Search Results (526)

This study presents a comprehensive analysis of the chemical composition of bottom ash samples generated during municipal waste incineration. A total of 52 samples were collected and subjected to statistical analysis for 17 elements and 2 element sums using techniques such as correlation analysis and one-way ANOVA. The results confirm a high degree of heterogeneity in the elemental content, reflecting the variability of waste streams and combustion processes. Strong correlations were identified between certain elements, including Cu-Zn, Co-Ni, and HREE-LREE, indicating common sources and similar geochemical properties. The analysis also revealed significant seasonal variability in the content of Ba and Sr, with lower average values observed during the spring season and greater variability noted during summer and winter. Although Al and HREE did not reach classical significance levels, their distributions suggest possible seasonal differentiation. These findings underscore the need for long-term monitoring and seasonal analysis of incineration bottom ash composition to optimize resource recovery processes and assess environmental risk. The integration of chemical data with operational data on waste composition and combustion parameters may contribute to a better understanding of the variability of individual elements, ultimately supporting the development of effective strategies for ash management and element recovery. Full article

With the rapid development of renewable energy, the proportion of small hydropower as a clean energy in the distribution network (DN) is increasing. However, the randomness and intermittence of small hydropower has brought new challenges to the operation of DN; especially, the problems of increasing network loss and reactive voltage exceeding the limit have become increasingly prominent. Aiming at the above problems, this paper proposes a reactive power optimization control method for DN with hydropower based on an improved discrete particle swarm optimization (PSO) algorithm. Firstly, this paper analyzes the specific characteristics of small hydropower and establishes its mathematical model. Secondly, considering the constraints of bus voltage and generator RP output, an extended minimum objective function for system power loss is established, with bus voltage violation serving as the penalty function. Then, in order to solve the following problems: that the traditional discrete PSO algorithm is easy to fall into local optimization and slow convergence, this paper proposes an improved discrete PSO algorithm, which improves the global search ability and convergence speed by introducing adaptive inertia weight. Finally, based on the IEEE-33 buses distribution system as an example, the simulation analysis shows that compared with GA optimization, the line loss can be reduced by 3.4% in the wet season and 13.6% in the dry season. Therefore, the proposed method can effectively reduce the network loss and improve the voltage quality, which verifies the effectiveness and superiority of the proposed method. Full article

This paper presents an annual, real-world evaluation of the performance and economics of a residential photovoltaic (PV) system coupled with a battery energy storage system (BESS) in southern Poland. The system, monitored with 5 min resolution, operated under time-of-use (TOU) electricity tariffs. Seasonal variation was significant; self-sufficiency exceeded 90% in summer, while winter conditions increased grid dependency. The hybrid system reduced electricity costs by over EUR 1400 annually, with battery operation optimized for high-tariff periods. Comparative analysis of three configurations—grid-only, PV-only, and PV + BESS—demonstrated the economic advantage of the integrated solution, with the shortest payback period (9.0 years) achieved with financial support. However, grid voltage instability during high PV production led to inverter shutdowns, highlighting limitations in the infrastructure. This study emphasizes the importance of tariff strategies, environmental conditions, and voltage control when designing residential PV-BESS systems. Full article

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems. Full article

The division of the flood season is of great significance for the precise operation of water conservancy projects, flood control and disaster reduction, and the rational allocation of water resources, alleviating the contradiction of the uneven spatial and temporal distribution of water resources. The single weighting method can only determine the weight of the flood season division indicators from a certain perspective and cannot comprehensively reflect the time-series attributes of the indicators. This study proposes a Flood Season Division Model based on the Goose Optimization Algorithm and Minimum Deviation Combined Weighting (FSDGOAMDCW). The model uses the Goose Optimization Algorithm (GOA) to solve the Minimum Deviation Combination model, integrating weights from two subjective methods (Expert Scoring and G1) and three objective methods (Entropy Weight, CV, and CRITIC). Combined with the Set Pair Analysis Method (SPAM), it realizes comprehensive flood season division. Based on daily precipitation data of the Nandujiang River (1961–2022), the study determines its flood season from 1 May to 30 October. Comparisons show that: ① GOA converges faster than the Genetic Algorithm, stabilizing at T = 5 and achieving full convergence at T = 24; and ② The model’s division results have the smallest Intra-Class Differences, avoiding indistinguishability between flood and non-flood seasons under special conditions. This research aims to support flood season division studies in tropical islands. Full article

