Search Results (7,823)

This article applies the exergy analysis to the production and use of sugarcane, considering a model published in the literature. In this way, we compute incident solar irradiation, carbohydrate production, water consumption, and the production of stalks and straws. Following the production estimate, we analyze a biorefinery production cycle, from solar irradiation to the biorefinery products on an exergy basis, from birth to production of sugar, electrical energy, and ethanol. The calculated sugarcane production values are 80.7 tons per hectare for a 52-week cycle. As a result, the average exergy efficiency of sugarcane is 4.99%, reaching peaks of 8.3%. When considering only the useful exergy generated in the production of stalks and straw, an annual yield of 17.86 kWh/m² represents an overall exergy efficiency of 1.31%. Considering the energy conversion processes in the biorefinery, the exergy efficiency from the radiation to the products from the biorefinery was 0.38%. The photovoltaic modules already have a well-established application in the country, though they need to increase their insertion over time, whereby the panels exhibit an average exergy efficiency of 31.6%, resulting in an annual electrical energy production of 255.84 kWh/m². The results show that photovoltaic modules are a more efficient alternative than sugarcane regarding exergy land use. In conclusion, this study briefly discusses the use of sugarcane and photovoltaic modules in the context of Brazil’s energy transition towards a reduced dependence on fossil fuels, based on the fact that sugarcane already has a low carbon footprint for transportation using ethanol, with supply from more than 40,000 stations, and a similar or lower carbon footprint than electrical vehicles used across the country. Full article

Smart meters monitor hourly water consumption patterns while reducing the cost and inconvenience of traditional meters. This study comprehensively analyzes 1871 households that previously used traditional meters from the distribution point to the distribution area. All the households were equipped with smart meters and the data collected were used for analysis. On the basis of the total estimated water consumption, 227 households were classified as single households. These households were further classified into single-worker and -nonworker households. This study analyzed smart meter data to evaluate the timings and amounts of water consumption peaks. The results indicate that worker households peaked at 8:00, with 29 L/h of consumption on weekdays, and peaked again on evenings at 20:00–21:00, averaging 32 L/h. For nonworker households, the peak occurred at 9:00, with 20 L/h, with no major changes in the afternoon, and a second peak occurred at 19:00–20:00 in evening, with an average of 19 L/h. Moreover, worker households peaked at 8:00 and 20:00 on weekdays, and at 9:00 and 19:00 on weekends. It was revealed that worker households consume 10% more on weekends than on weekdays, and 262% more from 13:00 to 16:00. These findings may assist in water supply planning by supporting distribution schedules on the basis of peak household consumption, leading to more helpful resource management. Full article

Driven by population growth, rising living costs, and the urgent need to address climate change, sustainable food production and circular economy principles are becoming increasingly important. Conventional agriculture faces significant challenges, including land scarcity, water shortages, and disrupted supply chains. As a solution, cities are adopting vertical farming to enhance urban food security and promote circularity. This research introduces FLOAT (Farming Lab on a Trough), an innovative vertical farming system made from bio-polymers and recycled polyethylene terephthalate glyco (rPETG) pellets from plastic bottles. FLOAT’s design emphasizes sustainability and closed-loop material usage. The study showcases the versatility of additive manufacturing (AM) in creating complex geometries with fully functional 1:1 prototypes. These prototypes highlight FLOAT’s potential as a scalable and adaptable solution for sustainable food production in urban settings, contributing to improved food security and environmental sustainability. By integrating FLOAT with conventional practices, we aim to exceed Singapore’s 2030 food security targets and achieve lasting urban food resilience. FLOAT aims to scale sustainable food production, fostering community ties with food, and nurturing future responsibility. Full article

Due to tremendous mining operations, large quantities of abandoned mines with considerable underground excavated space have formed in China during the past decades. This provides huge potential for geothermal energy production from mine water in abandoned coal mines to supply clean heating and cooling for buildings using heat pump technologies. In this study, an analytical model describing the injection pressure of mine water recharge for water source heat pumps in abandoned coal mines is developed. The analytical solution in the Laplace domain for the injection pressure is derived and the influences of different parameters on the injection pressure are investigated. This study indicates that a smaller pumping rate results in a smaller injection pressure, while smaller values of the hydraulic conductivity and the thickness of equivalent aquifer induce larger injection pressures. The well distance has insignificantly influenced the injection pressure at the beginning, but a smaller well distance leads to a larger injection pressure at later times. Additionally, the sensitivity analysis, conducted to assess the behavior of injection pressure with concerning changes in each input parameter, shows that the pumping rate and the hydraulic conductivity have a large influence on injection pressure compared with other parameters. Full article

