Search Results (36)

This preliminary study investigates a direct, non-delaminated route to valorize multilayer pharmaceutical sachet offcuts (comprising paper/plastic/aluminum) as partial substitutes for wood fiber in wood-based panels. Milled offcuts were incorporated at 10, 20, and 30 wt% (control: wood only). Laboratory mats were hot-pressed at 170 °C for 9 min under a staged pressure regime. Sampling and three-point bending were performed according to EN 326-1 and EN 310, respectively, with the density held essentially constant by controlling the mat mass and press stops. Bending stiffness (MOE) was maintained at 10–20 wt% (within experimental uncertainty of the reference), while 30 wt% showed a consistent downward trend (approximately 10%). Bending strength (MOR) peaked at 10 wt% (approximately 8% higher than the reference), then declined at 20% and 30%. Representative stress–strain curves corroborated these outcomes, indicating auxiliary bonding and crack-bridging effects at low waste loadings. Hygroscopic performance improved monotonically: 24 h water absorption and thickness swelling decreased progressively with increasing substitution, attributable to the hydrophobic polymer layers and aluminum fragments interrupting capillary pathways. Process observations identified opportunities to improve press-cycle efficiency at higher waste contents, and the dispersed foil imparted a subtle decorative sheen. Overall, the results establish the technical feasibility and a practical utilization window of approximately 10–20 wt% for furniture-grade applications. Limitations include the laboratory scale, a single resin/press schedule, and the absence of internal bond, density profile, emissions, and long-term durability tests—topics prioritized for future work (including TGA/DSC, EN 317 extensions, and scale-up). Full article

To address the pressing challenges of low new energy utilization, high power system operating costs, and compromised power supply reliability in regional grids, we propose a multi-time-scale source–storage–load coordinated scheduling strategy that explicitly accounts for the characteristic distribution of grid-connected energy storage stations, including their state-of-charge constraints, round-trip efficiency profiles, and location-specific operational dynamics. A day-ahead scheduling framework is developed by integrating the multi-time-scale behavioral patterns of diverse load-side demand response resources with the dynamic operational characteristics of energy storage stations. By embedding intra-day rolling optimization and real-time corrective adjustments, we mitigate prediction errors and adapt to unforeseen system disturbances, ensuring enhanced operational accuracy. The objective function minimizes a weighted sum of system operation costs encompassing generation, transmission, and auxiliary services; wind power curtailment penalties for unused renewables; and load shedding penalties from unmet demand, balancing economic efficiency with supply quality. A mixed-integer programming model formalizes these tradeoffs, solved via MATLAB 2020b coupled CPLEX to guarantee optimality. Simulation results demonstrate that the strategy significantly cuts wind power curtailment, reduces system costs, and elevates new energy consumption—outperforming conventional single-time-scale methods in harmonizing renewable integration with grid reliability. This work offers a practical solution for enhancing grid flexibility in high-renewable penetration scenarios. Full article

Hybrid natural/synthetic fiber laminates were examined as a practical process to cut mass, reduce material footprint, and meet structural demands while addressing sustainability targets. Yet direct, like-for-like comparisons generated under a single process and accompanied by durability measurements were limited, leaving design choices uncertain. This study aimed to fabricate and benchmark five representative laminates—C1: flax/epoxy, C2: jute/glass/epoxy, C3: hemp/carbon/epoxy, C4: flax/glass/bio-epoxy, and C5: kenaf/basalt/polyester—under a controlled hot-press schedule with a fixed cavity and verified fiber volume fraction. Panels were characterized using ASTM D3039 tension, ASTM D790 flexure, instrumented impact, 168 h water immersion, and thermogravimetric mass retention. The results were normalized to enable direct multi-criteria comparison, and a model was calibrated to predict tensile strength. C3 delivered the highest strengths (tension ≈ 120 MPa; flexure ≈ 126 MPa), while C5 showed the greatest impact capacity (≈60 kJ/m²). End-of-test water uptake at 168 h was C1 ≈ 3.4%, C2 ≈ 2.6%, C3 ≈ 1.4%, C4 ≈ 2.1%, and C5 ≈ 2.3%. The tensile predictor was fitted to panel means, with an R² of 0.988, and maintained an R² of 0.96 under leave-one-configuration-out testing. These results indicated that carbon-containing hybrids played the most critical roles in terms of stiffness, with kenaf/basalt being most suitable for stiffness-critical components at a similar density, and flax/glass with a bio-resin maximized the sustainability score while maintaining adequate strength. Future research should focus on enhancing specific strength at high renewable content through interface treatments, and extended modeling to improve flexure and impact responses. Full article

