Search Results (3,682)

This study explores the selection of a heat recovery system for cogeneration units based on gas engines supplying the district heating system in Opole in order to enhance the efficiency and sustainability of the system. The proposed modifications focus on utilizing low-temperature (LT) waste heat from engine cooling circuits and improving exhaust heat recovery. The research examines retrofitting three cogeneration engines (total thermal capacity of 7.6 MW) by integrating water-to-water heat pumps to upgrade low-temperature waste heat (55–45 °C up to 700 kW), enhancing heat supply to the district heating network. Additionally, a second stage of economizers is evaluated to maximize condensation-based exhaust heat recovery from the existing 95–135 °C system. These system modifications increase the overall thermal capacity up to 9–9.1 MW. To maintain heat supply during cogeneration unit shutdowns (due to failures or electricity price fluctuations), an auxiliary air-to-water cascade heat pump provides an additional 0.8–1 MW. With increasing electricity price volatility, these system modifications provide crucial operational flexibility. Computational simulations confirm that the hybrid configuration successfully upgrades waste heat while strictly maintaining the existing engine return water safety limit. The evaluation demonstrates high economic profitability alongside stable emission reductions. This research presents a case study in optimizing heat recovery in cogeneration-based district heating networks, demonstrating practical and scalable applications for sustainable energy systems. Full article

To tackle the intermittency of renewable energy and realize the repurposing of abandoned oil and gas wells, this study proposes a compressed air energy storage (CAES)-based cogeneration system integrated with geothermal energy and abandoned oil and gas wells, and conducts a five-dimensional comprehensive analysis covering exergy, exergoeconomic, exergoenvironmental, economic and environmental performance. The optimal operating parameters are determined as air compressed to 200 bar, an ORC turbine inlet pressure of 16 bar and an inlet temperature of 110 °C. The system’s annual total power generation is 2,971,416.5 kWh during low-power daytime operation, and 20,131,785 kWh during high-power nighttime operation. Compared with conventional CAES systems, the proposed system reduces total exergy destruction by 4121.35 kW and increases exergy efficiency from 48.49% to 63.38%. Coolers, geothermal heat exchangers and compressors are the main sources of exergy destruction cost and capital investment, while COM1, HE1 and HOT1 are the key components causing environmental impacts. The system realizes cogeneration of power, hydrogen and pure water, with a static payback period of about 5.4 years and significantly reduced TEWI value at elevated turbine inlet pressure. This system achieves multi-objective synergies in energy efficiency, economy and environment, providing a feasible scheme for the green repurposing of abandoned oil and gas wells and cascaded utilization of renewable energy. Full article

Coccocypselum lanceolatum is a tropical, perennial, creeping, herbaceous C3 plant species that is found in deeply shaded humid forests. This species has potential for medicinal and culinary uses. Knowledge about this species and other herbaceous Rubiaceae is confined to phytocoenological and morpho-anatomical studies. Here, it was hypothesized that (1) leaf gas exchange dynamics over a one-year period in C. lanceolatum are related to light conditions, phenology and environmental seasonal changes; (2) photosynthetic performance is focused on enhanced carbon gains through a high leaf net assimilation rate (A_net) relative to light availability, a low dark respiration rate (R_d) and a light compensation point (LCP); and (3) these parameters will vary over leaf age. The photosynthetic photon flux density (PPFD), characterizing the growth and development of C. lanceolatum, was reduced to 4–11% of incoming light in the open area, while the red-to-far-red light ratio (R:FR) was reduced from 1.15 to mean diurnal values of 0.45–0.81, depending on forest canopy dynamics. Leaf gas exchange parameters [A_net, stomatal conductance (g_s), leaf transpiration (E), and intrinsic water use efficiency (iWUE)] were observed over a one-year period. A_net, g_s, and E were correlated with energy factors (PPFD and air temperature) during vegetative growth, while only iWUE showed a correlation with leaf gas exchange parameters during blooming and fruiting, indicating that seasonality and phenology were additional drivers of leaf gas exchange. As a deep-shade forest species, C. lanceolatum displayed low iWUE (3–21 μmol m⁻² s⁻¹) and was adapted to maximize carbon gain and prioritize high g_s rather than water economy. The extremely low LCP (4.2 μmol m⁻² s⁻¹), low R_d (0.2 to 0.43 μmol m⁻² s⁻¹), maximum net photosynthesis (A_max, 5 μmol m⁻² s⁻¹), and apparent quantum efficiency of CO₂ assimilation (Φ of 0.04 µmol µmol⁻¹) were adaptational traits of this species for low light. Finally, the A_net, g_s, E, iWUE, gross photosynthesis under light saturation, R_d, LCP, and light saturation point values were different when comparing young and adult leaves. The ecophysiological responses over a one-year period shown here could assist in the success of C. lanceolatum as a sustainable soil-cover plant in shaded areas. Full article

