Search Results (485)

Nowadays, the circular economy is driving the construction industry towards greater sustainability for both environmental and financial purposes. One prominent area of research with significant contributions to circular economy is the reuse of steel from decommissioned structures in new construction projects. This approach is deemed far more efficient than ordinary steel recycling, due to the fact that it contributes towards reducing both the cost of the new project and the associated carbon emissions. Along these lines, the feasibility of utilizing steel wind turbine towers (WTTs) as part of a new structure is investigated herein, considering that wind turbines are decommissioned after a nominal life of approximately 25 years due to fatigue limitations. General principles of structural steel reuse are first presented in a systematic manner, followed by two case studies. Realistic data about the geometry and cross-sections of previous generation models of WTTs were obtained from the Greek Center for Renewable Energy Sources and Savings (CRES), including drawings and photographic material from their demonstrative wind farm in the area of Keratea. A specific wind turbine was selected that is about to exceed its life expectancy and will soon be decommissioned. Two alternative applications for the reuse of the tower were proposed and analyzed, with emphasis on the structural aspects. One deals with the use of parts of the tower as a small-span pedestrian bridge, while the second addresses the transformation of a tower section into a water storage tank. Several decision factors have contributed to the selection of these two reuse scenarios, including, amongst others, the geometric compatibility of the decommissioned wind turbine tower with the proposed applications, engineering intuition about the tower having adequate strength for its new role, the potential to minimize fatigue loads in the reused state, the minimization of cutting and joining processes as much as possible to restrain further CO₂ emissions, reduction in waste material, the societal contribution of the potential reuse applications, etc. The two examples are briefly presented, aiming to demonstrate the concept and feasibility at the preliminary design level, highlighting the potential of decommissioned WTTs to find proper use for their future life. Full article

To investigate the mechanisms by which inlet water velocity and rotational speed affect spray tower performance, computational fluid dynamics (CFD) was employed to analyze key performance indicators, including outlet flow velocity, flow rate, and the ratio of internal to external outlet flow rates. The results show that outlet flow rate is strongly positively correlated with rotational speed, while inlet water velocity demonstrates nonlinear effects on internal flow velocity. Significant parameter interaction exists—the correlation between inlet velocity and outlet velocity varies with rotational speed (R = −0.9831 to 0.5229), and the outlet flow rate ratio shows a strong negative correlation with rotational speed (R = −0.9918). The gray model demonstrated superior robustness with minimal error fluctuations, whereas the partial least squares regression model exhibited significantly increased errors under extreme conditions. This study provides a theoretical foundation and data support for spray tower parameter optimization. Full article

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions. Full article

The Saiss plain in northern Morocco covers an area of 2300 km² and is one of the main agricultural contributors to the national economy. However, climate change and water scarcity reduce the region’s agricultural yields. Conventional methods of estimating evapotranspiration (ET) provide localized results but cannot capture regional-scale variations. This study aims to estimate the spatiotemporal evolution of daily crop ET (olives, fruit trees, cereals, and vegetables) across the Saiss plain. The METRIC model was adapted for the region using Landsat 8 data and was calibrated and validated using in situ flux tower measurements. The methodology employed an energy balance approach to calculate ET as a residual of net radiation, soil heat flux, and sensible heat flux by using hot and cold pixels for calibration. METRIC-ET ranged from 0.1 to 11 mm/day, demonstrating strong agreement with reference ET (R² = 0.76, RMSE = 1, MAE = 0.78) and outperforming MODIS-ET in accuracy and spatial resolution. Olives and fruit trees showed higher ET values compared to vegetables and cereals. The results indicated a significant impact of ET on water availability, with spatiotemporal patterns being influenced by vegetation cover, climate, and water resources. This study could support the development of adaptive agricultural strategies. Full article

Aiming at the defects of the low mass transfer efficiency and large floor space of the traditional ozone process, a cross-tube ozone catalytic oxidation pilot plant was designed and developed. By implementing lateral aeration and a modular series configuration, the gas–liquid mass transfer pathways were optimized, achieving a hydraulic retention time of 25 min and maintaining an ozone dosage of 43 mg/L, which significantly improved the ozone utilization efficiency. During the pilot operation in an industrial park in Suzhou, Anhui Province, the average COD removal efficiency of the device for the actual biochemical tail water (COD 82.5~29.7 mg/L) reached 35.47%, and the effluent concentration was stably lower than 50 mg/L, which meets the stricter discharge standard. The intermediate products in the system were also analyzed by liquid chromatography–mass spectrometry (LC-MS), and the key pollutants were selected for degradation path analysis. Compared to the original tower process in the park, the ozone dosage was reduced by 46%, the reaction residence time was reduced by 60%, and the cost of water treatment was reduced to 0.067 USD, which is both economical and applicable to engineering. This process provides an efficient and low-cost solution for the deep treatment of wastewater in industrial parks, and has a broad engineering application prospect. Full article