An efficient integrated energy system (IES) can enhance the potential of building energy conservation and carbon mitigation. However, imbalances between user-side demand and supply side output present formidable challenges to the operational dispatch of building energy systems. To mitigate heat rejection and improve dispatch optimization, an integrated building energy system incorporating waste heat recovery via an absorption heat pump based on the flow temperature model is adopted. A comprehensive analysis was conducted to investigate the correlation among heat pump operational strategies, thermal comfort, and the dynamic thermal storage capacity of piping network systems. The optimization calculations and comparative analyses were conducted across five cases on typical season days via the CPLEX solver with MATLAB R2018a. The simulation results indicate that the operational modes of absorption heat pump reduced the costs by 4.4–8.5%, while the absorption rate of waste heat increased from 37.02% to 51.46%. Additionally, the utilization ratio of battery and thermal storage units decreased by up to 69.82% at most after considering the pipeline thermal inertia and thermal comfort, thus increasing the system’s energy-saving ability and reducing the pressure of energy storage equipment, ultimately increasing the scheduling flexibility of the integrated building energy system. Full article

Simulation is an important technical tool in corn threshing operations, and the establishment of the corn kernel model is the core part of the simulation process. The existing modeling method is to treat the whole kernel as a rigid body, which cannot be crushed during the simulation process, and the calculation of the crushing rate needs to be considered through multiple criteria such as the contact force, the number of collisions, and so on. Aiming at the issue that kernel crushing during maize threshing cannot be accurately modeled in discrete element simulations, in this study, a sub-area crushing model was constructed; representative samples with 26%, 30% and 34% moisture content were selected from a double-season maturing region in China; based on the physical dimensions and biological structure of the maize kernel, three stress regions were defined; and mechanical property tests were conducted on each of the three stress regions using a texturometer as a way to determine the different crushing forces due to the heterogeneity of the maize structure. The correctness of the model was verified by stacking angle and mechanical property experiments. A discrete element model of corn kernels was established using the Bonding V2 method and sub-area modeling. Bonding parameters were calculated by combining stacking angle tests and mechanical property tests. The flattened corn kernel was used as a prototype, and the bonding parameters were determined through size and mechanical property tests. A 22-ball bonding model was developed using dimensional parameters, and the kernel density was recalculated. Results showed that the relative error between the stacking angle test and the measured mean value was 0.31%. The maximum deviation of axial compression simulation results from the measured mean value was 22.8 N, and the minimum deviation was 3.67 N. The errors between simulated and actual rupture forces at the three force areas were 5%, 10%, and 0.6%, respectively. The decreasing trend of the maximum rupture force for the three moisture levels in the simulation matched that of the actual rupture force. The discrete element model can accurately reflect the rupture force, energy relationship, and rupture process on both sides, top, and bottom of the grain, and it can solve the error problem caused by the contact between the threshing element and the grain line in the actual threshing process to achieve the design optimization of the threshing drum. The modeling method provided in this study can also be applied to breakable discrete element models for wheat and soybean, and it provides a reference for optimizing the design of subsequent threshing devices. Full article

This research assesses the design efficiency of the future centralized wastewater treatment plant (WWTP) in Prishtina, which also takes into consideration rapidly expanding suburban areas, such as Fushë Kosova, Obiliq, and Graçanica. Using a combination of both ATV-DVWK-A 131E deterministic calculations and dynamic simulation with IWASP, this study focuses on the planned configurations for the future Prishtina wastewater treatment plant (WWTP) to evaluate design efficiency alongside operational feasibility. The primary goal was to determine if meeting projected loads for the year 2040 would be possible with compliance requirements for a single-stage CAS system. Simulation data suggest that reliable nitrogen removal would not be possible with a sole CAS stage (aerobic), particularly considering seasonal and peak load dynamics. Alternatively, an optimized three-reactor CAS model, including one anoxic pre-denitrification zone coupled with two alternating aerobic zones, achieved an average total nitrogen (TN) removal efficiency of about 85%, maintaining effluent TN below 10 mg/L. Additional advantages saw COD being removed at rates between 90 and 92%, along with MLSS levels stabilizing around 3500 mg/L. The flexibly scalable design also provides adaptive operation features, including expanded tertiary nutrient removal in phase II. In scenario two’s site comparative analysis, Lismir’s centralized WWTP emerges as the most economically and technically rational option due to the enhanced reactor layout optimization. These findings confirm that enhanced configurations, validated through both static and dynamic analyses, are essential for long-term treatment efficiency and regulatory compliance. Full article