Recent advances in ozone-assisted combustion for both compression ignition and spark ignition engines are discussed. Ozone, which can be produced by an electrical discharge in air or pure oxygen, decomposes at high temperature to yield highly oxidizing oxygen atoms, which enhance fuel/air reactions. As a result, flames are faster and more stable, ignition is enhanced and low-temperature chemistry is promoted. In the literature, the beneficial influence of ozone on standard and innovative fuels, including low-carbon (syngas) and zero-carbon (hydrogen and ammonia) fuels, has been assessed. In addition, the impact of ozone seeding on new combustion strategies has also been discussed. Ozone enables better control of ignition in Homogeneous Charge Compression Ignition (HCCI) engines, and improves the combustion stability in low-load Gasoline Compression Ignition (GCI) engines, as well as in lean Spark-Assisted Compression Ignition (SACI) and spark ignition (SI) engines, thus broadening the operating range of these engines. Full article

Rather than cooling an entire facility, local area cooling—achieved by placing simple-shaped heat exchangers near plants—could be an effective, low-energy climate regulation strategy for agricultural fields in greenhouses. In this study, a comparative analysis was performed for three serpentine-shaped heat exchangers varying in pipe diameter (12.7 and 15.88 mm) and pipe spacing (50 and 100 mm). Their heat transfer performance and air temperature distribution were measured in terms of local area cooling. Local air temperatures below the heat exchanger were also measured, whereas temperatures above the unit served as a reference. Both heat transfer performance and the pressure drop in the heat exchangers were investigated as well. Cooling experiments were conducted with inlet fluid temperatures from −5 to 10 °C and flow rates from 0.3 to 3.0 L/min (Re = 50–1394). The results showed that local air temperature reductions reached approximately 9 °C for the 12.7 mm pipe with 50 mm spacing, 10 °C for the 15.88 mm pipe with 50 mm spacing, and 5 °C for the 15.88 mm pipe with 100 mm spacing. Heat flux for the 15.88 mm pipe was two-thirds lower at a spacing of 50 mm and 1.5 times higher at a spacing of 100 mm compared to the pipes smaller in diameter. Moreover, pressure drops for the large-diameter pipes were about half those of the smaller pipes. The results from this experimental study are expected to contribute to practical greenhouse cooling applications and provide useful guidance for configuration selection for heat exchangers. Full article

The mixing layer height (MLH) is a key meteorological parameter governing the dispersion and accumulation of air pollutants in the atmospheric boundary layer. Over the Zagreb urban area, low MLH values are associated with stable atmospheric conditions and temperature inversions that inhibit vertical air mixing and promote pollutant accumulation near the ground. This study investigates the influence of MLH on concentrations of particulate matter with an aerodynamic diameter smaller than 10 µm (PM₁₀) and particle-bound polycyclic aromatic hydrocarbons (PAHs) using a five-year dataset of radiosonde measurements and air quality observations. Daily MLH values at 12:00 UTC were compared with simultaneous measurements of PM₁₀ and PAH concentrations. Inversion conditions (MLH < 1000 m) occurred on 44% of days, with approximately 80% of events recorded during the cold season. Elevated PM₁₀ and PAH concentrations were strongly associated with these stable conditions. Of 69 daily exceedances of the PM₁₀ limit value (50 µg m⁻³), 62 occurred during inversion events. Statistical tests confirmed significantly higher concentrations during stable atmospheric conditions (p < 0.05), with further increases observed during prolonged inversion periods. Empirical relationships between MLH and pollutant concentrations were developed and may support future machine-learning-based air quality forecasting. The results highlight atmospheric stability as a critical factor for urban air quality management. Full article

