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Keywords = operational energy consumption

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19 pages, 1835 KiB  
Article
Methods for Enhancing Energy and Resource Efficiency in Sunflower Oil Production: A Case Study from Bulgaria
by Penka Zlateva, Angel Terziev, Nikolay Kolev, Martin Ivanov, Mariana Murzova and Momchil Vasilev
Eng 2025, 6(8), 195; https://doi.org/10.3390/eng6080195 - 6 Aug 2025
Abstract
The rising demand for energy resources and industrial goods presents significant challenges to sustainable development. Sunflower oil, commonly utilized in the food sector, biofuels, and various industrial applications, is notably affected by this demand. In Bulgaria, it serves as a primary source of [...] Read more.
The rising demand for energy resources and industrial goods presents significant challenges to sustainable development. Sunflower oil, commonly utilized in the food sector, biofuels, and various industrial applications, is notably affected by this demand. In Bulgaria, it serves as a primary source of vegetable fats, ranking second to butter in daily consumption. The aim of this study is to evaluate and propose methods to improve energy and resource efficiency in sunflower oil production in Bulgaria. The analysis is based on data from an energy audit conducted in 2023 at an industrial sunflower oil production facility. Reconstruction and modernization initiatives, which included the installation of high-performance, energy-efficient equipment, led to a 34% increase in energy efficiency. The findings highlight the importance of adjusting the technological parameters such as temperature, pressure, grinding level, and pressing time to reduce energy use and operational costs. Additionally, resource efficiency is improved through more effective raw material utilization and waste reduction. These strategies not only enhance the economic and environmental performance of sunflower oil production but also support sustainable development and competitiveness within the industry. The improvement reduces hexane use by approximately 2%, resulting in energy savings of 12–15 kWh/t of processed seeds and a reduction in CO2 emissions by 3–4 kg/t, thereby improving the environmental profile of sunflower oil production. Full article
(This article belongs to the Special Issue Interdisciplinary Insights in Engineering Research)
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34 pages, 3002 KiB  
Article
A Refined Fuzzy MARCOS Approach with Quasi-D-Overlap Functions for Intuitive, Consistent, and Flexible Sensor Selection in IoT-Based Healthcare Systems
by Mahmut Baydaş, Safiye Turgay, Mert Kadem Ömeroğlu, Abdulkadir Aydin, Gıyasettin Baydaş, Željko Stević, Enes Emre Başar, Murat İnci and Mehmet Selçuk
Mathematics 2025, 13(15), 2530; https://doi.org/10.3390/math13152530 - 6 Aug 2025
Abstract
Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp [...] Read more.
Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp transitions between preference levels. These assumptions can lead to decision outcomes with insufficient differentiation, limited discriminatory capacity, and potential issues in consistency and sensitivity. To overcome these limitations, this study proposes a novel fuzzy decision-making framework by integrating Quasi-D-Overlap functions into the fuzzy MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution) method. Quasi-D-Overlap functions represent a generalized extension of classical overlap operators, capable of capturing partial overlaps and interdependencies among criteria while preserving essential mathematical properties such as associativity and boundedness. This integration enables a more intuitive, flexible, and semantically rich modeling of real-world fuzzy decision problems. In the context of real-time health monitoring, a case study is conducted using a hybrid edge–cloud architecture, involving sensor tasks such as heartrate monitoring and glucose level estimation. The results demonstrate that the proposed method provides greater stability, enhanced discrimination, and improved responsiveness to weight variations compared to traditional fuzzy MCDM techniques. Furthermore, it effectively supports decision-makers in identifying optimal sensor alternatives by balancing critical factors such as accuracy, energy consumption, latency, and error tolerance. Overall, the study fills a significant methodological gap in fuzzy MCDM literature and introduces a robust fuzzy aggregation strategy that facilitates interpretable, consistent, and reliable decision making in dynamic and uncertain healthcare environments. Full article
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19 pages, 1242 KiB  
Article
Integration of Renewable Energy Sources to Achieve Sustainability and Resilience of Mines in Remote Areas
by Josip Kronja and Ivo Galić
Mining 2025, 5(3), 51; https://doi.org/10.3390/mining5030051 - 6 Aug 2025
Abstract
Mining (1) operations in remote areas (2) face significant challenges related to energy supply, high fuel costs, and limited infrastructure. This study investigates the potential for achieving energy independence (3) and resilience (4) in such environments through the integration of renewable energy sources [...] Read more.
