Search Results (21,259)

This article presents a technical and economic analysis of a photovoltaic system operating in conjunction with a heat pump in a single-family home. The aim of this study was to compare the cost-effectiveness of two prosumer billing systems currently in use in Poland, net metering and net billing, implemented in accordance with the provisions of the Renewable Energy Sources (RES) Act and the Energy Law and to assess the effectiveness of a proprietary algorithm for managing surplus electricity produced by the photovoltaic system. The energy performance of the facility was determined using ArCADia Termo 11.1 software, while energy and economic calculations were performed using Microsoft Excel 365 and a developed heat pump control algorithm. The algorithm is based on an analysis of the building’s energy balance with a 15 min resolution and utilizes data on outdoor temperature, energy production from the PV system, building heat loss, heat pump operating parameters, and energy self-consumption. Its goal was to maximize the use of energy produced for the building’s own needs by appropriately controlling the heat pump and storing surplus energy as heat stored in domestic hot-water tanks. The annual electricity consumption of the analyzed building was 6902.18 kWh, of which 3724.13 kWh was for heating and domestic hot water provided by the heat pump. The algorithm reduced grid energy consumption by approximately 900 kWh per year and achieved a self-consumption level of 12.73 (%). Full article

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis. Full article

To address the limited cooling range of ventilation in deep, high-temperature development headings and the lack of coordinated design between coil-based cooling and the ventilation system, this study proposes a coupled “twin-forcing–coil” cooling scheme. Building on conventional overlap (forcing–exhausting) ventilation, a rear-mounted second forcing duct is added to the conventional overlap (force–exhaust combined) auxiliary ventilation system, forming a dual-duct forcing, single-exhausting configuration—hereafter termed the “twin-forcing–single-exhausting” (TFSE) system—that provides a booster (relay) air supply to mitigate the along-path attenuation of cooling capacity and the short-circuiting of cold air; an in situ heat-exchange coil wall further provides supplementary cooling where ventilation-based temperature control weakens. Using a development heading at the 790 m level of a metal mine in Yunnan as the engineering background, a three-dimensional numerical model coupling the roadway, ventilation system, and coil wall was established and validated against nine field monitoring points, showing average relative errors of approximately 1% for temperature and 2–3% for humidity, comparable to the measurement uncertainty of the field instrumentation. Because the numerical model does not account for evaporative and condensation phase-change processes, two supplementary development headings with standing water at the face were used for validation; results showed that model error increases with water accumulation and heading length, indicating the model’s applicability is limited to conditions with intact surrounding rock and minimal seepage. Six operating cases were designed with duct placement and coil spacing as variables. Results show that single-duct ventilation cooling decays markedly beyond 30 m from the face, whereas twin-forcing booster (relay) air supply effectively extends the cooling range, reducing the 30–70 m section temperature by 2.7–2.9 K; the second duct should be positioned where the first duct’s cooling capacity begins to attenuate but is not yet depleted. Based on only two spacing configurations tested (10 m and 15 m), coil-staggered spacing showed limited effect on cooling performance under the field conditions examined; this preliminary finding requires validation across a broader range of spacings. Among the chilled-water conditions tested, an inlet temperature of 280.65 K and a flow velocity of 0.5 m/s offered a reasonable trade-off between cooling uniformity and economic efficiency. Under the boundary conditions and equipment parameters of this case, energy consumption estimates further indicate that the cooling effect per unit electricity consumption of twin-forcing ventilation is roughly 6–8 times that of coil-based cooling, primarily due to pumping losses over the ~240 m chilled-water delivery distance. This energy penalty indicates that coil-based cooling is better suited as a localized, short-distance supplementary measure rather than as a means of extending the cooling range over long distances. Full article

The Fuzzy Cognitive Map (FCM) framework provides a useful tool for representing the complex interdependencies involved in wastewater treatment selection, particularly when social, ecological, climatic, and economic criteria are considered simultaneously. In this study, the FCM approach was applied to compare two wastewater treatment approaches in Ahvaz, Iran: constructed wetlands (CWs) as a nature-based solution and energy-based wastewater treatment plants. The developed model included 30 components and 127 causal links, and was used to examine four scenarios representing CWs, energy-based treatment, a hybrid approach, and direct wastewater discharge. The results showed that both CWs and energy-based solutions had similar effects on public health, while the hybrid scenario produced the greatest improvement. CWs had a positive effect on ecosystem restoration and showed better performance in heavy metal removal, whereas energy-based solutions had a greater negative influence on environmental conditions and climate-related components. In addition, the economic results indicated that CWs were more favorable in terms of capital cost, energy consumption, and operational cost. Sensitivity analysis using ±10% variations in causal weights showed that the main scenario-response patterns remained generally unchanged. Overall, the findings suggest that CWs and energy-based systems each have specific advantages and limitations, while the hybrid approach offers the most balanced performance across the evaluated criteria. This study demonstrates the usefulness of the FCM approach for supporting wastewater management decisions and for identifying trade-offs among treatment alternatives in sustainable water resource planning. Full article