Microalgae are photosynthetic organisms that produce bioproducts of commercial interest and are efficient sequestering CO₂. The monitoring and control processes are areas for improvement to increase the efficiency of its production. There are sensor options for monitoring microalgae cultures, but the vast majority rely on microcontrollers, often lacking the robustness required for applications in more demanding conditions. Also, commercial systems with industrial capabilities can fit the above purpose, but they require licensing and are expensive. Therefore, this work presents the technical details of developing an open-source platform to monitor environmental variables using Modbus industrial sensors and Python used to control the photoperiod and for measuring pH, dissolved oxygen, electrical conductivity, water and air temperatures, photosynthetic photon flux density, irradiance, and turbidity in three photobioreactors containing the microalgae Chlorella vulgaris. The resulting time series showed that the platform preserved data and had a low outlier rate. pH measurements showed that during photosynthesis, the microalgae used CO₂ as their carbon source. Dissolved oxygen and culture medium temperature had an almost perfect Pearson’s anticorrelation with air-sparging. However, with aeration interruption, the correlation was $-0.804$, because dissolved oxygen depends on illumination, aeration, temperature, and biomass quantity, as shown in the time series. Full article

We report a low-temperature plasma–liquid synthesis of crystalline β-Ga₂O₃ nanoparticles directly from aqueous solution. Pulsed discharge plasma bubbles generate reactive species that drive in situ dehydration and crystallization, bypassing the high-temperature calcination required by conventional methods. By varying the carrier gas, we tune morphology from uniform nanorice structures (He, Ar, and N₂) to amorphous microspheres (O₂ and air), revealing how plasma composition governs interfacial hydroxyl radical chemistry and growth kinetics. This approach demonstrates that localized plasma heating and reactive-species flux can achieve phase-selective oxide crystallization under ambient conditions, establishing plasma bubble reactors as a broadly applicable, low-temperature route for direct aqueous synthesis of crystalline wide-bandgap oxides that bridge solution chemistry and plasma nanomaterials design. Full article

The growing share of intermittent renewable electricity has increased the need for long-duration storage in industrial energy systems. Meanwhile, many industrial processes still release recoverable low-grade waste heat. Introducing this heat into pumped thermal energy storage (PTES) can improve thermal integration, but industrial waste heat is often unsteady, and its temperature and mass flow fluctuations may disturb the charging process. This study investigates an air-based reverse Brayton PTES system assisted by an industrial hot-water waste heat stream of approximately 100 °C. A dynamic model was developed in Simulink/Simscape. The shaft speed is fixed at 3000 rpm, and a PID controller regulates the molten-salt flow rate to maintain the thermal storage temperature. The results show that increasing the waste heat temperature from 95 °C to 105 °C mainly changes the charging-side heat distribution. The waste heat utilization power increases from 36.0 MW to 37.9 MW, while the regenerator power decreases from 126.8 MW to 122.0 MW. The thermal storage power increases slightly from 117.0 MW to 119.0 MW, with the mechanical input fixed at 81.0 MW. The influence of waste heat temperature is concentrated near the low-temperature heat exchanger, regenerator, and turbine outlet. Under dynamic disturbances, faster temperature ramps increase short-term deviations, but the PID-based molten-salt flow regulation keeps the storage temperature close to 550 °C, indicating that the proposed control strategy can suppress moderate thermal disturbances during charging. When waste heat temperature and mass flow rate vary together, same-direction changes strengthen the disturbance, whereas opposite-direction changes partly offset it. These results clarify the disturbance propagation mechanism of fluctuating industrial waste heat in the PTES charging loop and provide a basis for the dynamic design and temperature-control strategy of waste-heat-assisted PTES systems. Full article