Mining (1) operations in remote areas (2) face significant challenges related to energy supply, high fuel costs, and limited infrastructure. This study investigates the potential for achieving energy independence (3) and resilience (4) in such environments through the integration of renewable energy sources (5) and battery–electric mining equipment. Using the “Studena Vrila” underground bauxite mine as a case study, a comprehensive techno-economic and environmental analysis was conducted across three development models. These models explore incremental scenarios of solar and wind energy adoption combined with electrification of mobile machinery. The methodology includes calculating levelized cost of energy (LCOE), return on investment (ROI), and greenhouse gas (GHG) reductions under each scenario. Results demonstrate that a full transition to RES and electric machinery can reduce diesel consumption by 100%, achieve annual savings of EUR 149,814, and cut GHG emissions by over 1.7 million kg CO2-eq. While initial capital costs are high, all models yield a positive Net Present Value (NPV), confirming long-term economic viability. This research provides a replicable framework for decarbonizing mining operations in off-grid and infrastructure-limited regions. Full article
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12 pages, 1432 KiB  
Article
Optimizing Gear Selection and Engine Speed to Reduce CO2 Emissions in Agricultural Tractors
by Murilo Battistuzzi Martins, Jessé Santarém Conceição, Aldir Carpes Marques Filho, Bruno Lucas Alves, Diego Miguel Blanco Bertolo, Cássio de Castro Seron, João Flávio Floriano Borges Gomides and Eduardo Pradi Vendruscolo
AgriEngineering 2025, 7(8), 250; https://doi.org/10.3390/agriengineering7080250 - 6 Aug 2025
Abstract
In modern agriculture, tractors play a crucial role in powering tools and implements. Proper operation of agricultural tractors in mechanized field operations can support sustainable agriculture and reduce emissions of pollutants such as carbon dioxide (CO2). This has been a recurring [...] Read more.
In modern agriculture, tractors play a crucial role in powering tools and implements. Proper operation of agricultural tractors in mechanized field operations can support sustainable agriculture and reduce emissions of pollutants such as carbon dioxide (CO2). This has been a recurring concern associated with agricultural intensification for food production. This study aimed to evaluate the optimization of tractor gears and engine speed during crop operations to minimize CO2 emissions and promote sustainability. The experiment was conducted using a strip plot design with subdivided sections and six replications, following a double factorial structure. The first factor evaluated was the type of agricultural implement (disc harrow, subsoiler, or sprayer), while the second factor was the engine speed setting (nominal or reduced). Operational and energy performance metrics were analyzed, including fuel consumption and CO2 emissions, travel speed, effective working time, wheel slippage, and working depth. Optimized gear selection and engine speeds resulted in a 20 to 40% reduction in fuel consumption and CO2 emissions. However, other evaluated parameters remain unaffected by the reduced engine speed, regardless of the implement used, ensuring the operation’s quality. Thus, optimizing operator training or configuring machines allows for environmental impact reduction, making agricultural practices more sustainable. Full article
(This article belongs to the Collection Research Progress of Agricultural Machinery Testing)
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30 pages, 3996 KiB  
Article
Incentive-Compatible Mechanism Design for Medium- and Long-Term/Spot Market Coordination in High-Penetration Renewable Energy Systems
by Sicong Wang, Weiqing Wang, Sizhe Yan and Qiuying Li
Processes 2025, 13(8), 2478; https://doi.org/10.3390/pr13082478 - 6 Aug 2025
Abstract
In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems [...] Read more.