This paper establishes an explicable integrated machine learning model for predicting the discharge water quality in a circulating cooling water system of a power plant. The performance differences between three deep learning models, a Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and a Convolutional Neural Network (CNN), and traditional machine learning models, namely eXtreme Gradient Boosting (XGboost) and Support Vector Machine (SVM), were evaluated and compared. The TCN model has high fitting accuracy and low error in predicting ammonia nitrogen, nitrate nitrogen, total nitrogen, chemical oxygen demand (COD), and total phosphorus in the effluent of a circulating cooling tower. Compared to other traditional machine learning models, the TCN has a larger R² (maximum 0.911) and lower Root Mean Square Error (RMSE, minimum 0.158) and Mean Absolute Error (MAE, minimum 0.118), indicating the TCN has better feature extraction and fitting performance. Although the TCN takes additional time, it is generally less than 1 s, enabling the real-time prediction of drainage water quality. The main water quality indices have the greatest causal inference relationship with those of makeup water, followed by the concentration ratio, indicating that concentrations of ammonia nitrogen, nitrate nitrogen, total nitrogen, and COD have a more decisive impact. Shapley Additive Explanations (SHAP) analysis further reveals that the concentration ratio has a weaker decisive impact on circulating cooling water drainage quality. The results of this study facilitate the optimization of industrial water resource management and offer a feasible technical pathway for water resource utilization in power plants. Full article

Solar-induced chlorophyll fluorescence (SIF) is a powerful tool for the estimation of gross primary productivity (GPP), but the relationship between SIF and GPP under drought stress remains incompletely understood. Elucidating the response of the relationship between SIF and GPP to drought stress is essential in order to enhance the precision of GPP estimation in forests. In this study, we obtained SIF in the red (SIF₆₈₇) and far-red (SIF₇₆₀) bands and GPP data from tower flux observations in a Chinese cork oak plantation to explore the response of the diurnal GPP-SIF relationship to drought stress. The plant water stress index (PWSI) was used to quantify drought stress. The results show that drought reduced SIF and GPP, but GPP was more sensitive to drought stress than SIF. The diurnal non-linear relationship of GPP-SIF (R²) decreased with the increase in drought stress, but a significant non-linear correlation remained for GPP-SIF (R²_GPP-SIF₇₆₀ = 0.30, R²_GPP-SIF₆₈₇ = 0.23) under severe drought stress (PWSI_bin: 0.8–0.9). Physiological coupling strengthened the GPP-SIF relationship under drought, while canopy structure effects were negligible. Random forest and path analyses revealed that VPD was the key factor reducing the GPP-SIF correlation during drought. Incorporating VPD into the GPP-SIF relationship improved the GPP estimation accuracy by over 48% under severe drought stress. The red SIF allowed for more accurate GPP estimations than the far-red SIF under drought conditions. Our results offer important perspectives on the GPP-SIF relationship under drought conditions, potentially helping to improve GPP model predictions in the face of climate change. Full article

In the process of collecting operational data for the performance analysis of water-cooled centrifugal chillers, missing values are inevitable due to various factors such as sensor errors, data transmission failures, and failure of the measurement system. When a substantial amount of missing data is present, the reliability of data analysis decreases, leading to potential distortions in the results. To address this issue, it is necessary to either minimize missing occurrences by utilizing high-precision measurement equipment or apply reliable imputation techniques to compensate for missing values. This study focuses on two water-cooled turbo chillers installed in Tower A, Seoul, collecting a total of 118,464 data points over 3 years and 4 months. The dataset includes chilled water inlet and outlet temperatures ($T_1$ and $T_2$) and flow rate (${\dot{V}}_1$) and cooling water inlet and outlet temperatures ($T_3$ and $T_4$) and flow rate (${\dot{V}}_3$), as well as chiller power consumption (${\dot{W}}_c$). To evaluate the performance of various imputation techniques, we introduced missing values at a rate of 10–30% under the assumption of a missing-at-random (MAR) mechanism. Seven different imputation methods—mean, median, linear interpolation, multiple imputation, simple random imputation, k-nearest neighbors (KNN), and the dynamically clustered KNN (DC-KNN)—were applied, and their imputation performance was validated using MAPE and CVRMSE metrics. The DC-KNN method, developed in this study, improves upon conventional KNN imputation by integrating clustering and dynamic weighting mechanisms. The results indicate that DC-KNN achieved the highest predictive performance, with MAPE ranging from 9.74% to 10.30% and CVRMSE ranging from 12.19% to 13.43%. Finally, for the missing data recorded in July 2023, we applied the most effective DC-KNN method to generate imputed values that reflect the characteristics of the studied site, which employs an ice thermal energy storage system. Full article

