Search Results (48)

Effective management of cascade reservoir systems is essential for balancing hydropower generation, flood control, and ecological sustainability, especially under increasingly uncertain runoff conditions driven by climate change. Traditional optimization methods, while widely used, often struggle with high dimensionality and fail to adequately address inflow variability. This study introduces a novel deep reinforcement learning (DRL) framework that tightly couples probabilistic runoff forecasting with adaptive reservoir scheduling. We integrate a Long Short-Term Memory (LSTM) neural network to model runoff uncertainty and generate probabilistic inflow forecasts, which are then embedded into a Proximal Policy Optimization (PPO) algorithm via Monte Carlo sampling. This unified forecast–optimize architecture allows for dynamic policy adjustment in response to stochastic hydrological conditions. A case study on China’s Xiluodu–Xiangjiaba cascade system demonstrates that the proposed LSTM-PPO framework achieves superior performance compared to traditional baselines, notably improving power output, storage utilization, and spillage reduction. The results highlight the method’s robustness and scalability, suggesting strong potential for supporting resilient water–energy nexus management under complex environmental uncertainty. Full article

Dredging of fine sediments and dumping of fines at disposal sites produce passive plumes behind the dredging equipment. Each type of dredging method has its own plume characteristics. All types of dredging operations create some form of turbidity (spillage of dredged materials) in the water column, depending on (i) the applied method (mechanical grab/backhoe, hydraulic suction dredging with/without overflow), (ii) the nature of the sediment bed, and (iii) the hydrodynamic conditions. A simple parameter to represent the spillage of dredged materials is the spill percentage (R_spill) of the initial load. In the case of cutter dredging and hopper dredging without overflow, sediment spillage is mostly low, with values in the range of 1% to 3%, The spill percentage is higher, in the range of 3% to 30%, for hopper dredging of mud with intensive overflow. Spilling of dredged materials also occurs at disposal sites. The spill percentage is generally low, with values in the range of 1% to 3%, if the load is dumped through bottom doors in deep water, creating a dynamic plume which descends rapidly to the bottom with cloud velocities of 1 m/s. The most accurate approach to study passive plume behavior is the application of a 3D model, which, however, is a major, time-consuming effort. A practical 1D plume dispersion model can help to identify the best parameter settings involved and to conduct fast scan studies. The proposed 1D model represents equations for dynamic plume behavior, as well as passive plume behavior including advection, diffusion and settling processes. Full article

As the second largest power source in the world, hydropower plays a crucial role in the operation of power systems. This paper focuses on the key issues of regulating hydropower stations participating in the spot market. It aims at the core challenges, such as the conflict of cascade hydro plants’ joint clearing, the lack of adaptability for different types of power supply bidding on the same platform, and the contradiction between long-term operation and the spot market. Through the construction of a water spillage management strategy and settlement compensation mechanism, the competitive abandoned water problem caused by mismatched quotations of cascade hydro plants can be solved. In order to achieve reasonable recovery of the power cost, a separate bidding mechanism and capacity cost recovery model are designed. Subsequently, the sufficient electricity supply constraint of the remaining period is integrated into the spot-clearing model, which can coordinate short-term hydropower dispatch with long-term energy storage demand. The operation of the Yunnan electricity spot market is being simulated to verify the effectiveness of the proposed method. Full article

The water quality monitoring of large water masses using robotic vehicles is a complex task highly developed in recent years. The main approaches utilize adaptative informative path planning of fleets of autonomous surface vehicles and computer learning methods. However, water monitoring is characterized by a highly dynamic and unknown environment. Thus, the characterization of the water quality state of a water mass becomes a challenge. This paper proposes a variational autoencoder structure, trained in a model-free manner, that aims to provide a dynamic contamination model from partial observations of a homogeneous fleet of autonomous surface vehicles. To train the proposed approach, an oil spillage simulator based on heuristics is provided for world building. The proposed variational autoencoder was tested in three different environments characterized by different oil spill movements and twp different fleets equipped with different sensors. The results show accurate future contamination distribution predictions with a mean squared error ranging from 3 to 9% of the baseline at validation, defined as the static approach. Further tests addressed the overfit of the proposed neural network, showing a high robustness against unseen scenarios, and the effects of the gathered monitoring information in the variational autoencoder performance. Full article