In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article
(This article belongs to the Section Energy Systems)
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28 pages, 4243 KiB  
Article
Electric Bus Battery Energy Consumption Estimation and Influencing Features Analysis Using a Two-Layer Stacking Framework with SHAP-Based Interpretation
by Runze Liu, Jianming Cai, Lipeng Hu, Benxiao Lou and Jinjun Tang
Sustainability 2025, 17(15), 7105; https://doi.org/10.3390/su17157105 - 5 Aug 2025
Abstract
The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. [...] Read more.
The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior. Full article
(This article belongs to the Section Sustainable Transportation)
14 pages, 1310 KiB  
Article
Enhancing Energy Efficiency of Electric Grade Isopropyl Alcohol Production Process by Using Noble Thermally Coupled Distillation Technology
by Neha Agarwal, Nguyen Nhu Nga, Le Cao Nhien, Raisa Aulia Hanifah, Minkyu Kim and Moonyong Lee
Energies 2025, 18(15), 4159; https://doi.org/10.3390/en18154159 - 5 Aug 2025
Abstract
This study presents a comprehensive design, optimization, and intensification approach for enhancing the energy efficiency of electric grade isopropyl alcohol (IPA) production, a typical energy-intensive chemical process. The process entails preconcentration and dehydration steps, with the intensity of separation formulated from a multicomponent [...] Read more.
This study presents a comprehensive design, optimization, and intensification approach for enhancing the energy efficiency of electric grade isopropyl alcohol (IPA) production, a typical energy-intensive chemical process. The process entails preconcentration and dehydration steps, with the intensity of separation formulated from a multicomponent feed that consists of IPA and water, along with other impurities. Modeling and energy optimization were performed for a conventional distillation train as a base case by using the rigorous process simulator Aspen Plus V12.1. To improve energy efficiency, various options for intensifying distillation were examined. The side-stream preconcentration column was subsequently replaced by a dividing wall column (DWC) with two side streams, i.e., a Kaibel column, reducing the total energy consumption of corresponding distillation columns by 9.1% compared to the base case. Further strengthening was achieved by combining two columns in the preconcentration process into a single Kaibel column, resulting in a 22.8% reduction in reboiler duty compared to the base case. Optimization using the response surface methodology identified key operating parameters, such as side-draw positions and stage design, which significantly influence both energy efficiency and separation quality. The intensified Kaibel setup offers significant energy efficiencies and simplified column design, suggesting enormous potential for process intensification in energy-intensive distillation processes at the industrial level, including the IPA purification process. Full article
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21 pages, 3334 KiB  
Article
Market Research on Waste Biomass Material for Combined Energy Production in Bulgaria: A Path Toward Enhanced Energy Efficiency
by Penka Zlateva, Angel Terziev, Mariana Murzova, Nevena Mileva and Momchil Vassilev
Energies 2025, 18(15), 4153; https://doi.org/10.3390/en18154153 - 5 Aug 2025
Abstract
Using waste biomass as a raw material for the combined production of electricity and heat offers corresponding energy, economic, environmental and resource efficiency benefits. The study examines both the performance of a system for combined energy production based on the Organic Rankine Cycle [...] Read more.
