Search Results (251)

Large-scale composite equipment manufacturing imposes stringent requirements on the lean management of multi-specification fiber prepreg sheet storage, while existing optimization methods suffer from poor process adaptability, insufficient multi-objective collaborative optimization capability, and low space utilization of static layouts. This study constructs a symmetric optimization framework for multi-objective composite sheet storage to address these critical bottlenecks. Specifically, the multi-dimensional process value of fiber sheets is quantified, and the layered storage optimization problem is transformed into a 0–1 knapsack problem with symmetric constraints. An improved Dynamic Programming–Backtracking (DP-BT) material selection algorithm and an adaptive dynamic programming iterative space optimization algorithm are proposed to achieve a symmetric balance of inter-layer space utilization and global optimization. Experimental validation with actual production data of 17 fiber sheet types verifies that the proposed method enables space optimization for specified layer counts to maximize average space utilization, with the rate rising from 79.4% (initial 4-layer layout) to 95.7% (3-layer) and 99.9% (2-layer), and a peak single-layer utilization of 100%. This framework achieves favorable optimization performance in the target production scenario and provides a referenceable symmetric optimization approach for the lean storage management of similar fiber sheet storage scenarios in composite manufacturing. Full article

This study investigates the thermal performance of a flat-plate phase-change thermal energy storage system, focusing on two structural innovations: a cascaded arrangement of multiple phase-change materials (PCMs) with varying melting points, and the implementation of series/parallel flow configurations. A combined numerical and experimental approach is employed to analyze dynamic charging/discharging behavior. Quantitative results indicate that the cascaded configuration (three PCMs) reduces phase-change completion time by 13% and increases cooling energy storage power from 2.00 kW to 2.43 kW during charging compared to single-PCM systems. Flow configuration significantly impacts thermal response: the parallel layout delivers more stable cooling output, while the series layout achieves faster initial cooling (reaching 6.24 °C within 1200 s, 31% faster than the parallel layout). Experimental results reveal that inlet water temperature is the most critical operating parameter, with each 2 °C increase significantly prolonging charging time. This work offers practical guidance for the design and optimization of efficient cascaded PCM thermal storage systems. Full article

We propose and test a method to reduce the dimensionality of Full Waveform Inversion (FWI) inputs as a computational cost mitigation approach. Given modern seismic acquisition systems, the data (as an input for FWI) required for an industrial-strength case is in the teraflop level of storage; therefore, solving complex subsurface cases or exploring multiple scenarios with FWI becomes prohibitive. The proposed method utilizes a deep neural network with a binarized sensing layer that learns by compressed learning seismic acquisition layouts from a large corpus of subsurface models. Thus, given a large seismic data set to invert, the trained network selects a smaller subset of the data, then by using representation learning, an autoencoder computes latent representations of the shot gathers, followed by K-means clustering of the latent representations to further select the most relevant shot gathers for FWI. This approach can effectively be seen as a hierarchical selection. The proposed approach consistently outperforms random data sampling, even when utilizing only 10% of the data for 2D FWI, and these results pave the way to accelerating FWI in large scale 3D inversion. Full article

Addressing the dual challenges of efficient water resource utilization and high construction costs in agricultural production, this study proposes a low-cost sprinkler irrigation system featuring a joint optimized design of water supply facilities and sprinkler layout. Initially, to mitigate water wastage at the field boundaries, an enhanced sprinkler layout is designed. This design strategically adjusts sprinkler spacing to position units along the irrigation area’s perimeter, leveraging their adjustable spray angles for semicircular coverage, thereby achieving superior water conservation compared to traditional honeycomb full coverage layouts. Subsequently, considering the non-linear relationship between pipeline cost and its length and flow rate, a supply network comprising five independent pipelines running perpendicular to the river is constructed. Furthermore, water storage tanks are strategically located at the head of each pipeline near the water source to reduce costs. Finally, constrained by the daily soil moisture levels required for crop survival, an inference-based dimension reduction algorithm is employed to jointly optimize the daily pipeline flow rate and storage tank capacity for each supply line. Specifically, by constructing the functional mapping between flow rate and tank capacity, the complex bivariate optimization problem is reduced to a single-variable extremum problem. Additionally, a calculation method for the feasible region of decision variables is proposed to ensure solution validity. The results demonstrate that the proposed scheme achieves a minimum total construction cost of CNY 2,611,404.00 with a total storage tank capacity of 114,892.40 L, and generates a detailed daily irrigation strategy. This study offers a significant model reference and a technical pathway for developing agricultural irrigation systems that are both economical and efficient. Full article

