the large number of sheep and goats in SSA, their productivity is low, mainly due to diseases, poor feed, and inferior breeds. This review aims to summarize the most important diseases in small ruminants in SSA, with a focus on current treatment and control strategies. The following diseases were identified as the most significant constraints for small ruminant farmers: helminthoses, including gastrointestinal nematode infestation, lungworm infestation, fasciolosis, and cerebral coenurosis; viral diseases, such as peste des petits ruminants (PPR), sheep and goat pox, and contagious ecthyma (orf); bacterial diseases, including contagious caprine pleuropneumonia (CCPP), pneumonic pasteurellosis, and anthrax; as well as ectoparasite infestations. The diseases have significant economic implications due to mortality and production losses. Depending on the disease, they may also impact trade and export and hinder the introduction of new, more productive breeds. The ability to control diseases more efficiently is often limited due to financial constraints. In the case of infection with internal parasites, a lack of knowledge about the epidemiology of the disease, as well as the availability of appropriate anthelmintics and the development of resistance against commonly used anthelmintics, are often barriers. The control of viral diseases depends on the accessibility, quality, and handling of vaccines, whereas in bacterial diseases, increasing antibiotic resistance and inappropriate antimicrobial treatments pose challenges, as well as the availability of appropriate vaccines and their use. In the case of ectoparasitic infections, a strategic, regular, and appropriate antiparasitic treatment approach is often not achieved. Full article

The rapid growth in the use of wind energy has led to significant challenges in the inspection and maintenance of wind turbine blades, especially as turbine sizes increase dramatically and as operational environments become harsh and unpredictable. Wind turbine blades, being the most expensive and failure-prone components, directly affect operational stability and energy efficiency. The efficient and precise inspection of these blades is therefore essential to ensuring the sustainability and reliability of wind energy production. To overcome the limitations of the existing inspection methods, which suffer from low detection precision and inefficiency, this paper proposes a novel complete coverage path planning (CCPP) algorithm for wall-climbing robots operating on wind turbine blades. The proposed algorithm specifically targets highly complex regions with significant curvature variations, utilizing 3D point cloud data to extract height information for the construction of a 2.5D grid map. By developing a tailored energy consumption model based on diverse robot motion modes, the algorithm is integrated with a bio-inspired neural network (BINN) to ensure optimal energy efficiency. Through extensive simulations, we demonstrate that our approach outperforms the traditional BINN algorithms, achieving significantly superior efficiency and reduced energy consumption. Finally, experiments conducted on both a robot prototype and a wind turbine blade platform validate the algorithm’s practicality and effectiveness, showcasing its potential for real-world applications in large-scale wind turbine inspection. Full article