Ensuring optimal adhesive curing during plywood and LVL (Layered Veneer Lumber) hot pressing requires accurate knowledge of internal temperature distribution, which is often difficult to assess using conventional surface-based measurements. This study introduces a custom-developed multi-layer smart sensor network capable of in situ, real-time temperature profiling across LVL layers during industrial hot pressing. The system integrates miniature embedded sensors and proprietary data acquisition software, enabling the simultaneous multi-point monitoring of thermal dynamics with a high temporal resolution. Experiments were performed on LVL panels of varying thicknesses, applying industry-standard pressing schedules derived from conventional calculation rules. Despite adherence to prescribed pressing times, results reveal significant core temperature deficits in thicker panels, potentially compromising adhesive gelation and overall bonding quality. These findings underline the need to revisit the pressing time determination for thicker products and demonstrate the potential of advanced sensing technologies to support adaptive process control. The proposed approach contributes to smart manufacturing and the remote-like monitoring of internal thermal states, providing valuable insights for enhancing product performance and industrial process efficiency. Full article

Climate change is a pressing global issue with severe consequences for the planet and human health. The Earth’s temperature has risen by 2 °C from 1901 to 2023, and this warming trend is expected to continue, causing potentially dangerous shifts in climate. Climate change impacts are already visible, with more frequent and severe heat waves, droughts, intense rain, and floods becoming increasingly common. Therefore, hydropower can contribute to addressing the global climate change issue and help to achieve global energy transition and stabilize global energy security. A Long Short-Term Memory (LSTM)-based model implemented in Python for global and regional hydropower forecasting was developed for a study period of 2023 to 2060 by taking the input data from 1980 to 2022. The results revealed that Asian countries have greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh in 2030, 2772.06 TWh in 2040, 2811.41 TWh in 2050, and 3195.79 TWh in 2060, as compared with the other regions of the world like the Middle East, Africa, Asia, Common Wealth of Independent State (CIS), Europe, North America, and South and Central America. The global hydropower potential is also expected to increase from 4350.12 TWh in 2023 to 4806.26 TWh in 2030, 5393.80 TWh in 2040, 6003.53 TWh in 2050, and 6644.06 TWh in 2060, which is sufficient for achieving energy transition and energy security goals. Furthermore, the performance and accuracy of the LSTM-based model were found to be 98%. This study will help in the efficient scheduling and management of hydropower resources, reducing uncertainties caused by environmental variability such as precipitation and runoff. The proposed model contributes to the energy transition and security that is needed to meet the global climate targets. Full article

Every year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. Consequently, the need to reschedule aircraft and crew to better address these problems has become pressing. The airline scheduling problem comprises two stages—that is, the Aircraft-Routing Problem (ARP) and the Crew-Pairing Problem (CPP). While the ARP and CPP have traditionally been solved sequentially, such an approach fails to capture their interdependencies, often compromising the robustness of aircraft and crew schedules in the face of disruptions. However, existing integrated ARP and CPP models often apply static rules for buffer time allocation, which may result in excessive and ineffective long-buffer connections. To bridge these gaps, we propose a robust integrated ARP and CPP model with two key innovations: (1) the definition of new critical connections (NCCs), which combine structural feasibility with data-driven delay risk; and (2) a spatiotemporal delay-prediction module that quantifies connection vulnerability. The problem is formulated as a sequential decision-making process and solved via a novel multi-agent reinforcement learning algorithm. Numerical results demonstrate that the novel method outperforms prior methods in the literature in terms of solving speed and can also enhance planning robustness. This, in turn, can enhance both operational profitability and passenger satisfaction. Full article