Consecutive oscillatory eruptions of a mixture of gas and liquid in urban stormwater systems, commonly referred to as sewer geysers, are investigated using transient three-dimensional (3D) computational fluid dynamics (CFD) models. This study provides a detailed mechanistic understanding of geyser formation under partially filled dropshaft conditions, an area not previously explored in depth. The maximum geyser eruption velocities were observed to reach 14.58 m/s under fully filled initial conditions ($h_w/h_d$ = 1) and reduced to 5.17 m/s and 3.02 m/s for partially filled conditions ($h_w/h_d$ = 0.5 and 0.23, respectively). The pressure gradients along the horizontal pipe drove slug formation and correlated directly with the air ingress rates and dropshaft configurations. The influence of the dropshaft diameter was also assessed, showing a 116% increase in eruption velocity when the dropshaft to horizontal pipe diameter ratio ($D_d/D_t$) was reduced from 1.0 to 0.5. It was found that the strength of the geyser (as represented by the eruption velocity from the top of the dropshaft) increased with an increase in the initial water depth in the dropshaft and a reduction in the dropshaft diameter. Additionally, the Kelvin–Helmholtz instability criteria were satisfied during transitions from stratified to slug flow, and they were responsible for the jump and transition of the flow during the initial rise and fallback of the water in the dropshaft. The present study shows that, under an initially lower water depth in the dropshaft, immediate spillage is not guaranteed. However, the subsequent mixing of air from the horizontal pipe generated a less dense mixture, causing a change in pressure distribution along the tunnel, which drove the entire geyser mechanism. This study underscores the critical role of the initial conditions and geometric parameters in influencing geyser dynamics, offering practical guidelines for urban drainage infrastructure. Full article

Waterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially in remote areas. The system utilizes an autonomous water quality monitoring setup consisting of an airborne water sample collector, an autonomous sample processor, and an artificial intelligence-aided microscopic detector for risk assessment. The proposed system replaces the time-consuming conventional monitoring protocol by automating sample collection, sample processing, and pathogen detection. Furthermore, it provides a safer processing method against the spillage of contaminated liquids and potential resultant aerosols during the heat fixation of specimens. A morphological image processing technique of light microscopic images is used to segment images, assisting in selecting a unified appropriate input segment size based on individual blob areas of different bacterial cultures. The dataset included harmful pathogenic bacteria (A. baumanii, E. coli, and P. aeruginosa) and harmless ones found in drinking water and wastewater (E. faecium, L. paracasei, and Micrococcus spp.). The segmented labeled dataset was used to train deep convolutional neural networks to automatically detect pathogens in microscopic images. To minimize prediction error, Bayesian optimization was applied to tune the hyperparameters of the networks’ architecture and training settings. Different convolutional networks were tested in accordance with different required output labels. The neural network used to classify bacterial cultures as harmful or harmless achieved an accuracy of 99.7%. The neural network used to identify the specific types of bacteria achieved a cumulative accuracy of 93.65%. Full article

This research focuses on improving flood management of the Malwathu Oya River in Anuradhapura Historical City, Sri Lanka, by designing an efficient gate system for the weir of Halpan Ela in the Malwathu Oya River. Frequent flooding threatens agriculture, infrastructure, and public safety in this region. This research aims to enhance water level control in the upper reach of Halpan Ela Anicut by evaluating rainfall patterns, tank spillway efficiency, and gate operation challenges. Historical data on rainfall and tank spillage were analyzed. Flow simulations revealed significant pressure differences, with the existing gate structure showing an upstream pressure of 114,492.5 Pa at a maximum flow of 1740 m³/s, compared to 105,406 Pa for the new flap gate system at the same flow rate. This represents a pressure difference of 9 kPa, equivalent to a 0.9 m water head. Despite the system’s estimated cost of USD 0.1 million, the potential reduction in river flood damage, which currently exceeds USD 0.2 million annually, demonstrates its value. This research emphasizes the effectiveness of the flap gate system in reducing flood risks in Anuradhapura City compared to the existing gate type, though it is only a part of a broader flood mitigation strategy. Full article

