Water Science and Technology最新文献

In this paper, an optimization model is developed for the synthesis of water reuse networks in industrial processes, considering scenarios with and without pretreatment units. The model has a mixed-integer nonlinear programming (MINLP) formulation containing bilinear terms. The objectives are to reduce freshwater consumption in water networks, effluent generation, and total annual costs. The optimization model was coded in GAMS, and the bound contraction technique, which involves contracting the limits of one variable at a time, was employed. This technique enables the reduction of the distance between the lower and upper bounds obtained by solving the original problem. Two case studies, one involving a single contaminant and the other multiple contaminants, were used to test the applicability of the developed model and the optimization method, considering regeneration and pretreatment units. Results indicate that global optimal solutions are found for all the cases studied. The strategy employed contributes to both a reevaluation of existing water networks and the design of new ones, resulting in more efficient configurations.

The minimum navigation flow discharge (MNFD) required to maintain waterway dimensions in basins affected by water transfer projects is often subject to strategic interactions over ecological compensation among stakeholders, making its rapid determination highly important. However, in backwater zones of small to medium rivers, water level is jointly controlled by topographic variations and downstream backwater effects, complicating its estimation. This study develops a rapid calculation procedure for MNFD in such zones through theoretical analysis and numerical simulation, introducing a water surface slope correction into the Manning equation. Taking the Shiguan River, affected by the Huai River, as a case study, a two-dimensional hydrodynamic model was constructed to compare the simulated MNFD with the proposed method's result. Results indicate that, for a Class IV waterway depth of 3.10 m, direct application of the Manning equation yields an MNFD of approximately 1,529 m³/s, while the proposed rapid procedure gives 616 m³/s, and the numerical simulation indicates 600 m³/s. Compared with the direct Manning estimate, the proposed method substantially improves agreement with the numerical result, reducing the relative deviation from 155% to 3%.

A dam is constructed across a river, which stores and supplies water for various purposes. Though the dams have many benefits, there is always a threat of a break. The research is aimed at developing a dam break simulation model for the Tiga dam and producing an inundation map of affected areas. The study conducted a dam break analysis for the Tiga dam in Kano State, Nigeria, using HEC-RAS and HEC-HMS. The analysis simulated dam failure scenarios to predict breach parameters and flood hydrograph downstream. The results showed that 213 communities would be affected, with 122 in Jigawa State and 91 in Kano, covering an area of 4,397.06 km². Breach peak flow was estimated as 117,000 m³/s, arriving in 31.3 min. Validation of the hydrodynamic breach model performance was done using the observed annual outflow and simulated results, employing the Nash -Sutcliffe efficiency (NSE) statistical analysis. The NSE score of 0.71 indicates a decent fit of the HEC-RAS model to the data. The study recommends that the Hadejia Jama'are River Basin Development Authority implement the findings to develop emergency response plans and flood mitigation strategies to safeguard lives and property downstream. Future studies should conduct socio-economic impacts on affected areas.

Accurate reservoir water level forecasting is crucial for effective water resource management, flood control, and irrigation planning. This study investigates the application of deep learning methods to forecast daily water elevation in the Nesa Dam, located in southeastern Iran. A 15-year dataset comprising daily hydrometeorological variables - including rainfall, temperature, evaporation, inflow, and outflow - was used to train and evaluate three models: CNN + BiLSTM + Attention, Encoder-Decoder LSTM with Attention, and ConvLSTM2D.Using a sliding window approach, the models were trained on 80% of the data and tested on the remaining 20%. Performance was evaluated using RMSE, MAE, and R². The Encoder-Decoder LSTM with Attention model achieved the best results, with the lowest prediction errors and highest generalization performance. The CNN-based model showed moderate accuracy, while ConvLSTM2D exhibited noisy outputs and limited predictive capability. The study demonstrates that attention-based architectures are highly effective in modelling temporal dependencies in hydrological time series. The novelty lies in the comparative analysis of these models under identical experimental conditions using real-world data. The findings offer practical insights for developing intelligent forecasting systems in water resource management.

The persistent contamination of aquatic ecosystems by recalcitrant organic pollutants, including industrial dyes and pharmaceuticals, necessitates the development of efficient and sustainable water treatment technologies. While semiconductor photocatalysis offers a promising route for mineralization, conventional materials such as TiO₂, ZnO, and g-C₃N₄ are severely hindered by their reliance on UV light and rapid electron-hole recombination. Polypyrrole (PPy)-based nanocomposites have emerged as a transformative solution, leveraging PPy's unique conductivity and visible-light absorption to enable highly efficient, solar-driven photocatalysis. Unlike prior surveys that often focus solely on performance, this review systematically connects rational nanocomposite design with fundamental mechanistic insights and, critically, operational stability. The architecture of the catalyst - encompassing core-shell, ternary, and advanced Z-scheme heterojunction systems - constitutes a critical factor governing overall performance. Notably, optimized configurations have demonstrated degradation rates up to fivefold greater than those achieved by more basic designs. However, the literature reveals a crucial trade-off: the most kinetically rapid catalysts often suffer from poor long-term stability, posing a significant barrier to practical deployment. This review explores the intricate relationships between structure, performance, and stability, highlighting evidence-based design principles with direct relevance to the development of scalable water treatment technologies.