Optimizing the use of resources is a key aspect of organizational management. Various methods have been developed and applied to optimize different variables, including sequencing methods that aim to minimize work time. This paper presents an integrated approach for optimizing the sequencing of operations, considering indicators such as usage time, completion time, waiting time, delivery delay, and flow time. A multi-criteria optimization method with weighted aggregation was used, employing either an exhaustive search or a heuristic algorithm with nested loops, in which multiple possible combinations of operational sequences were evaluated, considering several key indicators and their respective weights. The application of the methodology in a press validated its effectiveness, providing managers with key information to prioritize the indicators according to their needs, whether optimizing resource usage or minimizing waiting times and delays. The application resulted in a 95.3% improvement in the level of utilization; a 79.3% reduction in the average completion time; a 90.5% reduction in machine waiting time; and a 90.9% decrease in product delivery delay. The results show that prioritizing the objective function leads to a balanced optimization of all indicators, improving operational efficiency and reducing flow time. This study contributes to the body of knowledge on production scheduling by offering a novel multi-criteria optimization approach in manufacturing settings. The validated methodology can be adapted to a variety of industries and offers flexibility to align with the specific interests of each organization. Full article

This paper presents a deep neural network (DNN)-embedded mixed-integer linear programming (MILP) model for fault prediction and production optimization in tablet pressing machines. The DNN predicts the probability of failures during the tablet pressing process by analyzing key operational parameters such as pressure, temperature, humidity, speed, vibration, and number of maintenance cycles. The MILP model optimizes the temperature and humidity settings, production schedules, and maintenance planning to maximize total profit while minimizing penalties for fault pressing, energy consumption, and maintenance costs. To integrate DNN into the MILP framework, Big-M constraints are applied to linearize the Rectified Linear Unit (ReLU) activation functions, ensuring solvability and global optimality of the optimization problem. A case study using the Kaggle dataset demonstrates the model’s ability to dynamically adjust production and maintenance schedules, enhancing profitability and resource utilization under fluctuating electricity prices. Sensitivity analyses further highlight the model’s robustness to variations in maintenance and energy costs, striking an effective balance between cost efficiency and production quality, which makes it a promising solution for intelligent scheduling and optimization in complex manufacturing environments. Full article

With the rapid expansion of 5G base stations, the increasing energy consumption and fluctuations in power grid loads pose significant challenges to both network operators and grid stability. This paper proposes a coordinated scheduling strategy designed to address these pressing issues by leveraging the flexible load management capabilities of 5G base stations and their potential for inter-regional power demand response within the smart grid framework. This study begins by quantifying the dispatch potential of 5G base stations through a detailed analysis of their load dynamics, particularly under tidal fluctuations, which are critical for understanding the temporal variability of energy consumption. Building on this foundation, dormancy and load transfer strategies are introduced to model the scheduling potential for regional energy storage, enabling more efficient utilization of available resources. To further enhance the optimization of energy distribution, a many-to-many proportional energy-sharing algorithm is developed, which facilitates the aggregation of scheduling capacities across multiple regions. Finally, a comprehensive multi-objective, two-layer collaborative dispatching strategy is proposed, aiming to mitigate grid load volatility and reduce electricity procurement costs for 5G operators. Extensive simulation results demonstrate the effectiveness of this strategy, showing a significant reduction in grid load variance by 37.88% and a notable decrease in operational electricity costs for 5G base stations from CNY 4616.0 to 3024.1. These outcomes highlight the potential of the proposed approach to achieve a win-win scenario, benefiting both base station operators and the smart grid by enhancing energy efficiency and grid stability. Full article