Oil spillages on a sea’s or an ocean’s surface are a threat to marine and coastal ecosystems. They are mainly caused by ship accidents, illegal discharge of oil from ships during cleaning and oil seepage from natural reservoirs. Synthetic-Aperture Radar (SAR) has proved to be a useful tool for analyzing oil spills, because it operates in all-day, all-weather conditions. An oil spill can typically be seen as a dark stretch in SAR images and can often be detected through visual inspection. The major challenge is to differentiate oil spills from look-alikes, i.e., low-wind areas, algae blooms and grease ice, etc., that have a dark signature similar to that of an oil spill. It has been noted over time that oil spill events in Pakistan’s territorial waters often remain undetected until the oil reaches the coastal regions or it is located by concerned authorities during patrolling. A formal remote sensing-based operational framework for oil spills detection in Pakistan’s Exclusive Economic Zone (EEZ) in the Arabian Sea is urgently needed. In this paper, we report the use of an encoder–decoder-based convolutional neural network trained on an annotated dataset comprising selected oil spill events verified by the European Maritime Safety Agency (EMSA). The dataset encompasses multiple classes, viz., sea surface, oil spill, look-alikes, ships and land. We processed Sentinel-1 acquisitions over the EEZ from January 2017 to December 2023, and we thereby prepared a repository of SAR images for the aforementioned duration. This repository contained images that had been vetted by SAR experts, to trace and confirm oil spills. We tested the repository using the trained model, and, to our surprise, we detected 92 previously unreported oil spill events within those seven years. In 2020, our model detected 26 oil spills in the EEZ, which corresponds to the highest number of spills detected in a single year; whereas in 2023, our model detected 10 oil spill events. In terms of the total surface area covered by the spills, the worst year was 2021, with a cumulative 395 sq. km covered in oil or an oil-like substance. On the whole, these are alarming figures. Full article

This study introduces a novel approach for optimizing the monthly hydropower scheduling of cascaded reservoirs by employing a special ordered set of type 2 (SOS2) formulation within a mixed integer linear programming (MILP) model. The proposed method linearizes the relationships between hydropower output, spillage, storage, and outflow, enabling controllable spillage. The objective is to minimize spillage, maximize firm hydropower output, and maximize energy production, all in priority while considering complex constraints such as reservoir storage and discharge bounds, upstream–downstream relationship, and water balance. The approach is applied to four cascaded reservoirs on the Lancang River. Results indicate that the SOS2 formulation effectively minimizes spillage, maximizes hydropower generation, and ensures maximum firm power output. Comparisons across different gridding resolutions reveal that more grid points yield greater benefits but with a longer solution time. Furthermore, a comparison with the Successive Quadratic Programming (SQP) method highlights the superior performance of the SOS2 model in terms of objective improvement and solution efficiency. This research offers valuable insights into optimizing monthly hydropower scheduling for cascaded reservoir systems, enhancing operational efficiency and decision-making in water resources management. Full article

The spillage of oil causes severe and long-lasting impacts on both the environment and human life. It is crucial to carefully reconsider the methods and techniques currently employed to recover spilled oil in order to prevent any possible secondary pollution and save time. Therefore, the techniques used to recover spilled oil should be readily available, highly responsive, cost-effective, environmentally safe, and, last but not least, they should have a high sorption capacity. The use of sorbents obtained from natural materials is considered a suitable approach for dealing with oil spills because of their exceptional physical characteristics that support sustainable environmental protection strategies. This article presents a novel sorbent material, which is a composite siloxane foam filled with bentonite clay, aimed at enhancing the hydrophobic and oleophilic behavior of the material. The thermal treatment of bentonite optimizes its sorption capacity by eliminating water, and increasing the surface area, and, consequently, its interaction with oils. In particular, the maximum sorption capacity is observed in kerosene and naphtha for the bentonite clay thermally treated at 600 °C, showing an uptake at saturation of 496.8% and 520.1%, respectively. Additionally, the reusability of the composite foam is evaluated by squeezing it after reaching its saturation point to determine its sorption capacity and reusability. Full article

The purpose of this research was to evaluate the adsorbent properties of carbon nanotubes by investigating, in particular, the possibility of their use in the purification of water contaminated with automotive diesel, caused, in most cases, as a result of spillage from underground tanks, leaks from pipelines, traffic accidents, etc. In particular, we investigated whether the high molecular weights of the hydrocarbon molecules present in diesel could influence the adsorption capacity of carbon nanotubes. Initial systems consisting of water and diesel were treated with different amounts of carbon nanotubes. The final post-adsorption phases were characterized using NMR analysis, FT-IR spectroscopy and TG-DTG-DTA thermal analysis. Carbon nanotubes showed great efficiency in the adsorption of diesel, the possibility of their reuse in several adsorption cycles and the consequent recovery of the adsorbed diesel and of the treated water. Full article