This investigation attempts to develop a tower solar collector-based system designed for the cogeneration of cooling and desalination. The traditional organic Rankine cycle (ORC) integrated with the ejector refrigeration cycle generates limited power and cooling at a single temperature. Acknowledging their limitations, our present study uses an organic flash cycle (OFC) supported by solar heat combined with the two-phase ejector cycle and the reverse osmosis (RO) desalination unit. Since the OFC turbine is fed with two extra streams of fluid, therefore, it provides greater power to run the compressor of the ejector and pumps of the RO unit, resulting in the production of cooling at two different temperatures (refrigeration and air conditioning) and a higher mass flow rate of fresh water. A mathematical model is employed to assess the impact of coil curvature ratio, Rib height, and direct normal irradiation (DNI) on the temperature of the collector’s oil outlet. ANSYS-FLUENT conducts numerical simulations through computational fluid dynamics (CFD) analysis. The results indicate an ultimate increase in oil outlet temperature of 45% as the DNI increased from 450 to 1000 W/m² at a curvature ratio of 0.095 when employing the 1st Rib. Further, a steady-state energy and exergy analysis is conducted to evaluate the performance of the proposed cogeneration, with different design parameters like DNI, coil curvature ratio, rib height, and OFC turbine inlet pressure. The energetic and exergetic efficiencies of the cogeneration system at DNI of 800 W/m² are obtained as 16.67% and 6.08%, respectively. Exergetic assessment of the overall system shows that 29.57% is the exergy produced as cooling exergy, and the exergy accompanied by freshwater, 68.13%, is the exergy destroyed, and 2.3% is the exergy loss. The solar collector exhibits the maximum exergy destruction, followed by the ejector and RO pumps. Integrating multiple technologies into a system with solar input enhances efficiency, energy sustainability, and environmental benefits. Full article

The building drainage system (BDS) is a critical building component and must be designed to protect public health by maintaining safe and hygienic conditions within the indoor environment. The recent COVID-19 pandemic and the emergence of other wastewater-related issues, such as the spread of anti-microbial resistance (AMR), place the BDS at the centre of the public health agenda. To understand the complex characteristics of the BDS and its performance, the numerical simulation model AIRNET was used to model whole system responses to discharging events. In this study, the model’s effectiveness and accuracy were evaluated through its application in a case study system representative of a real-world tall building. Data reflecting actual conditions were collected using the drainage test rig at the National Lift Tower (NLT) in Northampton. The data show a strong correlation between the measured and modelled air pressures in the system over time and along the drainage stack height. More importantly, a sample dataset representing various ventilation configurations, flow rates, and water usage combinations shows a strong linear relationship between the simulated and measured pressure values. These results confirm the accuracy and reliability of the AIRNET model in modelling the BDS, even when applied to high-rise buildings. This is crucial for addressing drainage challenges in high-rise building design. Full article

The Po Nagar Towers (Thap Ba) complex, an iconic heritage site of Cham culture and a nationally recognized special relic, has stood in Nhatrang, Vietnam, for over a thousand years. We report here a preliminary analysis of original ancient Cham bricks from the Po Nagar Towers using a combination of appropriate characterization techniques, including X-ray fluorescence (XRF), X-ray diffraction (XRD), Raman micro-spectroscopy, thermal dilatometry, compressive strength testing, and water sorption. Mechanical properties and firing temperatures of the ancient bricks have been determined to support the discussion on the likely technology used to make them. Specifically, they were made from clay, sand, plagioclases/feldspar, and grog mixed with intentionally added carbon precursor (charcoal powder), then fired at temperatures between 800 °C and 1000 °C to form lightweight bricks with a mass density of 1.3–1.6 kg/dm³ and an open porosity of 18–25%. The ancient Cham bricks have their texture and porosity to meet the requirements of the thin rubbing joint technique in tower construction and to contribute to the carvability and durability of Cham towers. A comparison is made with the bricks for tower restoration during the 2000s. Full article

When traditional HVAC (heating, ventilation, and air-conditioning) systems are in operation, they often run according to the designed operating conditions. In fact, they operate under part-load conditions for more than 90% of the time, resulting in energy waste. Therefore, studying the optimization and regulation of their operating conditions during operation is necessary. Given that the control set point for cooling tower outlet water temperature differentially impacts chiller and cooling tower energy consumption during system operation, optimization of this parameter becomes essential. Therefore, this study focuses on optimizing the cooling tower outlet water temperature control point in central air-conditioning systems. We propose the Hippopotamus Optimization Algorithm (HOA), a novel population-based approach, to optimize cooling tower outlet water temperature control points for energy consumption minimization. This optimization is achieved through a coupled computational methodology integrating building envelope dynamics with central air-conditioning system performance. The energy consumption of the cooling tower was analyzed for varying outlet water temperature set points, and the differences between three control strategies were compared. The results showed that the HOA strategy successfully identifies an optimized control set point, achieving the lowest combined energy consumption for both the chiller and cooling tower. The performance of HOA is better compared to other algorithms in the optimization process. The optimized fitness value is minimal, and the function converges after five iterations and completes the optimization in a single time step when run in MATLAB in only 1.96 s. Compared to conventional non-optimized operating conditions, the HOA strategy yields significant energy savings: peak daily savings reach 4.5%, with an average total daily energy reduction of 3.2%. In conclusion, this paper takes full account of the mutual coupling between the building and the air-conditioning system, providing a feasible method for the simulation and optimization of the building air-conditioning system. Full article