The International Maritime Organization (IMO) aims for net zero emissions in shipping by 2050. Ports, key links in the supply chain, are embracing green innovation, focusing on efficient berth and quay crane scheduling to support green port development amid limited resources. Additionally, the energy consumption and carbon emissions from the port shipping industry contribute significantly to environmental challenges and the sustainable development of ports. Therefore, reducing carbon emissions, particularly those generated during vessel berthing, has become a pressing task for the industry. The increasing complexity of berth allocation now requires compliance to vessel service standards while controlling carbon emissions. This study presents an integrated model that incorporates tidal factors into the joint optimization of berth and quay crane operations, addressing both service standards and emissions during port stays and crane activities, and further designs a PSO-GA hybrid algorithm, combining particle swarm optimization (PSO) with crossover and mutation operators from a genetic algorithm (GA), to enhance optimization accuracy and efficiency. Numerical experiments using actual data from a container terminal demonstrate the effectiveness and superiority of the PSO-GA algorithm compared to the traditional GA and PSO. The results show a reduction in total operational costs by 24.1% and carbon emissions by 15.3%, highlighting significant potential savings and environmental benefits for port operators. Furthermore, the findings reveal the critical role of tidal factors in improving berth and quay crane scheduling. The results provide decision-making support for the efficient operation and carbon emission control of green ports. Full article

In this paper, the feasibility of an innovative solid-state recycling process for aluminum alloy AA6063 chips through direct rolling is studied, with the aim of offering an environmentally sustainable alternative to conventional recycling processes. Aluminum chips, produced by milling an AA6063 billet without the use of lubricants, were first compacted using a hydraulic press with a 200 kN load and subsequently heat-treated at 570 °C for 6 h. The compacted chips were directly hot-rolled through several successive passes at 490 °C. The bulk material underwent the same rolling schedule to allow comparison of the samples and assess the process, in terms of mechanical properties and microstructure. All the rolled samples were tested by tensile and microhardness tests, whereas the microstructure was observed by an optical microscope and the EBSD-SEM technique. The fracture surface of all tested samples was analyzed by SEM. Recycled samples exhibited good mechanical properties, comparable to those of the bulk material. In particular, the bulk material showed an ultimate tensile strength of 218 MPa, in contrast to 177 MPa for the recycled chips, and comparable elongation at break. This study demonstrates that direct rolling of compacted aluminum chips is both technically feasible and has environmental benefits, offering a promising approach for sustainable aluminum recycling in industrial applications within a circular economy framework. Full article

This study presents an innovative automatic scheduling method for the relocation of customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. With the expansion of global trade, customs authorities face increasing pressure to enhance logistics efficiency. Traditional vehicle scheduling often relies on manual processes and simplistic algorithms, resulting in prolonged waiting times and inefficient resource allocation. This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. EVI technology facilitates rapid identification of vehicles entering customs facilities, while biometric recognition ensures that only authorized personnel can operate specific vehicles. This dual-layered approach enhances security and streamlines the inspection process, significantly reducing delays. A thorough analysis of the existing literature on customs vehicle scheduling identifies key limitations in current methodologies. The automatic scheduling algorithm is detailed, encompassing vehicle prioritization criteria, dynamic path planning, and real-time driver assignment. The genetic algorithm framework allows for adaptive responses to varying operational conditions. Extensive simulations using real-world data from customs operations validate the effectiveness of the proposed method. Results indicate a significant reduction in vehicle waiting times—up to 30%—and an increase in resource utilization rates by approximately 25%. These findings demonstrate the potential of integrating EVI and biometric technologies to transform customs logistics management. Additionally, a comparison against state-of-the-art scheduling algorithms, such as NSGA-II and MOEA/D, reveals superior efficiency and adaptability. This research not only addresses pressing challenges faced by customs authorities but also contributes to optimizing logistics operations more broadly. In conclusion, the automatic scheduling method presented represents a significant advancement in customs logistics, providing a robust solution for managing complex vehicle scheduling scenarios. Future research directions will focus on refining the algorithm to handle peak traffic periods and exploring predictive analytics for enhanced scheduling optimization. Advancements in the intersection of technology and logistics aim to support more efficient and secure customs operations globally. Full article