The power fluctuation of large-scale photovoltaic (PV) clusters is significantly affected by cloud movement. Aiming at the engineering reality that meteorological observation data are generally lacking for most power stations in wide-area PV clusters, as well as the problem that existing models overfit second-order high-frequency noise such as microscopic cloud deformation, this paper proposes a disturbance reconstruction and smoothing effect quantification method for PV clusters focusing on the first-order dominant meteorological component. First, a clear-sky model is introduced as a deterministic trend filter to extract the purely random disturbance sequence that induces grid-connection risks from the measured output power. Second, the dimensionality reduction modeling concept of “macro-advection dominance and microscopic deformation filtering” is established: the PV cluster is finely partitioned by fusing Dynamic Time Warping (DTW) and geographical distance, and a cross-space inversion of the macro-cloud velocity vector is realized, driven by pure power data using the Time-Lagged Cross-Correlation (TLCC) algorithm, thus constructing a disturbance power generation model that accounts for the phase misalignment of power output. Independent verification based on measured data in Jilin Province shows that the 95% confidence interval of the power reconstructed only by the first-order advection characteristics can cover 90.2% of the measured fluctuations, and the reconstruction error of the fluctuation standard deviation—an indicator that determines the system reserve demand—is merely 5.9%. This verifies that the macro-cloud displacement is the absolute dominant factor governing the extreme fluctuations of PV clusters. Finally, a normalized Smoothing Factor (SF) characterizing the “reserve capacity release ratio” is constructed, and combined with its statistical indicators, it is used to quantitatively evaluate the smoothing benefits provided by different spatial layout schemes. Under data-constrained conditions, the method proposed in this paper verifies the engineering rationality that microscopic meteorological noise can be safely neglected at the macro-PV cluster scale, providing a reliable quantitative basis for the safe grid expansion and peak-shaving energy storage capacity sizing of high-proportion PV bases. Full article

Historic cities face a dual challenge of managing waterlogging risks while adhering to strict preservation constraints. Traditional drainage upgrades often require extensive excavation, threatening cultural heritage. This study establishes a quantitative assessment framework for the historic urban district of City B using a 1D-2D-coupled hydrodynamic model (InfoWorks ICM). The model was calibrated using continuous monitoring data, achieving a Nash–Sutcliffe Efficiency (NSE) of 0.91. Its spatial accuracy was subsequently validated against historical waterlogging records, showing a strong consistency between simulated flood-prone areas and observed flood locations. We simulated waterlogging distribution under rainfall events with return periods of 0.5 to 5 years. Results reveal two key deficiencies in the current drainage system under a 0.5-year return period storm event. Firstly, 75.3% of the pipe segments are hydraulically overloaded, failing to meet the design standard. Secondly, this widespread network overload contributes to surface waterlogging, with 9.58 ha (1.80% of the total area) being waterlogged. We evaluated three strategies: Low Impact Development (LID), underground storage tanks, and intercepting sewers. A hybrid grey-green infrastructure (HGGI) system was proposed, integrating source reduction and terminal storage. The HGGI system reduced waterlogged areas by 83.58% (0.5-year event) and 64.87% (5-year event), outperforming single measures. Crucially, this hybrid system achieves minimal intervention in historic street patterns through trenchless construction for intercepting sewers, decentralized LID layout and underground storage tanks, avoiding large-scale road excavation while enhancing flood resilience. This study demonstrates that hybrid strategies can effectively balance flood resilience with environmental and cultural preservation in high-density historic districts. Full article