This laboratory-scale study investigates the effects of pulsed electric fields on electrochemical water softening using a strategy combining low-frequency pulse co-regulation, grey relational analysis, and neural network optimization. Results indicate that initial hardness, duty cycle, and frequency significantly influence hardness removal efficiency in descending order. Optimized pulse parameters enhance softening efficiency by balancing ion reaction and mass transfer rates, reducing energy consumption and concentration polarization. Under low hardness (≤600 mg L^-1), pulsed operation increases descaling per unit energy by 28.23-43.59% compared with direct current. High-speed imaging revealed that pulse intervals optimize bubble dynamics, promoting detachment with higher density and larger specific surface area, which weakens crystal-electrode adhesion and reduces ion diffusion resistance. The GA-MLP model, combined with grey correlation analysis, optimized softening under high hardness, determining ideal parameters for different hardness levels. Experimental verification confirmed these parameter combinations. The study provides new recommendations for optimizing electrochemical water softening parameters across varying hardness conditions based on laboratory-scale data.

Heavy metals are emerging pollutants that originate largely from anthropogenic activities. Their remediation in the free ion state was successfully achieved by simple alkali precipitation. However, the presence of organic compounds in aqueous media from diverse sources coexisting with heavy metals leads to the formation of stable, soluble, and recalcitrant complexes that challenge conventional treatment methods. Therefore, the remediation of heavy metal complexes (HMCs) has been extensively studied by researchers, resulting in the development of methods and techniques such as the use of chelating agents, bioadsorbents, and advanced oxidation processes for their treatment. In this review, the route to the environment, concentration level, and associated potential harm or damage caused by HMCs were covered. A detailed and comprehensive bibliometric analysis of publications dealing with the methods and mechanisms for the removal of various metal complexes was conducted. This review introduces the chemical interaction within the heavy metal complex (metal + organic ligands), summarises and discusses the newly developed methods, as well as their treatment performance and limitations for such newly formed pollutant compounds. To the best of our knowledge, this is the first article to provide a systematic summary of common and attractive methods employed for the remediation of HMCs.

The pursuit of sustainable livestock farming and environmentally responsible agricultural practices has spurred the development of innovative and affordable wastewater treatment technologies. This study investigates new biological treatment approaches that integrate the complementary processes of filtration, biosorption, and biodegradation to enhance eco-friendly wastewater management. A novel treatment concept was developed, representing a modern modification of the biosorption method that combines the oxidation of organic pollutants with ammonium reduction by an immobilized biocenosis, achieved through controlled aeration zones within a single bioreactor. An experimental facility was constructed and implemented at Feldman EcoPark (Kharkiv region, Ukraine) to serve the wastewater treatment needs of a contact zoo and animal rehabilitation center. The installation consists of a drainage treatment column with filter materials and a bioreactor - rotating biological contactor (RBC) containing microbial communities immobilized on inert carriers. Operational testing demonstrated high treatment efficiency, achieving up to 97.1% reduction in chemical oxygen demand (COD) and 85.6% removal of nitrogen compounds. Among the tested methods, biosorption proved particularly advantageous due to its cost-effectiveness, operational simplicity, and adaptability. The study also evaluated recycled polymers, including post-consumer PET, polycarbonate, and LDPE, as sustainable functional materials supporting filtration and microbial growth in wastewater treatment systems.

Nutrient contamination is a major contributor to eutrophication and water quality degradation worldwide. Conventional treatment technologies often lack selectivity and efficiency in complex aquatic environments, highlighting the need for advanced materials with tailored recognition capabilities. Molecularly imprinted polymers (MIPs) have emerged as promising adsorbents for nutrient remediation due to their high selectivity, stability and reusability. This review synthesizes recent progress on the synthesis strategies of MIPs. Applications of MIPs in removing phosphate, nitrate and ammonia from water are critically examined, with particular attention to adsorption performance under varying environmental conditions. The limitations of current systems, including modest adsorption capacities, incomplete template removal, matrix interferences and scalability challenges, are discussed alongside concerns about the fate and transport of MIPs in natural waters. Finally, the review highlights future opportunities in green synthesis and hybrid MIP composites to overcome current barriers. Collectively, this work positions MIPs as promising next-generation materials for selective nutrient removal and sustainable water remediation.

Coal gangue, a solid waste from coal mining, contains sulfide minerals that oxidize with oxygen and water to produce sulfuric acid, leading to acidic leachate. This leachate, rich in acidic substances and heavy metals, contaminates water sources through runoff or precipitation. To address this, a treatment system combining electrocoagulation and sulfate-reducing bacteria (SRB) was developed. Pine needles, used as a slow-release carbon source, replaced traditional carbon sources for microbial growth and metabolism. The system's key parameters included an electrode spacing of 6.5 mm, a current density of 25.0 mA/cm², a reaction time of 29.5 min, 500 g of pine needles, 260 mL of SRB inoculum, and a daily water intake of 1,300 mL. Over 60 days, water samples were analyzed every 2 days for efficiency and microbial structure. Pine needles effectively released carbon, sustaining microbial activity. Removal rates for total iron (TFe), Mn²⁺, Zn²⁺, and SO₄^2- were 99.5, 95.22, 99.60, and 79.59%, respectively, with effluent pH stabilized between 7.0 and 8.0. Key microbial taxa, including Clostridium, Lutispora, and Citrobacter, decomposed organic matter and reduced sulfate, enhancing treatment efficiency. This system effectively treated acidic coal gangue leachate, complied with standards, reduced environmental impact, and delivered significant benefits.