Data centers are large and rapidly growing electricity consumers, and cooling systems account for a substantial share of their energy demand. A key contribution of this study is a climate-sensitive, hourly techno-economic comparison of three data-center cooling configurations under consistent operating assumptions: a conventional water-cooled centrifugal chiller baseline, a magnetic bearing chiller (MBC) system, and an MBC system integrated with aquifer thermal energy storage (ATES). The comparison is performed for Phoenix, Arizona, and Fairbanks, Alaska, which represent substantially different cooling climates in the U.S. Hourly simulations use identical information technology (IT) load profiles, identical aggregate installed chiller capacity represented by two 4058 kW chiller units, common water-side economizer controls, and site-specific weather and electricity tariffs. Results show that the MBC system reduces annual cooling-system electricity consumption from 1169.4 to 957.4 MWh in Phoenix (18.1%) and from 361.6 to 319.4 MWh in Fairbanks (11.7%). Peak cooling-system electrical demand decreases by 119.4 kW in Phoenix and 71.6 kW in Fairbanks. Relative to the centrifugal baseline, the MBC case gives a 5.8-year simple payback in Phoenix but is not economically attractive in Fairbanks under the assumed tariff. The MBC-only case gives the lowest annual cooling electricity use in both climates. The MBC+ATES case is treated only as a screening-level, discharge-assisted cold-storage scenario rather than a full techno-economic assessment of seasonal ATES, and no site-specific hydrogeological feasibility assessment is performed. Under the assumed O&M cost structure, MBC+ATES gives a higher discounted value of savings than MBC-only, but this economic result is not caused by additional cooling-electricity savings relative to MBC-only. The MBC+ATES case also has a longer payback period because of its higher capital cost. These results show that the value of advanced cooling configurations depends on climate, free-cooling availability, electricity pricing, storage assumptions, and economic assumptions within the modeling framework considered in this study. Full article

The long-term durability of repair systems combining normal concrete (NC) and ultra-high-performance concrete (UHPC) in marine environments depends on the response of the near-interface composite region to sequential carbonation and seawater exposure. However, the effects of seawater immersion following pre-carbonation remain insufficiently understood. This study compared an unexposed reference (REF), specimens carbonated for 28 d (C28), and specimens carbonated for 28 d and then immersed in simplified artificial seawater for 60 d (C28-SW60) using X-ray diffraction, thermogravimetry, backscattered electron imaging with energy-dispersive X-ray spectroscopy, and mercury intrusion porosimetry. Pre-carbonation promoted portlandite consumption, carbonate formation, and pore refinement. Subsequent seawater immersion further enhanced calcite-related diffraction and carbonate decomposition signals, while no typical crystalline salt-attack product was detected as dominant. The initial Ca-rich-to-Si-rich gradient from the NC side through the overlay transition zone to the UHPC side was accompanied by marked Cl accumulation and further S and Mg enrichment and redistribution. After seawater immersion, the measured total intrusion volume increased from 0.026 to 0.043 mL/g, the volume-based median pore-entry diameter increased from 27.49 to 58.42 nm, and the >1000 nm pore-volume fraction reached 39.82%, a change consistent with a shift toward coarser mercury-accessible pore entries. Together, the results link the initial heterogeneity of the NC–Overlay transition zone (OTZ)–UHPC region to a sequence-dependent response in which carbonate enrichment coexisted with multi-ion redistribution and transport-relevant defects, distinguishing carbonate accumulation from sustained near-interface refinement. Full article