Hybrid renewable energy systems (HRESs), mixing conventional and renewable power sources and occasionally storage units, have become the norm regarding electricity generation. Robust long-term planning of such systems requires stakeholders to test different layouts and system configurations, while their operational management relies on forecasting surpluses and deficits to achieve optimal decision making. However, both tasks, which in fact constitute a flow allocation problem across power networks, are subject to multiple peculiarities, arising from the nonlinear dynamics of the underlying processes, subject to numerous technical and operational constraints. Interestingly, a mutual problem emerges in water resource systems, also comprising network-type storage, abstraction and conveyance components. In this vein, triggered from well-established simulation approaches from the water domain, we introduce a generic (i.e., topology-free) and time-agnostic framework, the key methodological elements of which are: (a) the graph-based representation of the power fluxes; (b) the effective handling of energy uses and constraints through virtual nodes and edges; (c) the implementation of priorities via proper assignment of virtual costs across all graph components; and (d) the configuration of the overall problem as a network linear programming context, which allows the use of exceptionally fast solvers. Specific adjustments are required to address highly complex issues within HRESs, particularly the representation of conventional thermal and pumped-storage hydropower units, as well as the power losses across transmission lines. The modeling approach is stress-tested by means of configuring a hypothetical HRES in a non-interconnected Aegean island, i.e., Sifnos, Greece. Full article

Robinia pseudoacacia is a major tree species for soil and water conservation afforestation in the “Three-North” Region, with crucial ecological improvement and carbon sequestration functions. This study aimed to investigate the dynamics of suitable areas and carbon storage of R. pseudoacacia plantations under different future climate scenarios, further reveal the changing trend of their carbon sequestration potential, and provide a scientific basis for the rational layout and sustainable management of R. pseudoacacia plantations in the “Ω”-shaped bend of the Yellow River. Based on the MaxEnt model, we predicted the potential suitable distribution of R. pseudoacacia under future climate change scenarios, identified the potentially threatened geographical distribution regions and area changes in R. pseudoacacia, and clarified the limiting factors affecting the potential geographical distribution of R. pseudoacacia plantations by analyzing the contribution rates and permutation importance of comprehensive environmental variables. Combined with the InVEST model, we estimated and analyzed the spatial distribution of carbon storage in R. pseudoacacia plantations in the 2090s. The results showed that the minimum temperature of the coldest month was the main environmental factor affecting the distribution of potential suitable areas of R. pseudoacacia plantations, with a contribution rate of 46.98%, followed by annual precipitation. Under current climatic conditions, the potential suitable areas of R. pseudoacacia plantations were mainly distributed in the Loess Plateau, Hetao Plain, Ordos Plateau, Kubuqi Desert, and northern Mu Us Sandy Land. The highly suitable areas were mainly concentrated in the south-central part of the Loess Plateau, accounting for approximately 22.81% of the total area of the “Ω”-shaped bend of the Yellow River. Under future climate change, the moderately and highly suitable areas tended to shift northwestward. Under the four future climate scenarios, the carbon storage and carbon density of R. pseudoacacia plantations showed a trend of first increasing and then decreasing; by 2100, the carbon storage reached the maximum under the SSP370 scenario, and the areas with medium-to-high carbon storage first expanded and then contracted, mainly concentrated in the Ordos Plateau and Loess Plateau. Full article

CO₂-assisted steam huff and puff is an effective method to improve oil recovery and store CO₂ in heavy oil reservoirs. However, few studies focused on complex geological formations, such as bottom water. The bottom water condition not only complicates the process of oil production and CO₂ sequestration, but also makes migration and distribution of oil, water and CO₂ unclear. In this paper, a numerical geological model of an offshore heavy oil reservoir with bottom water is established to analyze the influence of bottom water on injection and production parameters, oil recovery and CO₂ storage capability under vertical and horizontal well layouts. The results show that the bottom water could maintain the formation pressure, but reduce the steam chamber radius and heavy oil utilization area, increase water production and decrease the oil–water ratio. CO₂ could enhance oil recovery in the bottom water reservoir. Oil development indicators of the horizontal well are higher than the vertical well. Meanwhile, CO₂-assisted steam huff and puff use in the bottom water reservoir can create a high-pressure and -temperature environment to make CO₂ supercritical, as it has better CO₂ storage capability and efficiency. The CO₂ storage efficiency of the horizontal well is 63% larger than the vertical well. Thus, the horizontal well layout should be used as a priority if bottom water presents. Conducted analysis of bottom water formation sensitivity parameters shows that the advantageous formation conditions are high oil saturation, porosity of 0.2–0.4 and permeability of 2000–3000 mD. The influence degrees of each formation parameter were evaluated as well. Full article