Water, one of the most important resources that the planet offers us, cannot be used without meeting certain quality parameters which are increasingly difficult to achieve due to human activities such as deforestation, improper industrial and agricultural waste management, maritime traffic and fuel spillages. Cellulose-based materials or membranes are among the most important candidates to water treatment processes in the actual context of sustainable processes due to the chemical versatility of this cellulose derivative and also due to its large availability This review aims to present the use of functionalized or composite cellulose acetate membranes in water reuse processes in the context of the circular economy. The synthesis methods, process performances, and limitations of these membranes are presented, and the main future directions are thoroughly discussed at the end of the manuscript. Full article

Currently, frequent oil spill accidents caused by transportation, storage, and usage may lead to extensive damage to marine ecosystems. Effective methods for oil spillage recovery from offshore environments are still urgently in demand. A superhydrophobic sponge (MS@PVC@SiO₂) was obtained via a facile two-step method for rapid oil adsorption, and a piece of novel collection equipment loaded with MS@PVC@SiO₂ was developed for in situ continuous oil/seawater separation. The results showed that MS@PVC@SiO₂ exhibits excellent water repellence, compressibility, and durability. Furthermore, the obtained MS@PVC@SiO₂ shows high diesel oil adsorption capacity (32 g/g), and excellent recyclability (up to 200 times). The collection equipment demonstrates highly selective oil adsorption capacity and good stability in real seawater. The maximum possible recovery capacity of collection equipment was 557.784 L/h with 98% efficiency, which was much higher than that of commercial disc oil collectors (119.8 L/h). The recovery performance was effectively improved by introducing MS@PVC@SiO₂, due to its large specific area and enough storage space. Moreover, even after continuous operation for 58 h in seawater, the collection equipment remained at a high recovery capacity. The results indicate that both MS@PVC@SiO₂ and the collection equipment have great application perspectives in practical marine oil spillage recovery. Full article

Natural rubber latex (NRL) is normally transported to a destination in colloid/liquid form. It requires large storage containers such as drums and the probability of latex leakage during transportation is high. This is prevalent especially when transporting latex by sea. To prevent latex spillage, the liquid form of NRL is transformed into solid/frozen latex by freezing. However, the coagulation/destabilization of NRL by freezing has been acknowledged as a problem for years. Therefore, this study proposed a new low temperature stabilizer named azidated glycerol (AG) to be incorporated in NRL liquid before the freezing process. AG was prepared by a chemical reaction of pure glycerol with sodium azide. NRL containing AG was then frozen at a temperature of −4 °C. After 24 h of freezing, the frozen latex was thawed at ambient temperature for 1 h followed by heating in a water bath at 40 °C for another 1 h. The regenerated latex was then allowed to stand at room temperature before testing. The effect of AG on the colloid properties before and after the freeze–thaw processes was studied. The production of AG was confirmed by the appearance of a peak in the range of 2160–2120 cm⁻¹, corresponding to N=N=N stretching, confirming the introduction of an azide group into the glycerol molecule. Modifying NRL with AG did not significantly influence the TSC of latex. Increasing the AG content up to 0.4 phr resulted in an increase in MST from 699 s to 828 s. An AG content of 0.2 phr resulted in the highest anionically stabilized latex as indicated by zeta potential values of −59.63 mV (before freezing) and −56.27 mV (after thawing). It is concluded that the AG produced in this study can be used as an anti-freeze stabilizer for NRL and is suitable for latex marine transportation. Full article

Operators often have a dilemma in deciding what water levels the over-year hydropower reservoirs should drawdown at the end of dry seasons, either too high to achieve a large firm hydropower output during the dry seasons in the current year and minor spillage in coming flood seasons, or too low to refill to the full storage capacity at the end of the flood seasons and a greater firm hydropower output in the coming year. This work formulates a third-monthly (in an interval of about ten days) hydropower scheduling model, which is linearized by linearly concaving the nonlinear functions and presents a rolling strategy to simulate many years of reservoir operations to investigate how the water level at the end of dry seasons will impact the performances, including the energy production, firm hydropower output, full-refilling rate, etc. Applied to 11 cascaded hydropower reservoirs in a river in southwest China, the simulation reveals that targeting a drawdown water level between 1185–1214 m for one of its major over-year reservoirs and 774–791 m for another is the most favorable option for generating more hydropower and yielding larger firm hydropower output. Full article