Apple harvesting faces increasing challenges owing to rising labor costs and the limited seasonal workforce availability, highlighting the need for robotic harvesting solutions in precision agriculture. This study presents a 6-DOF robotic arm system designed for harvesting in dwarf hedge-planted orchards, featuring a lightweight perception module, a task-adaptive motion planner, and an adaptive soft gripper. A lightweight approach was introduced by integrating the Faster module within the C2f module of the You Only Look Once (YOLO) v8n architecture to optimize the real-time apple detection efficiency. For motion planning, a Dynamic Temperature Simplified Transition Adaptive Cost Bidirectional Transition-Based Rapidly Exploring Random Tree (DSA-BiTRRT) algorithm was developed, demonstrating significant improvements in the path planning performance. The adaptive soft gripper was evaluated for its detachment and load-bearing capacities. Field experiments revealed that the direct-pull method at 150 mN·m torque outperformed the rotation-pull method at both 100 mN·m and 150 mN·m. A custom control system integrating all components was validated in partially controlled orchards, where obstacle clearance and thinning were conducted to ensure operation safety. Tests conducted on 80 apples showed a 52.5% detachment success rate and a 47.5% overall harvesting success rate, with average detachment and full-cycle times of 7.7 s and 15.3 s per apple, respectively. These results highlight the system’s potential for advancing robotic fruit harvesting and contribute to the ongoing development of autonomous agricultural technologies. Full article

Aiming at the economic problems of industrial users with wind power, photovoltaic, and small hydropower resources in clean energy consumption and trading with superior power grids, this paper proposes a distributed pumped storage capacity optimization configuration method considering clean energy systems. First, considering the maximization of the investment benefit of distributed pumped storage as the upper goal, a configuration scheme of the installed capacity is formulated. Second, under the two-part electricity price mechanism, combined with the basin hydraulic coupling relationship model, the operation strategy optimization of distributed pumped storage power stations and small hydropower stations is carried out with the minimum operation cost of the clean energy system as the lower optimization objective. Finally, the bi-level optimization model is solved by combining the alternating direction multiplier method and CPLEX solver. This study demonstrates that distributed pumped storage implementation enhances seasonal operational performance, improving clean energy utilization while reducing industrial electricity costs. A post-implementation analysis revealed monthly operating cost reductions of 2.36, 1.72, and 2.13 million RMB for wet, dry, and normal periods, respectively. Coordinated dispatch strategies significantly decreased hydropower station water wastage by 82,000, 28,000, and 52,000 cubic meters during corresponding periods, confirming simultaneous economic and resource efficiency improvements. Full article

In this paper, we address the problem of intelligent operation of Distribution Static Synchronous Compensators (D-STATCOMs) in power distribution systems to reduce energy losses and CO₂ emissions while improving system operating conditions. In addition, we consider the entire set of constraints inherent in the operation of such networks in an environment with D-STATCOMs. To solve such a problem, we used three master–slave methodologies based on sequential programming methods. In the proposed methodologies, the master stage solves the problem of intelligent D-STATCOM operation using the continuous versions of the Monte Carlo (MC) method, the population-based genetic algorithm (PGA), and the Particle Swarm Optimizer (PSO). The slave stage, for its part, evaluates the solutions proposed by the algorithms to determine their impact on the objective functions and constraints representing the problem. This is accomplished by running an Hourly Power Flow (HPF) based on the method of successive approximations. As test scenarios, we employed the 33- and 69-node radial test systems, considering data on power demand and CO₂ emissions reported for the city of Medellín in Colombia (as documented in the literature). Furthermore, a test system was adapted in this work to the demand characteristics of a feeder located in the city of Talca in Chile. This adaptation involved adjusting the conductors and voltage limits to include a test system with variations in power demand due to seasonal changes throughout the year (spring, winter, autumn, and summer). Demand curves were obtained by analyzing data reported by the local network operator, i.e., Compañía General de Electricidad. To assess the robustness and performance of the proposed optimization approach, each scenario was simulated 100 times. The evaluation metrics included average solution quality, standard deviation, and repeatability. Across all scenarios, the PGA consistently outperformed the other methods tested. Specifically, in the 33-node system, the PGA achieved a 24.646% reduction in energy losses and a 0.9109% reduction in CO₂ emissions compared to the base case. In the 69-node system, reductions reached 26.0823% in energy losses and 0.9784% in CO₂ emissions compared to the base case. Notably, in the case of the Talca feeder—particularly during summer, the most demanding season—the PGA yielded the most significant improvements, reducing energy losses by 33.4902% and CO₂ emissions by 1.2805%. Additionally, an uncertainty analysis was conducted to validate the effectiveness and robustness of the proposed optimization methodology under realistic operating variability. A total of 100 randomized demand profiles for both active and reactive power were evaluated. The results demonstrated the scalability and consistent performance of the proposed strategy, confirming its effectiveness under diverse and practical operating conditions. Full article