Temperature and humidity monitoring in grain-carton warehousing is essential for quality assurance, yet fixed wiring is difficult under frequent stacking and battery-powered tags require routine maintenance. This study proposes a distributed passive monitoring sensing system that combines high-efficiency light energy harvesting with low-power long-range wide-area network (LoRa) communication. The key novelty is a carton-oriented separated architecture: an external photovoltaic harvester is wired to internal sensing/communication modules, mitigating stack-induced shading and enabling reliable operation for sensors embedded inside densely stacked cartons; an occlusion-tolerant multi-tag reporting strategy is further adopted. The tag integrates (i) an energy management module based on the bq25570 with a monocrystalline light cell and energy storage for low-light/intermittent illumination, (ii) a LoRa transceiver optimized for long-range and occlusion-tolerant data delivery, and (iii) a temperature–humidity sensing module for reliable microenvironment measurements. A hardware layout with an external photovoltaic panel and internal core modules mitigates carton-induced shading, while low-power scheduling and a lightweight protocol ensure robust sensing and transmission. Experiments show that the energy management module achieves > 60% charging efficiency at a 1.3 V input. After penetrating three layers of grain cartons, the LoRa link maintains a stable range of 500–800 m with ≤1% packet loss under concurrent multi-tag transmission. The measurement errors are within ±1 °C and ±3% relative humidity (RH) in the experimental setup. The proposed system eliminates fixed bus wiring and routine battery replacement, offering a scalable solution that enables maintenance-free monitoring in densely stacked warehousing environments. Full article

In this study, we present the design and development of a mobile, multi-modal electroencephalography and functional near-infrared spectroscopy (EEG/fNIRS) device for wireless neurophysiological monitoring. The system was engineered to achieve high signal fidelity, low power consumption, and a fully untethered operation suitable for ambulatory brain research. The device integrates four Texas Instruments ADS1299 24-bit biopotential amplifiers, providing up to 32 simultaneous acquisition channels. Signal control, processing, and local storage via an SD card are managed by an STM32H7 microcontroller, while an ESP32-S2 module handles Wi-Fi communication. Dual-wavelength light-emitting diodes and OPT101 photodiodes form the optical front-end, driven by digitally controlled constant-current sources for stable illumination. The design employs galvanic isolation, multi-rail power management, and a four-layer PCB layout to minimise interference between analogue, power, and digital domains. Data are captured by a deterministic, clock-driven STM32 acquisition loop and forwarded to the ESP32, which operates under an RTOS and streams packets over Wi-Fi for collection on a mobile phone or PC using the Lab Streaming Layer (LSL) framework. The STM32H7 architecture was chosen for its capability to support future embedded edge-machine-learning functions, enabling on-device signal quality assessment and artefact rejection. Validation demonstrations include 32-channel synchronised acquisition using the ADS1299 internal test signal, eyes-open/eyes-closed alpha modulation visualised in EEGLAB, a forehead fNIRS breath-hold response with physiological spectral content, and real-time ECG/optical pulse streaming via LSL. The resulting system provides a compact platform with explicitly defined acquisition and data interfaces for synchronised EEG/fNIRS acquisition, enabling scalable, low-cost mobile neuroimaging research. Full article

Under the backdrop of the “dual carbon” strategy, leveraging renewable energy to promote low-carbon renovations of existing buildings has become an important path for the construction industry to achieve sustainable development. Currently, to achieve efficient utilization of renewable energy in buildings, key issues such as energy type matching, optimization of energy storage system configuration, and multi-objective collaborative decision-making need to be addressed. This paper explores the adaptation mechanisms between building characteristics, such as layout, climate impact, and energy distribution, and different energy systems, highlighting the core role of optimizing energy storage technology in achieving flexible energy use and dynamic regulation. Combined with artificial intelligence algorithms and multi-objective optimization models, it supports the real-time trade-off and optimization of the system’s operational efficiency, economic performance, and environmental benefits. This review aims to provide theoretical and practical references for enhancing the overall energy efficiency of buildings and promoting the scientific planning and refined operation of renewable energy in sustainable building practices. Full article