Developing operation strategies for district cooling systems with chilled water storage is challenging due to uncertain fluctuations of cooling demand in actual operations. To address this issue, this paper developed an adaptive operation strategy and performed its validations by modeling and simulating a commercial cooling system in Shanghai using OpenModelica. Firstly, the originally designed operation strategy of the cooling system was evaluated by simulation but was found unable to meet the statistically averaged ideal cooling requirements due to the early exhaustion of stored chilled water at about 5:30 PM. Then, to build foundations for adaptive operation strategy development, a newly designed operation strategy was established by increasing the operation time of base load chillers in the valley and flat electricity price periods. The new strategy proved numerically sustainable in satisfying the ideal cooling demand. Moreover, to realize the strategy’s adaptability to actual cooling load fluctuations, an adaptive operation strategy was developed by tracking the target stored chilled water mass curve that was calculated by implementing the newly designed strategy. The simulation results verify that the adaptive operation strategy enables good adaptability to representative cooling load fluctuation cases by automatically and periodically adjusting the operation status of base load chillers. The adaptive operation strategy was then further widely numerically tested in hundreds of simulation cases with different cooling load variations. The time-lagging problem resulting in strategy failures was found in numerical tests and was addressed by slightly modifying the adaptive strategy. Results indicate that the adaptive operation strategy enables adaptability to deal with cooling demand fluctuations as well as allowing low cooling supply economic costs and power grid-friendly characteristics. This study provides theoretical support to strategy design and validations for district cooling system operations. Full article

Water management is vital for building an adaptive and resilient society. Water demand forecasting aids water management by learning the underlying relationship between consumption and governing variables for optimal supply. In this paper, we propose a week-ahead hourly water demand forecasting technique based on deep learning (DL) utilizing an encoded representation of historical supply data and influencing exogenous variables for a District Metered Area (DMA). We deploy a CNN model with and without attention and evaluate the model’s ability to forecast the supply for different DMAs with varying characteristics. The performances are quantitatively and qualitatively compared against a baseline LSTM. Full article

Pumped storage units serve as a crucial support for power systems to adapt to large-scale and high-proportion renewable energy sources by providing a stable and flexible energy supply. However, due to the coupling effects of electric power load demands and the complex multi-source factors within the water–mechanical–electrical system, the interrelationship between unit parameters becomes more intricate, posing significant threats to the operational reliability and health status of the units. The complexity of fault diagnosis is further aggravated by the intricate and varied nature of fault characteristics, as well as the challenges in signal extraction under conditions of strong electromagnetic interference and high noise levels. To address these issues, this paper proposes a novel data-model hybrid-driven strategy that analyzes vibration signals to achieve rapid and accurate fault diagnosis of the units. Firstly, the spectral kurtosis theory is employed to enhance the traditional empirical mode decomposition, achieving optimal decomposition and noise reduction effects for vibration signals. Secondly, the intrinsic mode functions (IMFs) obtained from the decomposition are reconstructed, and the entropy values of effective IMFs are calculated as fault feature vectors. Subsequently, the CNN-LSTM model is utilized for fault diagnosis. The effectiveness and feasibility of the proposed method are verified through actual operational data from pumped storage units in a specific region. Through analysis, the fault diagnosis accuracy of the method proposed in this paper can be maintained above 95%, demonstrating robustness in complex engineering environments and effectively ensuring the safe and stable operation of pumped storage units. Full article

Plant extracts are widely used in sustainable agriculture practices to enhance crop production and reduce chemical usage in agriculture. This study employed several extraction solutions of various plant extracts to synthesize planting and spraying strategies, assess the persistence efficacy of rice, and investigate the influence of selected water extracts on secondary chemicals at different rice planting stages. Among 17 water extracts that were evaluated on rice seeds, 7 were enhanced to align with the lengths of rice roots 50–70% and shoots 40–50%. The analysis of extraction, spraying, and planting experiments revealed that water extracts, soil application, and transplanting were the most efficient methods for stimulating rice growth, especially 0.1 and 0.5% concentrations. The efficacy of the extracts remained intact also after 14 days of treatment. This study showed that photosynthesis and antioxidant activities may play crucial roles in plant growth. Rice growth stimulation has been linked to photosynthesis, flavonoid contents, and antioxidant enzymes, providing a balanced supply of nutrients for plant growth. Among all tested water extracts, Psidium guajava, Aloe vera, Allium sativum, and Medicago sativa extracts can be used to promote plant growth in organic farming. Full article