More than half of the cultivated land belongs to the Irrigation Association. Therefore, there have been no farmland consolidation, irrigation, and drainage projects. The cultivation in the non-irrigation area suffers from poor geographical conditions and a lack of water sources. A practical planning strategy is required for expanding irrigation services. The mountainous area of Nantou County, Taiwan, has 7477 ha of available land and 4656 ha of agricultural land outside the irrigation area. Rain and streams are the main water source. There are 82 ponds, 80% of which belong to the loam soil, and the rainfall from October to February is limited. The water requirement of crops is 1.5–3.1 mm/day. Wild streams, groundwater, and rainwater are the only potential water sources due to elevation and terrain. The potential runoff is estimated to be 0–0.927 cms (m³/s) when using the SCS-CN method. Water supply and demand from October to April are limited, and the rainfall comprises 22% of the total water supply. Large reservoirs and water storage towers are required for flooding and in dry seasons. To address water storage challenges and stabilize the balance between water supply and demand, it is essential to construct additional ponds. Full article

The Tibetan Plateau (TP) serves as a vital ecological safeguard and water conservation region in China. In recent decades, substantial efforts have been made to promote vegetation greening across the TP; however, these interventions have added complexity to the local water balance and evapotranspiration (ET) processes. To investigate these dynamics, we apply the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model to simulate ET components in the TP. Through model sensitivity experiments, we isolate the contribution of vegetation greening to ET variations. Furthermore, we analyze the role of climatic drivers on ET using a suite of statistical techniques. Based on satellite and climate data from 1982 to 2018, we found the following: (1) The PT-JPL model successfully captured ET trends over the TP, revealing increasing trends in total ET, canopy transpiration, interception loss, and soil evaporation at rates of 0.06, 0.39, 0.005, and 0.07 mm/year, respectively. The model’s performance was validated using eddy covariance observations from three flux tower sites, yielding R² values of 0.81–0.86 and RMSEs ranging from 6.31 to 13.20 mm/month. (2) Vegetation greening exerted a significant enhancement on ET, with the mean annual ET under greening scenarios (258.6 ± 120.9 mm) being 2.9% greater than under non-greening scenarios (251.2 ± 157.2 mm) during 1982–2018. (3) Temperature and vapor pressure deficit were the dominant controls on ET, influencing 53.5% and 23% of the region, respectively, as identified consistently by both multiple linear regression and dominant factor analyses. These findings highlight the net influence of vegetation greening and offer valuable guidance for water management and sustainable ecological restoration efforts in the region. Full article

The increasing frequency, intensity, and duration of heatwaves and droughts pose significant societal and environmental challenges across Europe. This study analyzes land surface temperature (LST) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) between 2003 and 2023 to identify thermal anomalies associated with heatwaves. Additionally, this study examines the role of different land cover types in modulating heatwave impacts, employing turbulent flux observations from micrometeorological towers. The interaction between heatwaves and droughts is further explored using the Standardized Precipitation Evapotranspiration Index (SPEI) and soil moisture data, highlighting the amplifying role of water stress through land–atmosphere feedbacks. The results reveal a statistically significant upward trend in LST-derived thermal anomalies, with the 2022 heatwave identified as the most extreme event, when approximately 75% of Europe experienced strong positive anomalies. On average, 91% of heatwave episodes identified in reanalysis-based air temperature records coincided with LST-defined anomaly events, confirming LST as a robust proxy for heatwave detection. Flux tower observations show that, during heatwaves, evergreen coniferous and mixed forests predominantly enhance sensible heat fluxes (mean anomalies during midday of 74 W/m² and 62 W/m², respectively), while grasslands exhibit increased latent heat flux (89 W/m²). Notably, under extreme compound heat–drought conditions, this pattern reverses for grassed sites due to rapid soil moisture depletion. Overall, the findings underscore the combined influence of surface temperature and drought in driving extreme heat events and introduce a novel, multi-source approach that integrates satellite, reanalysis, and ground-based data to assess heatwave dynamics across scales. Full article