The automotive panoramic roof exhibits a large-size and thin-wall geometry, with a length-to-thickness ratio approaching the thousand level. This geometric feature makes its forming quality highly sensitive to forming conditions. During the glass molding process, variations in temperature evolution, loading, and cooling parameters may lead to residual stress accumulation and springback deformation, thereby affecting dimensional accuracy and final forming quality. In this study, a full-process finite element model was established and combined with an L16(4^5) orthogonal design to investigate the effects of five key process parameters—heating temperature, holding time, quenching air velocity, quenching air pressure, and quenching time—on the mean residual stress and mean springback displacement in the glass molding process (GMP). The results showed that, within the given parameter ranges, heating temperature, holding time, and quenching time had relatively pronounced effects on the mean residual stress; the mean residual stress was relatively low when the heating temperature was 680 °C, the holding time was 3 s, and the quenching time was 12 s. Heating temperature, quenching air velocity, and quenching time had relatively pronounced effects on the mean springback displacement; the mean springback displacement was relatively low when the heating temperature was 677.5 °C, the quenching air velocity was 13 m/s, and the quenching time was 10 s. Based on the orthogonal analysis, regression models for the mean residual stress and mean springback displacement were further developed, and parameter combinations were screened using the NSGA-III method. Experimental validation showed that the relative error of the mean residual stress was controlled within 15%, indicating that the established model could, to some extent, capture the relationship between process parameters and forming quality indicators, thereby providing guidance for precision forming and process optimization of large-scale thin-walled automotive panoramic roofs. Full article

The northward shift in climate zones and the urban heat island effect demand passive cooling for building roofs in northern regions. Artificial turf is a lightweight candidate, but existing studies treat it as homogeneous material, overlooking blade morphology and roof-scale thermal performance. This study conducted a scaled indoor experiment using a 1 m³ building model. Three artificial turfs with different blade lengths (Type A long, Type B medium, Type C short) were compared against concrete and XPS roofs under simulated summer solar radiation. Results show that blade morphology governs thermal performance. Type A exhibited the lowest peak surface temperature (48.9 °C vs. 53.4 °C and 60.6 °C), and its interface temperature (37.0 °C) was 15.1–19.0 °C lower than Types B and C, attributed to a static air insulation layer and enhanced convection. Its cooling rate (0.98 °C/min) was 1.69–2.33 times faster. Compared to concrete and XPS, Type A had lower surface temperature, less downward heat conduction, and a 29.3 °C drop in 30 min (concrete: 22.3 °C; XPS: 21.7 °C), showing urban heat island mitigation potential. Its heat flux reduction ratio reached 42.9%, with equivalent thermal resistance of ~0.40 m²·K/W, reducing summer peak indoor temperature by 3–6 °C in aging buildings. Double-layer stacking underperformed a single long-blade layer due to heat accumulation. Optimised long-blade turf challenges the view that low albedo inevitably causes high temperature, offering dual benefits of insulation and rapid dissipation for passive cooling in urban renewal. Full article

Stable control of the main combustion chamber temperature is critical for pollutant emission compliance, energy recovery, and equipment longevity in municipal solid waste incineration (MSWI). However, the response delays from manipulated variables such as primary air, secondary air, and feed rate to the furnace temperature span from seconds to tens of minutes, and a uniform-delay assumption is inadequate to characterize the true response lag. Moreover, without an action-smoothing constraint, optimizers tend to produce abrupt control commands that destabilize the temperature trajectory. Using real industrial distributed control system (DCS) data from a full-scale grate furnace, this paper develops a prediction–decision collaborative control framework. In the prediction module, mutual information (MI) is used to identify the optimal delay of each manipulated variable separately, and the time-aligned manipulated variables together with a low-order autoregressive component serve as input to XGBoost and yield a prediction RMSE of 6.85 °C with an $R^2$ of 0.9845. In the decision module, a normalized smoothing penalty is incorporated into the fitness function of particle swarm optimization (PSO) to constrain the step-to-step variation in manipulated variables. Offline predictor-in-the-loop simulation on the test set shows that, compared with a multi-loop PID controller, the proposed method reduces the standard deviation of the furnace temperature tracking error by about 35% (from 5.80 °C to 3.80 °C), and lowers the mean tracking error to 3.65 °C while improving actuator smoothness over both unconstrained PSO and a genetic algorithm. The framework provides a collaborative-control design for pre-deployment evaluation of data-driven controllers in MSWI operation. Full article