Driven by the growing energy crisis and environmental concerns regarding the utilization of fossil fuels, biomass liquefaction has emerged as a highly promising technology for the production of renewable energy and value-added chemicals. However, due to the high oxygen content of biomass materials, biocrude oil produced from liquefaction processes often contains substantial oxygenated compounds, posing challenges for direct downstream applications. Catalytic hydrodeoxygenation (HDO) upgrading with hydrogen donors is crucial for improving the quality and applicability of biomass-derived fuels and chemicals. The costs, safety, and sustainability concerns associated with high-pressure gaseous hydrogen and organic molecule hydrogen donors are driving researchers to explore alternative and innovative biomass hydrodeoxygenation approaches without exogenous hydrogen donors. This review offers an overview of the recent developments in catalytic hydro-liquefaction and hydrodeoxygenation methods for biomass valorization without external hydrogen donation, including catalytic self-transfer hydrogenolysis using endogenous hydrogen in biomass structure, in situ catalytic hydrodeoxygenation employing water as the hydrogen donor, and in situ hydrodeoxygenation via water splitting assisted by zero-valent metals. The in situ hydrogen supply mechanisms and the impact of various hydrodeoxygenation catalysts on hydrogen donation efficiency using endogenous hydrogen are summarized in detail in this work. Furthermore, the current obstacles and future research demands are also discussed in order to provide valuable recommendations for the advancement of biomass utilization technologies. Full article

The efficient detection of leakages in water distribution networks (WDNs) is crucial to ensuring municipal water supply safety and improving urban operations. Traditionally, machine learning methods such as Convolutional Neural Networks (CNNs) and Autoencoders (AEs) have been used for leakage detection. However, these methods heavily rely on local pressure information and often fail to capture long-term dependencies in pressure series. In this paper, we propose a transformer-based model for detecting leakages in WDNs. The transformer incorporates an attention mechanism to learn data distributions and account for correlations between historical pressure data and data from the same time on different days, thereby emphasizing long-term dependencies in pressure series. Additionally, we apply pressure data normalization across each leakage scenario and concatenate position embeddings with pressure data in the transformer model to avoid feature misleading. The performance of the proposed method is evaluated by using detection accuracy and F1-score. The experimental studies conducted on simulated pressure datasets from three different WDNs demonstrate that the transformer-based model significantly outperforms traditional CNN methods. Full article

Access to clean and safe water is essential to sustain human life. With the growth of the world population, the demand for clean water is also increasing. Water Treatment Plants (WTPs) are among the highest electricity consumers, thus causing damage to the environment and human health by producing solid waste and sludge as by-products, along with air pollution and noise. With the fourth highest population in the world, Indonesia faces a high demand for clean water. The country has pledged to reach net zero by 2060 or sooner, requiring effort in all sectors of the economy. Taiwan is relatively small compared to Indonesia but has a higher average clean water supply per capita than Indonesia. This study assessed and compared the Carbon Footprint (CF) emitted from four WTPs in Indonesia and Taiwan. A Life Cycle Assessment (LCA) was used, employing a cradle to gate as the methodology. The results showed that the Indonesian WTPs emit more CF than the Taiwanese ones. Electricity consumption from the intake operation and screening process mainly contributes to the CF in most of the studied WTPs. While chemical usage is related to the amount of treated water and the water quality regulations, their consumption has more impact on ecosystem services in Taiwan. Using both renewable energy and good chemical management will be a better solution to lessen the environmental impact of all of these WTPs. Full article