Using waste biomass as a raw material for the combined production of electricity and heat offers corresponding energy, economic, environmental and resource efficiency benefits. The study examines both the performance of a system for combined energy production based on the Organic Rankine Cycle (ORC) utilizing wood biomass and the market interest in its deployment within Bulgaria. Its objective is to propose a technically and economically viable solution for the recovery of waste biomass through the combined production of electricity and heat while simultaneously assessing the readiness of industrial and municipal sectors to adopt such systems. The cogeneration plant incorporates an ORC module enhanced with three additional economizers that capture residual heat from flue gases. Operating on 2 t/h of biomass, the system delivers 1156 kW of electric power and 3660 kW of thermal energy, recovering an additional 2664 kW of heat. The overall energy efficiency reaches 85%, with projected annual revenues exceeding EUR 600,000 and a reduction in carbon dioxide emissions of over 5800 t/yr. These indicators can be achieved through optimal installation and operation. When operating at a reduced load, however, the specific fuel consumption increases and the overall efficiency of the installation decreases. The marketing survey results indicate that 75% of respondents express interest in adopting such technologies, contingent upon the availability of financial incentives. The strongest demand is observed for systems with capacities up to 1000 kW. However, significant barriers remain, including high initial investment costs and uneven access to raw materials. The findings confirm that the developed system offers a technologically robust, environmentally efficient and market-relevant solution, aligned with the goals of energy independence, sustainability and the transition to a low-carbon economy. Full article
(This article belongs to the Section B: Energy and Environment)
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28 pages, 2057 KiB  
Article
Design and Fabrication of a Cost-Effective, Remote-Controlled, Variable-Rate Sprayer Mounted on an Autonomous Tractor, Specifically Integrating Multiple Advanced Technologies for Application in Sugarcane Fields
by Pongpith Tuenpusa, Kiattisak Sangpradit, Mano Suwannakam, Jaturong Langkapin, Alongklod Tanomtong and Grianggai Samseemoung
AgriEngineering 2025, 7(8), 249; https://doi.org/10.3390/agriengineering7080249 - 5 Aug 2025
Abstract
The integration of a real-time image processing system using multiple webcams with a variable rate spraying system mounted on the back of an unmanned tractor presents an effective solution to the labor shortage in agriculture. This research aims to design and fabricate a [...] Read more.
The integration of a real-time image processing system using multiple webcams with a variable rate spraying system mounted on the back of an unmanned tractor presents an effective solution to the labor shortage in agriculture. This research aims to design and fabricate a low-cost, variable-rate, remote-controlled sprayer specifically for use in sugarcane fields. The primary method involves the modification of a 15-horsepower tractor, which will be equipped with a remote-control system to manage both the driving and steering functions. A foldable remote-controlled spraying arm is installed at the rear of the unmanned tractor. The system operates by using a webcam mounted on the spraying arm to capture high-angle images above the sugarcane canopy. These images are recorded and processed, and the data is relayed to the spraying control system. As a result, chemicals can be sprayed on the sugarcane accurately and efficiently based on the insights gained from image processing. Tests were conducted at various nozzle heights of 0.25 m, 0.5 m, and 0.75 m. The average system efficiency was found to be 85.30% at a pressure of 1 bar, with a chemical spraying rate of 36 L per hour and a working capacity of 0.975 hectares per hour. The energy consumption recorded was 0.161 kWh, while fuel consumption was measured at 6.807 L per hour. In conclusion, the development of the remote-controlled variable rate sprayer mounted on an unmanned tractor enables immediate and precise chemical application through remote control. This results in high-precision spraying and uniform distribution, ultimately leading to cost savings, particularly by allowing for adjustments in nozzle height from a minimum of 0.25 m to a maximum of 0.75 m from the target. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
35 pages, 6795 KiB  
Article
Thermal Analysis of Energy Efficiency Performance and Indoor Comfort in a LEED-Certified Campus Building in the United Arab Emirates
by Khushbu Mankani, Mutasim Nour and Hassam Nasarullah Chaudhry
Energies 2025, 18(15), 4155; https://doi.org/10.3390/en18154155 - 5 Aug 2025
Abstract
Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green [...] Read more.
Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article
(This article belongs to the Special Issue Energy Efficiency and Thermal Performance in Buildings)
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22 pages, 6187 KiB  
Article
Device Modeling Method for the Entire Process of Energy-Saving Retrofit of a Refrigeration Plant
by Xuanru Xu, Lun Zhang, Jun Chen, Qingbin Lin and Junjie Chen
Energies 2025, 18(15), 4147; https://doi.org/10.3390/en18154147 - 5 Aug 2025
Abstract
With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the [...] Read more.