In this work, Direct Numerical Simulation (DNS) investigates the combustion behaviour of a reactive transverse lean premixed jet of an ammonia blend (10% NH₃, 11% H₂, 16% O₂ and 63% N₂ by volume) injected through a rectangular nozzle in a pre-heated non-vitiated air crossflow at a pressure of 5 bar. The configuration has been chosen from a Reynolds-Averaged Navier–Stokes (RANS) test campaign to ensure low NO and low unburned fuel, while maintaining a high temperature profile at the turbine inlet. The DNS shows that the flame stabilises on the leeward side of the rectangular jet, within and downstream of the recirculation region, while high scalar dissipation and short residence times prevent persistent anchoring on the windward side. Joint statistics reveal that the reaction does not follow a constant equivalence ratio path, since intermediate progress states are shifted towards leaner mixtures by entrainment, dilution and differential diffusion. The strongest heat-release and displacement-speed events occur in localised regions where mixture state, stretch and flame-front geometry act jointly. The displacement-speed budget is mainly controlled by the chemical source term, with diffusion reducing the net propagation speed and stratification-induced cross terms remaining small. Under intense stretch, positively curved flame elements exhibit larger displacement speeds, indicating a coupled effect of curvature, preferential diffusion and local radical transport. NO formation is dominated by fuel-nitrogen chemistry: HNO and NH₂ are the main NO-producing routes, whereas N₂ and N₂O provide the dominant NO-sink channels. The DNS predicts an outlet-averaged NO level of 400 dppm, while extended-domain RANS calculations indicate that longer residence times could reduce it below 100 dppm. Full article

While double-layer insulation structures are widely adopted, their thermal performance is critically dependent on the thermophysical behavior of the interstitial air cavity, a variable often oversimplified in current design practices. This article moves beyond generic material descriptions to investigate the specific mechanism of heat transfer transition within sealed air gaps sandwiched between graphite polystyrene boards. The innovation of this experiment lies in the rigorous isolation of air gap thickness as the primary independent variable within a 1 × 1 × 1 m closed building model, instrumented with high-precision GPRS temperature and humidity sensors to capture real-time thermal gradients under the authentic climate conditions of Anyang, Henan. The results demonstrate a non-monotonic relationship between gap thickness and effective thermal resistance, governed by the competition between molecular conduction and buoyancy-driven natural convection. Specifically, the data validates that a 20 mm air gap represents the statistically significant optimum, thereby maximizing insulation efficiency while minimizing radiative heat loss. Using this optimized structure reduces steady-state heat flux compared to monolithic equivalents and aligns with the energy conservation target. Unlike previous studies limited by simulation assumptions or short-term testing, this research provides empirically verified, long-term field data that bridges the gap between theoretical fluid dynamics and practical building envelope engineering. These findings offer a robust, physics-based reference for optimizing double-layer insulation systems in the Central Plains, directly supporting the low-carbon retrofitting of existing building stocks. Full article

Black tea quality is governed by aroma chemistry: terpene alcohols (linalool, geraniol, nerolidol), methyl salicylate, and short-chain aldehydes whose abundance and release kinetics from the polyphenol-rich leaf matrix shape perceived grade. Grade information lies not only in the average headspace concentration but in the temporal shape of volatile organic compound (VOC) release under controlled heating. Conventional electronic noses obscure this signal: they rely on multi-sensor arrays, compress each response into summary statistics, and report accuracy only at the level of individual measurements. Whether a single low-cost metal–oxide–semiconductor (MOS) gas sensor can recover grade-defining aroma chemistry, and whether waveform-level modeling can exploit it, was therefore investigated. A portable electronic nose built around a Bosch BME688 sensor recorded 90 time series, each comprising four directly measured channels (temperature, humidity, pressure, gas sensor resistance) and a derived indoor-air-quality (IAQ) proxy computed from them by the on-chip BSEC library, from 16 commercial Turkish black teas across three quality grades. Two representations were compared on the same data: a feature-based pipeline reducing 25 statistical descriptors to seven principal components for six classifiers (best F1-macro = 0.624, MLP), and a raw-waveform Multi-Scale 1D-CNN with Squeeze–Excitation and temporal self-attention (MS-CNN-Attention). Under product-grouped cross-validation, the deep model reached F1-macro = 0.811 (+30%) and graded 14 of 16 products correctly by majority vote, against 11 of 16 for the MLP, with the largest gain in the medium grade (F1: 0.52 → 0.79), where summary-statistic compression destroys the release-kinetic signal. The contributions are threefold: one programmable MOS sensor operated as a thermal-desorption profiler rather than a sensor array; a direct comparison of feature-based classical learning against raw-waveform deep learning on the same small, non-normally distributed dataset; and a product-level decision-consistency metric suited to batch screening. Pairing a low-cost MOS sensor with waveform-level modeling offers a rapid, non-destructive route to aroma-chemistry-based tea quality screening. Full article