Road traffic surveys require sensing systems that can be deployed rapidly without modifying the road surface or requiring a permanent power connection. This paper presents the design, embedded implementation, and evaluation of a low-power roadside magnetic sensor that performs vehicle-event detection and classification directly at the edge. The sensing node integrates two RM3100 three-axis magnetometers (PNI Sensor, Santa Rosa, CA, USA) with an NXP MK22FN512VLH12 microcontroller (NXP Semiconductors N.V., Eindhoven, The Netherlands) based on a 120 MHz Arm Cortex-M4F core with 512 kB Flash and 128 kB SRAM. Magnetic-field data are acquired at 250 Hz and processed locally using baseline removal, low-pass filtering, signal-energy calculation, and peak-based event detection. Detected magnetic signatures are classified using an integer-quantised one-dimensional convolutional neural network implemented directly on the microcontroller. The model processes four synchronised 512-sample channels representing the three magnetic-field axes and their combined signal energy. Model development was supported by approximately 50,000 annotated events obtained from 36 h of real-world traffic measurements at eight locations. The selected model achieved an overall classification accuracy of 91.1% for the considered operational categories. The implemented network requires 288,128 multiply–accumulate operations per inference, while its quantised weights and biases occupy approximately 23 kB of Flash memory. Complete three-axis event signatures are stored locally for subsequent verification, whereas only the timestamp and predicted vehicle category are transmitted through the wireless interface. Based on the capacity of the applied LiFePO₄ battery and the estimated consumption of the implemented hardware, the expected autonomous operating period is approximately 41 days. The results demonstrate the feasibility of integrating magnetic sensing, embedded signal processing, and Edge AI on a conventional resource-constrained Cortex-M4 platform for non-invasive road traffic monitoring. Full article

Human–robot collaborative assembly line balancing and scheduling constitutes an NP-hard combinatorial optimization problem involving the simultaneous optimization of task assignment, resource allocation, processing mode selection, station-level scheduling, and ergonomic constraints. This study proposes a Q-learning-based hyper-heuristic genetic algorithm (QLHH-GA) to solve the cost-oriented ergonomic mixed-model human–robot collaborative assembly line balancing and scheduling problem. The proposed approach integrates bio-inspired evolutionary mechanisms of population variation and selection with adaptive, Q-learning-guided low-level heuristic selection. The Q-learning layer uses performance feedback to adapt the search strategy to different solution states while maintaining solution feasibility. A mixed-integer linear programming (MILP) model is also developed to minimize the total operating cost, including station opening, labor, robot operation, and energy consumption costs, while enforcing station-level energy expenditure (EE) limits. Computational experiments conducted using benchmark instances of varying sizes and a literature-based industrial case study demonstrate that QLHH-GA produces solutions comparable to those obtained by the MILP model on small-scale instances and maintains strong solution quality on larger instances, for which exact optimization becomes computationally prohibitive. These findings demonstrate the scalability and effectiveness of reinforcement-learning-guided hyper-heuristic search for designing cost-efficient and ergonomically constrained human–robot collaborative assembly lines. Full article

Under ISO 50001, energy performance is monitored through Energy Performance Indicators (EnPIs) and energy baselines. In practice, the energy-to-production ratio (kWh/t) is commonly adopted by default and validated solely by the coefficient of determination ($R^2$), which is insensitive to systematic bias, to the base load contained in the intercept, and to the residual structure that reveals an omitted explanatory variable. This work organizes well-established statistical and engineering checks into a sequential, four-outcome decision procedure anchored to the diagnosis of Significant Energy Uses (SEUs): retain the simple ratio, adopt a regression baseline with the same variable, switch to the SEU-associated variable, or reject the model as structurally misspecified. Relative to common practice, the procedure makes three methodological corrections explicit: in-sample NMBE is identically zero for OLS models with an intercept and is therefore defined out of sample; residual diagnostics are evaluated against exact, design-specific Durbin–Watson critical values with a Šidák-corrected family-wise error of 0.044–0.050 (versus ≈0.14 uncorrected); and the candidate-variable step uses a partial F-test on nested models, since the naive residual-versus-variable regression is attenuated by collinearity with production. The procedure is characterized on synthetic data with known truth ($N=1000$ replicates per cell): against an interannual drift of ≈2%/yr, its sensitivity reaches 1.00 at $n=72$ months while an $R^2$-only criterion has sensitivity 0.00, and with a base-load fraction of ≈0.28 the $R^2$-only rule retains the biased ratio in 100% of the replicates; specificity under a correct ratio is 0.95–0.96, and the adopted thresholds lie in a stable region of the ($R^2$, $f_0$) sensitivity sweep. The procedure is then demonstrated on six industrial cases; most notably, in a fuel oil power plant a pooled baseline with $R^2=0.998$ is rejected (Durbin–Watson $=0.79$ versus an exact critical value of 1.64; runs test $p<0.001$) because of drift in specific fuel consumption that $R^2$ cannot detect, and its out-of-sample validation over 37 rolling origins shows that an aggregated bias of $+0.31\%$ can mask an origin-to-origin drift from $-1.7\%$ to $+2.1\%$. The contribution is not a new indicator or a new statistic, but the integration of indicator selection and multistage statistical validation into a single auditable decision procedure whose operating characteristics are quantified. Full article