In the context of the “Dual-Carbon Strategy”, the seamless integration and optimal utilization of renewable energy sources present a pressing challenge for the emerging power system. The advent of demand-side response technology offers a promising solution to this challenge. This study proposes a two-stage response control strategy for multiple DR clusters based on the specific response time characteristics of industrial and residential loads. The strategy enhances the utilization rate of wind power, harnesses the joint response capability of various types of loads on the demand side, and ensures the overall revenue of the load aggregator (LA). It underscores the importance of industrial loads in large-scale energy consumption control throughout the overall consumption response process, while residential load clusters exhibit quick response flexibility. A homogeneous energy consumption sorting unit response strategy is established from the perspective of a residential load variable-frequency air conditioning cluster unit. This strategy addresses the challenge faced by industrial electrolytic aluminum plants in coping with long-term response intervals amidst significant fluctuations in wind power consumption demand, which may lead to incomplete consumption. This study constructs a response model based on industrial and residential time-sharing tariffs, as well as the aggregator consumption penalty price, with the optimal load energy economy index serving as the evaluation criterion. A series of simulations are conducted to comprehensively evaluate the energy consumption of the two load clusters at all times and the total revenue of the aggregator in the response zone. The objective is to achieve a win–win situation for the total wind power energy consumption rate and the aggregator’s economy. The results of the simulations demonstrate that the response control strategy proposed in this study enhances the overall energy consumption rate by nearly 4 percentage points compared to a single industrial cluster. The total benefit of the load aggregator can reach CNY 941,732.09. The consumption response scheduling strategy put forward in this paper bolsters wind power consumption, triggers demand response, and significantly propels the comprehensive construction and development of the dual-high power grid. Full article

The primary function of a mine hoist is the transportation of personnel and equipment, serving as a crucial link between underground and surface systems. The proper functioning of key components such as work braking and safety braking is essential for ensuring the safety of both personnel and equipment, thereby playing a critical role in the safe operation of coal mines. As coal mining operations extend to greater depths, they introduce heightened challenges for safe transportation, compounded by increased equipment loss. Consequently, there is a pressing need to enhance safety protocols to safeguard personnel and materials. Traditional maintenance and repair methods, characterized by routine equipment inspections and scheduled downtime, often fall short in addressing emerging issues promptly, leading to production delays and heightened risks for maintenance personnel. This underscores the necessity of adopting predictive maintenance strategies, leveraging digital twin models to anticipate and prevent potential faults in mine hoists. In summary, the implementation of predictive maintenance techniques grounded in digital twin technology represents a proactive and scientifically rigorous approach to ensuring the continued safe operation of mine hoists amidst the evolving challenges of deepening coal mining operations. In this study, we propose the integration of a CNN-LSTM algorithm within a digital twin framework for predicting faults in mine hoist braking systems. Utilizing software such as AMESim 2019 and MATLAB 2016b, we conduct joint simulations of the hoist braking digital twin system. Subsequently, leveraging the simulation model, we establish a fault diagnosis platform for the hoist braking system. Finally, employing the CNN-LSTM network model, we forecast failures in the mine hoist braking system. Experimental findings demonstrate the effectiveness of our proposed algorithm, achieving a prediction accuracy of 95.35%. Comparative analysis against alternative algorithms confirms the superior performance of our approach. Full article

With the electrification and connectivity of vehicles in transportation, traditional vehicles with single drivetrains are being replaced by pure electric or hybrid electric vehicles (HEVs). This evolution has given rise to diverse electromechanical coupling drivetrains. There is a pressing need to update simulation software to assess the economic performance of vehicles in various environments, and promote sustainable development and energy conservation. This paper presents a unified framework for the construction and automated operation of large-scale automated vehicle simulations with multiple drivetrain types, facilitating synchronous information exchange among vehicles. Central to the framework is the development of a plug-and-play vehicle model based on a modular component design, facilitating the rapid assembly of vehicles with varied drivetrain configurations and standardizing simulation file management. Additionally, a standardized simulation process construction is established to accommodate the automated operation of simulations. Furthermore, a data scheduling method among vehicles is introduced to achieve multi-vehicle interconnection simulation. Finally, the effectiveness of the framework is demonstrated through a case study involving queue-following control for HEVs. This framework aims to provide a comprehensive solution for quickly establishing automated simulation environments for multi-vehicle interaction, enhancing model reusability and scalability. Full article