With the continuous improvement of reservoir projects and the advancement of digital twin basin initiatives in China, rapidly and accurately generating long-term practical reservoir operation schedules has become a key priority for stakeholders. This study proposes a Theory-guided Long Short-Term Memory (TgLSTM) model to extract optimal reservoir operation rules accurately and reliably. Concretely, TgLSTM integrates data-fitting accuracy with the physical constraints of an operation, e.g., water level constraints and minimal discharge constraints, to address the low credibility often observed in conventional LSTM networks. Using the Three Gorges Reservoir during the dry season as a case study, a multi-year hydrological series optimized by particle swarm optimization (PSO) was used to train the TgLSTM network and derive optimized operation rules. Results show that TgLSTM efficiently generates operation schemes close to the theoretical optimum, achieving power generations of 4.27 × 10¹⁰ kW·h and 4.19 × 10¹⁰ kW·h in two test years, with deviations of only 4.20% and 2.33%, respectively. Compared to traditional LSTM models, TgLSTM is more reliable as it captures key operational characteristics such as terminal water levels and water level fluctuations, maintaining an average ten-day drawdown depth below 1.5 m—significantly lower than the 7 m fluctuations observed with conventional LSTM. Furthermore, comparative analyses against SwR, BP–ANN, and SVM confirm that TgLSTM offers a moderate performance in absolute metrics but is the best to simulate the constrained reservoir operation. Full article

Accurate wind-speed prediction plays an important role in improving the operation stability of wind-power generation systems. However, the inherent complexity of meteorological dynamics poses a major challenge to forecasting accuracy. In order to overcome these limitations, we propose a new hybrid framework, which combines variational mode decomposition (VMD) for signal processing, enhanced quantum particle swarm optimization (e-QPSO), an improved walking optimization algorithm (IHOA) for feature selection and the long short-term memory (LSTM) network, and which finally establishes a reliable prediction architecture. The purpose of this paper is to optimize VMD by using the e-QPSO algorithm to improve the problems of excessive filtering or error filtering caused by parameter problems in VMD, as the noise signal cannot be filtered completely, and the number of sources cannot be accurately estimated. The IHOA algorithm is used to find the optimal hyperparameters of LSTM to improve the learning efficiency of neurons and improve the fitting ability of the model. The proposed e-QPSO-VMD-IHOA-LSTM model is compared with six established benchmark models to verify its predictive ability. The effectiveness of the model is verified by experiments using the hourly wind-speed data measured in four seasons in Changchun in 2023. The MAPE values of the four datasets were 0.0460, 0.0212, 0.0263, and 0.0371, respectively. The results show that e-QPSO-VMD effectively processes the data and avoids the problem of error filtering, while IHOA effectively optimizes the LSTM parameters and improves prediction performance. Full article

To investigate the impact of sand-laden flow on energy loss in Francis turbines, this study integrates entropy generation theory with numerical simulations conducted using ANSYS CFX. The mixture multiphase flow model and the SST k-ω turbulence model are employed to simulate the solid–liquid two-phase flow throughout the entire flow passage of the turbine at the Gengda Hydropower Station (Minjiang River Basin section, 103°17′ E and 31°06′ N). The energy loss characteristics under different off-design conditions are analyzed on the basis of the average sediment concentration during the flood season (2.9 kg/m³) and a median particle diameter of 0.058 mm. The results indicate that indirect entropy generation and wall entropy generation are the primary contributors to total energy loss, while direct entropy generation accounts for less than 1%. As the guide vane opening increases, the proportion of wall entropy generation initially rises and then decreases, while the total indirect entropy generation exhibits a non-monotonic trend dominated by the flow pattern in the draft tube. Entropy generation on the runner walls increases steadily with larger openings, whereas entropy generation on the draft tube walls first decreases and then increases. The variation in entropy generation on the guide vanes remains relatively small. These findings provide technical support for the optimal design and operation of turbines in sediment-rich rivers. Full article

Effective energy management in microgrids is essential for integrating renewable energy sources and maintaining operational stability. Machine learning (ML) techniques offer significant potential for optimizing microgrid performance. This study provides a comprehensive comparative performance evaluation of four ML-based control strategies: deep Q-networks (DQNs), proximal policy optimization (PPO), Q-learning, and advantage actor–critic (A2C). These strategies were rigorously tested using simulation data from a representative islanded microgrid model, with metrics evaluated across diverse seasonal conditions (autumn, spring, summer, winter). Key performance indicators included overall episodic reward, unmet load, excess generation, energy storage system (ESS) state-of-charge (SoC) imbalance, ESS utilization, and computational runtime. Results from the simulation indicate that the DQN-based agent consistently achieved superior performance across all evaluated seasons, effectively balancing economic rewards, reliability, and battery health while maintaining competitive computational runtimes. Specifically, DQN delivered near-optimal rewards by significantly reducing unmet load, minimizing excess renewable energy curtailment, and virtually eliminating ESS SoC imbalance, thereby prolonging battery life. Although the tabular Q-learning method showed the lowest computational latency, it was constrained by limited adaptability in more complex scenarios. PPO and A2C, while offering robust performance, incurred higher computational costs without additional performance advantages over DQN. This evaluation clearly demonstrates the capability and adaptability of the DQN approach for intelligent and autonomous microgrid management, providing valuable insights into the relative advantages and limitations of various ML strategies in complex energy management scenarios. Full article