With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the equipment within the chiller plants of central air-conditioning systems. Traditional modeling approaches have been static and have focused on modeling within narrow time frames when a certain amount of equipment operating data has accumulated, thus prioritizing the precision of the model itself while overlooking the fact that energy-saving retrofits are a long-term process. This study proposes a modeling scheme for the equipment within chiller plants throughout the energy-saving retrofit process. Based on the differences in the amount of available operating data for the equipment and the progress of retrofit implementation, the retrofit process was divided into three stages, each employing different modeling techniques and ensuring smooth transitions between the stages. The equipment within the chiller plants is categorized into two types based on the clarity of their operating characteristics, and two modeling schemes are proposed accordingly. Based on the proposed modeling scheme, chillers and chilled-water pumps were selected to represent the two types of equipment. Real operating data from actual retrofit projects was used to model the equipment and evaluate the accuracy of the model predictions. The results indicate that the models established by the proposed modeling scheme exhibit good accuracy at each stage of the retrofit, with the coefficients of variation (CV) remaining below 6.88%. Furthermore, the prediction accuracy improved as the retrofitting process progressed. The modeling scheme performs better on equipment with simpler and clearer operating characteristics, with a CV as low as 0.67% during normal operation stages. This underscores the potential application of the proposed modeling scheme throughout the energy-saving retrofit process and provides a model foundation for the subsequent optimization of the refrigeration system. Full article
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20 pages, 2385 KiB  
Article
Assessing Thermal Comfort in Green and Conventional Office Buildings in Hot Climates
by Abdulrahman Haruna Muhammad, Ahmad Taki and Sanober Hassan Khattak
Sustainability 2025, 17(15), 7078; https://doi.org/10.3390/su17157078 - 5 Aug 2025
Abstract
Green buildings are recognised for their potential to reduce energy consumption, minimise environmental impact, and improve occupants’ well-being, benefits that are especially critical in rapidly urbanising regions. However, questions remain about whether these buildings fully meet occupant comfort expectations while delivering energy efficiency. [...] Read more.
Green buildings are recognised for their potential to reduce energy consumption, minimise environmental impact, and improve occupants’ well-being, benefits that are especially critical in rapidly urbanising regions. However, questions remain about whether these buildings fully meet occupant comfort expectations while delivering energy efficiency. This is particularly relevant in Africa, where climate conditions and energy infrastructure challenges make sustainable building operation essential. Although interest in sustainable construction has increased, limited research has examined the real-world performance of green buildings in Africa. This study helps address that gap by evaluating indoor thermal comfort in a green-certified office building and two conventional office buildings in Abuja, Nigeria, through post-occupancy evaluation (POE). The Predicted Mean Vote (PMV) and Thermal Sensation Vote (TSV) were used to assess comfort, revealing discrepancies between predicted and actual occupant responses. In the green building, PMV indicated near-neutral conditions (0.28), yet occupants reported a slightly cool sensation (TSV: −1.1). Neutral temperature analysis showed that the TSV-based neutral temperature (26.5 °C) was 2.2 °C higher than the operative temperature (24.3 °C), suggesting overcooling. These findings highlight the importance of incorporating occupant feedback into HVAC control. Aligning cooling setpoints with comfort preferences could improve satisfaction and reduce unnecessary cooling, promoting energy-efficient building operation. Full article
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22 pages, 4189 KiB  
Article
A Hierarchical Path Planning Framework of Plant Protection UAV Based on the Improved D3QN Algorithm and Remote Sensing Image
by Haitao Fu, Zheng Li, Jian Lu, Weijian Zhang, Yuxuan Feng, Li Zhu, He Liu and Jian Li
Remote Sens. 2025, 17(15), 2704; https://doi.org/10.3390/rs17152704 - 4 Aug 2025
Abstract
Traditional path planning algorithms often fail to simultaneously ensure operational efficiency, energy constraint compliance, and environmental adaptability in agricultural scenarios, thereby hindering the advancement of precision agriculture. To address these challenges, this study proposes a deep reinforcement learning algorithm, MoE-D3QN, which integrates a [...] Read more.