Computational fluid dynamics (CFD)-driven optimization of engineering systems is often constrained by high computational cost, particularly when surrogate models must be constructed from limited simulation samples. Although surrogate-assisted multi-objective optimization can substantially reduce CFD evaluations, local prediction errors in decision-sensitive Pareto regions may alter feasibility classification and engineering recommendations near active constraints. To address this issue, this study proposes a Pareto-active-region-guided sequential surrogate modeling framework (PAR-SSM) for multi-objective optimization of liquid-cooled battery thermal management systems. Starting from 15 face-centered central composite design (FCCD) samples, the framework selectively introduces additional high-fidelity CFD evaluations into Pareto-active and constraint-sensitive regions, yielding a 21-sample refined surrogate model. Rather than uniformly improving global prediction accuracy, PAR-SSM directs the limited CFD budget toward regions where surrogate errors can directly influence engineering decisions. After model freezing, three independent Fluent cases were used exclusively for validation, yielding mean absolute deviations of 0.098 °C for maximum temperature and 0.341 °C for temperature difference, while also revealing residual feasibility risk near active constraint boundaries. Application to an autonomous underwater vehicle (AUV) battery module showed that the N = 3 configuration dominated the nominally constrained Pareto set and provided a favorable thermal–hydraulic trade-off under low auxiliary energy consumption. Overall, PAR-SSM provides a decision-oriented strategy for balancing computational cost and optimization credibility in CFD-intensive, constrained multi-objective design. Full article

Understanding building operational carbon dioxide (CO₂) emissions is essential for sustainable urban planning, yet variations in emissions across building types remain poorly characterized. This study integrated top-down and bottom-up approaches to estimate building-scale CO₂ emissions, capturing fine-scale emission patterns while maintaining consistency with aggregate energy statistics. Taking Guiyang as a case study, electricity consumption was simulated using the Designer’s Simulation Tool (DeST), while natural gas (NG) and liquefied petroleum gas (LPG) consumption were disaggregated using an area-proportional allocation method. Emission factors were then applied to estimate monthly CO₂ emissions. Results showed that monthly building CO₂ emissions ranged from 0.81 to 1.09 million tons. Significant spatial disparities were observed, with core districts contributing more than 22% of total emissions, whereas peripheral districts accounted for only approximately 6%. Residential buildings produced the highest total emissions, averaging 433 thousand tons per month, while shopping malls showed the highest emission intensity, reaching 8.08 kg/m² in July. The different building types and different seasons had different emissions, with residential buildings showing higher emissions in winter, whereas hotels and shopping malls experienced higher emissions during summer. The proposed framework extends existing approaches, providing reliable building CO₂ data for urban low-carbon planning and targeted mitigation. Full article

Currently, heating supply in rural areas of China still predominantly relies on conventional coal-fired heating, which suffers from poor thermal insulation performance and severe environmental pollution. To address the issues of energy waste and environmental pollution associated with traditional heating methods in rural China, this study selects a detached rural residential building in Jilin City as the research object. A building thermal load calculation model incorporating phase-change material (PCM) walls and dynamic simulation models for five clean heating coupling systems are developed using TRNSYS software, so as to analyze the influence of PCM placement at different positions within the wall assembly on the building’s thermal load, as well as the technical and economic performance of the five heating systems. The results show that, when PCM is placed on the inner side of the building envelope, the peak heating load is reduced from 15,234.2 W to 11,266.5 W, and the cumulative heating load drops from 33,744.3 kWh to 25,688.9 kWh. Compared with the conventional PV (photovoltaic) system, the PVT (photovoltaic–thermal) system achieves an 11% improvement in power generation efficiency. Among the five clean heating systems, the PVT–ground-source heat pump system exhibits the lowest energy consumption, while the PVT–biomass boiler system records the highest energy consumption. Based on life-cycle cost analysis, the PVT–biomass boiler system delivers the optimal economic performance, with a equivalent annual cost of 9285.48 CNY. Full article