Water deficiency and potential drought periods could be important ecological factors influencing cultivation areas and productivity once different crops are established. The principal supply of vegetable oil for oil crops is oil palm, and new challenges are emerging in the face of climatic changes. This study investigated the photosynthetic performance of 12 genotypes of Elaeis exposed to drought stress under controlled conditions. The assay included genotypes of Elaeis guineensis, Elaeis oleifera, and the interspecific O×G hybrid (E. oleifera × E. guineensis). The principal results showed that the E. guineensis genotype was the most efficient at achieving photosynthesis under drought stress conditions, followed by the hybrid and E. oleifera genotypes. The physiological parameters showed good prospects for vegetal breeding with different O×G hybrids, mainly because of their ability to maintain the equilibrium between CO₂ assimilation and stomatal aperture. We validated 11 genes associated with drought tolerance, but no differences were detected. These results indicate that no allelic variants were represented in the RNA during sampling for the validated genotypes. In conclusion, this study helps to define genotypes that can be used as parental lines for oil palm improvement. The gas exchange data showed that drought stress tolerance could define guidelines to incorporate the available genetic resources in breeding programs across the early selection in nursery stages. Full article

As freshwater supplies decrease, adopting sustainable practices like water- and energy-efficient irrigation is crucial, particularly in resource-constrained regions. Here, farmers often cannot purchase precision irrigation equipment, which achieves high water and energy efficiencies via full automation. Currently, no irrigation methods exist that combine automatic scheduling of events with manual operation of valves, familiar hardware on low-income farms. This work synthesizes functional requirements for a tool that could address efficiency needs while integrating into current manual practices. Then, a design concept for an automatic scheduling and manual operation (AS-MO) human–machine interaction (HMI) that meets these requirements is proposed. Two design stages of the AS-MO HMI were evaluated by farmers and market stakeholders in three countries. Results show that farmers in Kenya and Jordan valued the proposed AS-MO HMI because they could increase efficiency on their farms without the cost or complexity of automatic valves. In Morocco, a possible market was found, but a majority of participants preferred full automation. Interviewees provided feedback on how to improve the tool’s design in future iterations. If adopted at scale, the proposed AS-MO tool could increase efficiency on farms that otherwise cannot afford current precision irrigation technology, improving sustainable agriculture worldwide. Full article

Land use and land cover changes significantly affect the function and value of ecosystem services (ES). Exploring the spatial correspondence between changes in land cover and ES is conducive to optimizing the land use structure and increasing regional coordinated development. Thus, this study aimed to examine changes in land use and land cover (30 × 30 m) in Laos between 2000 and 2020 and their effects on ecosystem services value (ESV) using the Global Surface Cover Database land use data for 2000 to 2020, ArcGIS technology, and the table of Costanza’s value coefficients. The study results indicated that forest (79.5%), cultivated land (10.6%), and grassland (8.3%) were the dominant land use types in Laos over the past two decades. The forest area decreased significantly, while there were increases in other land types, and the forest was transformed into cultivated land and grassland. ES in Laos was valued at about USD 140–150 billion, with forest contributing the most, followed by cultivated land and grassland. ESV over the last two decades in Laos has increased by USD 3.94 million. Large values were assigned to regulating services (40%) and supporting services (14%). The ESV of food production, soil formation, and water supply increased, and the ESV of climate regulation, genetic resources, and erosion control decreased. In addition, the elasticity value of artificial surfaces was more prominent, with a more evident impact on ESV. For future development, Laos should rationally plan land resources, develop sustainable industries, maintain the dynamic balance of second-category ESV, and achieve sustainable economic and ecological development. This study provides a scientific basis for revealing changes in ESV in Laos over the past two decades, maintaining the stability and sustainable development of the environment in Laos, and realizing the sustainable use and efficient management of the local environmental resources. Full article

Umbria is an Italian region characterized by a highly fragmented water supply and distribution system, with many small systems fed by local sources. Chlorination in these small systems faces several challenges, including strong fluctuating demand, low economic significance, and limited access to infrastructure. Due to Italian regulations, the resulting frequent noncompliance with water quality standards negatively impacts performance indicators and tariffs. This research explores the possibility of implementing a low-cost chlorination system designed to adapt to varying environmental conditions (e.g., water and environmental temperature fluctuations, changes in pH, etc.) and demands. A prototype of the device was developed and tested in the Water Engineering Laboratory of the University of Perugia, Italy, to assess its ability to apply a programmable chlorination model. The effects of electronic environmental noise, along with the reliability of water meters, thermometer measurements, dosing pump control, local logging, and remote data transmission, were tested under different conditions. The results demonstrated the instrument’s readiness for field applications in pilot studies under real-world conditions. Full article