Traditional path planning algorithms often fail to simultaneously ensure operational efficiency, energy constraint compliance, and environmental adaptability in agricultural scenarios, thereby hindering the advancement of precision agriculture. To address these challenges, this study proposes a deep reinforcement learning algorithm, MoE-D3QN, which integrates a Mixture-of-Experts mechanism with a Bi-directional Long Short-Term Memory model. This design enhances the efficiency and robustness of UAV path planning in agricultural environments. Building upon this algorithm, a hierarchical coverage path planning framework is developed. Multi-level task maps are constructed using crop information extracted from Sentinel-2 remote sensing imagery. Additionally, a dynamic energy consumption model and a progressive composite reward function are incorporated to further optimize UAV path planning in complex farmland conditions. Simulation experiments reveal that in the two-level scenario, the MoE-D3QN algorithm achieves a coverage efficiency of 0.8378, representing an improvement of 37.84–63.38% over traditional algorithms and 19.19–63.38% over conventional reinforcement learning methods. The redundancy rate is reduced to 3.23%, which is 38.71–41.94% lower than traditional methods and 4.46–42.77% lower than reinforcement learning counterparts. In the three-level scenario, MoE-D3QN achieves a coverage efficiency of 0.8261, exceeding traditional algorithms by 52.13–71.45% and reinforcement learning approaches by 10.15–50.2%. The redundancy rate is further reduced to 5.26%, which is significantly lower than the 57.89–92.11% observed with traditional methods and the 15.57–18.98% reported for reinforcement learning algorithms. These findings demonstrate that the MoE-D3QN algorithm exhibits high-quality planning performance in complex farmland environments, indicating its strong potential for widespread application in precision agriculture. Full article
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33 pages, 8443 KiB  
Article
Model for Planning and Optimization of Train Crew Rosters for Sustainable Railway Transport
by Zdenka Bulková, Juraj Čamaj and Jozef Gašparík
Sustainability 2025, 17(15), 7069; https://doi.org/10.3390/su17157069 - 4 Aug 2025
Abstract
Efficient planning of train crew rosters is a key factor in ensuring operational reliability and promoting long-term sustainability in railway transport, both economically and socially. This article presents a systematic approach to developing a crew rostering model in passenger rail transport, with a [...] Read more.
Efficient planning of train crew rosters is a key factor in ensuring operational reliability and promoting long-term sustainability in railway transport, both economically and socially. This article presents a systematic approach to developing a crew rostering model in passenger rail transport, with a focus on the operational setting of the train crew depot in Česká Třebová, a city in the Czech Republic. The seven-step methodology includes identifying available train shifts, defining scheduling constraints, creating roster variants, and calculating personnel and time requirements for each option. The proposed roster reduced staffing needs by two employees, increased the average shift duration to 9 h and 42 min, and decreased non-productive time by 384 h annually. These improvements enhance sustainability by optimizing human resource use, lowering unnecessary energy consumption, and improving employees’ work–life balance. The model also provides a quantitative assessment of operational feasibility and economic efficiency. Compared to existing rosters, the proposed model offers clear advantages and remains applicable even in settings with limited technological support. The findings show that a well-designed rostering system can contribute not only to cost savings and personnel stabilization, but also to broader objectives in sustainable public transport, supporting resilient and resource-efficient rail operations. Full article
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21 pages, 3452 KiB  
Article
Features of Ash and Slag Formation During Incomplete Combustion of Coal from the Karazhyra Deposit in Small- and Medium-Scale Power Plants
by Natalya Seraya, Vadim Litvinov, Gulzhan Daumova, Maksat Shaikhov, Raigul Ramazanova and Roza Aubakirova
Processes 2025, 13(8), 2467; https://doi.org/10.3390/pr13082467 - 4 Aug 2025
Abstract
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 [...] Read more.
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
(This article belongs to the Section Chemical Processes and Systems)
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