Taking a metal structural part blanking workshop as the application background, this study addresses the challenges of high material variety, long crane feeding travel caused by heterogeneous line-side storage layouts, and frequent machine stoppages due to the limited feeding capacity of a single overhead crane. To this end, an integrated machine–crane dual-resource scheduling model is developed by explicitly considering line-side storage locations. The objective is to minimize the maximum waiting time among all machine tools. Under constraints of material assignment, processing sequence, and the crane’s single-task execution and travel requirements, the storage positions of materials in line-side buffers are jointly optimized. To solve the problem, a genetic algorithm with fitness-value-based crossover is proposed, and a simulated-annealing acceptance criterion is embedded to suppress premature convergence and enhance the ability to escape local optima. Comparative experiments on randomly generated instances show that the proposed algorithm can significantly reduce the maximum waiting time and yield more stable results for medium- and large-scale cases. Furthermore, a simulation based on real production data from an industrial enterprise verifies that, under limited feeding capacity, the proposed method effectively shortens material-waiting time, improves equipment utilization, and enhances production efficiency, demonstrating its effectiveness. Full article

In traditional industry setups, Automated Guided Vehicles (AGVs) follow trajectories planned together with the layout of the storage or production facility and supported by fixed markers on the floor or on the walls. Traffic rules manage the avoidance of multiple vehicles, while fleet management gets movement and transportation commands completed as soon as possible. In contrast, recent developments in navigation and advanced computing, sensor, and communication capabilities make their free movement safe and manageable. Detailed route planning and scheduling can guarantee that the vehicles keep a safe distance in time and space. A recent challenge of electric AGVs is that their charging may take several hours, which must be factored into their schedule. This has made minimal energy demand a key objective alongside earliest delivery and strictly meeting the deadlines. This paper presents a method for detailed routing and scheduling of AGV fleets to minimize energy consumption while considering battery levels and charging times. The optimization method is illustrated by a case study where multiple delivery tasks are performed by synchronized movement of vehicles on a complex warehouse layout. In the optimal solution, the scheduled waiting times for collision avoidance are utilized by the vehicles to pre-charge their batteries. Full article

The problem of the post-harvest loss of perishable products has been a loss facing food security, especially in areas that lack adequate cold chain facilities. This issue is directly connected with sustainability objectives because post-harvest losses are the major source of food wastage, unneeded energy use, and related greenhouse gas emissions. Cold storage with phase-change material (PCM) is a promising alternative, as it aims at stabilizing temperatures and enhancing energy consumption, but current analyses of performance have been conducted through experimental testing and computational fluid dynamic (CFD) simulations, which are precise but computationally expensive. To handle this drawback, the current work constructs a machine learning predictive model to predict the dynamics of charging and discharging temperature of PCM cold storage systems. Four regression models, namely Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and K-Nearest Neighbors (KNNs), were trained and tested on experimental datasets that were obtained for varying storage layouts. The various error and accuracy measures used to determine model performance comprised MSE, MAE, R², MAPE, and percentage accuracy. The findings suggest that Random Forest provides the best accuracy during both the charging and the discharging process, with the highest R² values of over 0.98 and with minimal mean absolute errors. The KNN model was competitive in the discharge process, especially in cases of consistent thermal recovery patterns, and XGBoost was consistent in layout accuracy. However, SVR had relatively lower robustness, particularly when using nonlinear charged dynamics. Among the evaluated models, the Random Forest algorithm demonstrated the highest predictive accuracy, achieving coefficients of determination (R²) exceeding 0.98 for both charging and discharging processes, with mean absolute errors below 0.6 °C during charging and 0.3 °C during discharging. This paper has proven that machine learning is an efficient surrogate to CFD and experimental-only methods and can be used to predict the thermal behavior of PCM quickly and precisely. The proposed framework will allow for developing cold storage systems based on energy efficiency, low costs, and sustainability, especially in the context of decentralized and resource-limited agricultural supply chains, with the help of quick and data-focused forecasting of PCM thermal behavior. Full article