This study introduces a novel class of architected foam structures based on triply periodic minimal surface (TPMS) geometries for passive battery thermal management. Unlike conventional cold plates that rely on active pumping or high-conductivity solid materials, the proposed TPMS-like foams leverage convective fluid transport within a lightweight, electrically insulating polymer matrix. We present the design, fabrication, and experimental characterization of Schwarz Primitive TPMS structures manufactured via injection molding using silicone rubber, which has a comparable quality to additively manufactured polymer cold plates but with significantly lower manufacturing complexity and cost. The TPMS cold plates achieve passive fluid circulation without external pumps, reducing parasitic power consumption while maintaining thermal resistance values of approximately 23.5 K/W. Although its thermal resistance is higher than that of an aluminum plate of the same size (2.7 K/W), the TPMS cold plate is electrically insulating and offers additional safety benefits. Additionally, it can be fabricated from and filled with fire-retardant materials to prevent thermal propagation while maintaining a relatively low temperature gradient. Mechanical compression testing of TPMS foam samples with about 30% solid volume fraction showed a compressive strength of 86.2 kPa at 0.2 strain, equivalent to 30.5% of the compressive modulus of solid silicone (282.6 kPa). Compared to solid silicone plates (thermal resistance is 1420 K/W), the TPMS fluid-filled structures reduce thermal resistance by more than two orders of magnitude. This work establishes design rules, fabrication protocols, and performance benchmarks for TPMS-based passive cooling devices, offering a scalable pathway toward safer, lighter, and more energy-dense battery packs. Full article

Kombucha is a fermented tea beverage produced by a symbiotic culture of bacteria and yeast (SCOBY) containing a complex matrix of organic acids, polyphenols, electrolytes, and live microorganisms. Tea is the world’s most widely consumed beverage after water, and fermentation transforms its sensory properties in ways that have contributed to kombucha’s growing global popularity alongside its perceived health benefits; yet, despite widespread consumption and long-standing traditional use, controlled human data are relatively recent and sparse. This review integrates clinical, mechanistic, sensory, cultural, and public health perspectives to evaluate kombucha as a functional fermented beverage. A randomised, placebo-controlled crossover trial demonstrated that consumption of live kombucha with a high-glycaemic-index meal significantly reduced postprandial glycaemia and insulinaemia, lowering the glycaemic index from 86 to 68 (approximately 20% reduction). A pilot randomised controlled study in adults with type 2 diabetes reported reductions in fasting blood glucose following four weeks of regular kombucha consumption. Most recently, a 10-week RCT in adults with excess body weight demonstrated significant within-group reductions in total cholesterol, LDL-c, VLDL-c, triglycerides, Castelli II index, and uric acid, as well as reduced hydrogen peroxide levels and improved gastrointestinal symptoms, following daily green tea kombucha consumption combined with an energy-restricted diet. Mechanistically, these effects likely arise from synergistic interactions between organic acids, polyphenols, vitamins, minerals, and microbial communities influencing gastric emptying, carbohydrate and lipid digestion, gut microbiota composition, antioxidant defence, and hydration physiology. While promising, current evidence requires confirmation in larger, longer-term trials before definitive clinical recommendations can be made. Full article

Indonesia has significant fisheries potential; however, limited cold chain infrastructure at small-scale fishing ports contributes to post-harvest losses and quality degradation of fishery products. This study presents the design and techno-economic assessment of a modular portable cold storage unit for tuna fisheries integrated with renewable energy systems. The system (7 × 3 × 5 m) uses polyurethane sandwich panels and requires a maximum cooling load of 6.14 kW with peak power consumption of 7.93 kW. The estimated capital cost is approximately USD 34,100, while the operational cost is about USD 198 per cycle. A comparative analysis using the Levelized Cost of Energy (LCOE) method indicates that diesel generators provide the lowest cost at approximately USD 0.56/kWh, whereas standalone photovoltaic (PV) systems exhibit the highest cost at around USD 0.89/kWh. However, hybrid PV systems offer the best balance between cost efficiency and environmental performance by reducing carbon emissions. The results demonstrate that integrating hybrid renewable energy into modular cold storage enhances cold chain reliability, reduces fish losses, and supports sustainable coastal development. This approach contributes to low-carbon fisheries infrastructure and aligns with global sustainability and renewable energy transition goals. Full article