Water Science and Technology最新文献

Sewage sludge (SS) is a potential source of bioenergy, yet its management is a global concern. Anaerobic digestion (AD) is applied to effectively valorize SS by reclaiming energy in the form of methane. However, the complex floc structure of SS hinders hydrolysis during AD process, thus resulting in lower process efficiency. To overcome the rate-limiting hydrolysis, various pre-treatment methods have been developed to enhance AD efficiency. This review aims to provide insights into recent advancements in pre-treatment technologies, including mechanical, chemical, thermal, and biological methods. Each technology was critically evaluated and compared, and its relative worth was summarized based on full-scale applicability, along with economic benefits, AD performance improvements, and impact on digested sludge. The paper illuminates the readers about existing research gaps, and the future research needed for successful implementation of these approaches at full scale.

Effective wastewater management is crucial in regions experiencing water scarcity and environmental stressors, such as pollution and climate change. Optimizing treatment processes is essential for achieving environmental sustainability. This study aims to highlight the importance of effective wastewater management strategies, particularly in regions facing water scarcity. Our objective was to identify key factors influencing the treatment process. Therefore, we evaluated associations between physicochemical parameters using multivariate statistical methods, including Principal Component Analysis (PCA) and Hierarchical Ascendant Classification (HAC). Our findings categorize the monthly water samples into three distinct groups based on levels of organic pollution: the first group (July, August, and September) is characterized by high oxygenation levels and significantly low organic pollution, indicating optimal system operation. The second group (April, October, November, and December) exhibits low oxygenation and low organic pollution, promoting sludge settling and pollutant reduction. The third group (January, February, March, May, and June) shows significantly high organic pollution and low oxygenation, which corresponds to unfavorable environmental conditions. Our study demonstrates the effectiveness of multivariate statistical methods in optimizing wastewater treatment processes, providing crucial insights for environmental sustainability and water resource management.

The primary objective of this study is to develop a robust model that employs a fuzzy logic interface (FL) and particle swarm optimization (PSO) to forecast the optimal parameters of a pyramid solar still (PSS). The model considers a range of environmental variables and varying levels of silver nanoparticles (Ag) mixed with paraffin wax, serving as a phase change material (PCM). The study focuses on three key factors: solar intensity ranging from 350 to 950 W/m², water depth varying between 4 and 8 cm, and silver (Ag) nanoparticle concentration ranging from 0.5 to 1.5% and corresponding output responses are productivity (P), glass temperature (T_g), and basin water temperature (T_w). The experimental design is based on Taguchi's L9 orthogonal array. A technique for ordering preference by similarity to the ideal solution (TOPSIS) is utilized to optimize the process parameters of PSS. Incorporating a fuzzy inference system (FIS) aims to minimize the uncertainty within the system, and the particle swarm optimization algorithm is employed to fine-tune the optimal settings. These methodologies are employed to forecast the optimal conditions required to enhance the productivity of the PSS.

Precipitation forecasting plays a pivotal role in guiding the effective management of regional water resources and providing crucial warnings for regional droughts and floods. Finding a monthly precipitation simulation model with robust fitting performance is a significant research endeavor in practical precipitation prediction. This paper introduces two modified African vulture optimization algorithms (MAVOA1 and MAVOA2). It provides hyperparameter optimization techniques for the least squares support vector machine (LSSVM), long short-term memory neural network (LSTM), and random forest (RF) models. These techniques are used to construct a monthly precipitation simulation model based on algorithmic optimization coupled with variational mode decomposition for full decomposition. The test results at five typical stations in the North China Plain reveal the following: (1) the LSSVM model demonstrates significantly better performance than the LSTM and RF models. (2) the MAVOA2-LSSVM model has the best-integrated effect: the average test fitting error is RMSE = 17.50 mm/month, MRE = 117.25%, NSE = 0.90, which shows its superiority in practical application and can significantly improve the accuracy of precipitation prediction; MAVOA2 is more suitable for machine learning models with more hyperparameters of its own, which provides a reference for hyperparameter optimization algorithms in the other fields.

Efficient degradation of industrial organic wastewater has become a significant environmental concern. Electrochemical oxidation technology is promising due to its high catalytic degradation ability. In this study, Co-Bi/GAC particle electrodes were prepared and characterized for degradation of 1,4-dioxane. The electrochemical process parameters were optimized by response surface methodology (RSM), and the influence of water quality factors on the removal rate of 1,4-dioxane was investigated. The results showed that the main influencing factors were the Co/Bi mass ratio and calcination temperature. The carrier metals, Co and Bi, existed mainly on the GAC surface as Co₃O₄ and Bi₂O₃. The removal of 1,4-dioxane was predominantly achieved through the synergistic reaction of electrode adsorption, anodic oxidation, and particle electrode oxidation, with ·OH playing a significant role as the main active free radical. Furthermore, the particle electrode was demonstrated in different acid-base conditions (pH = 3, 5, 7, 9, and 11). However, high concentrations of Cl^- and NO₃^- hindered the degradation process, potentially participating in competitive reactions. Despite this, the particle electrode exhibited good stability after five cycles. The results provide a new perspective for constructing efficient and stable three-dimensional (3D) electrocatalytic particle electrodes to remove complex industrial wastewater.

Maintaining the standard of water quality in an aquatic habitat necessitates continual assessment of its physicochemical properties. The purpose of this study was to evaluate physicochemical properties and to discuss the causes of spatiotemporal variability in key physicochemical parameters at five different locations of Dal Lake. Water samples were collected in four seasons for 3 years (i.e., January 2019-December 2021) to evaluate various physicochemical properties using standard methods. The analysis shows that the macrophytic development has increased due to organic and inorganic load, leading to the Lake's deterioration. The analysis indicates positive and negative correlations among various parameters across five sampling sites. Principal component analysis shows that two components (PC1 and PC2) explain 47.35, 47.54, and 48.11% of the variability in the years 2019, 2020, and 2021, respectively. From 2019 to 2021, the continuous decrease in dissolved oxygen and increased levels of magnesium, conductivity, alkalinity, total hardness, calcium hardness, total phosphorus, and nitrate-nitrogen suggest a trend toward eutrophication in the lake.

The fast-growing global population has led to a substantial increase in food production, which generates large volumes of wastewater during the process. Despite most industrial wastewater being discharged at lower ambient temperatures (<20 °C), majority of the high-rate anaerobic reactors are operated at mesophilic temperatures (>30 °C). High-rate low-temperature anaerobic digestion (LtAD) has proven successful in treating industrial wastewater both at laboratory and pilot scales, boasting efficient organic removal and biogas production. In this study, we demonstrated the feasibility of two full-scale high-rate LtAD bioreactors treating meat processing and dairy wastewater, and the microbial communities in both reactors were examined. Both reactors exhibited rapid start-up, achieving considerable chemical oxygen demand (COD) removal efficiencies (total COD removal >80%) and generating high-quality biogas (CH₄% in biogas >75%). Long-term operations (6-12 months) underscored the robustness of LtAD bioreactors even during winter periods (average temperature <12 °C), as evidenced by sustained high COD removal rates (total COD removal >80%). The stable performance was underpinned by a resilient microbial community comprising active acetoclastic methanogens, hydrolytic, and fermentative bacteria. These findings underscore the feasibility of high-rate low-temperature anaerobic wastewater treatment, offering promising solutions to the zero-emission wastewater treatment challenge.

Microalgae biomass products are gaining popularity due to their diverse applications in various sectors. However, the costs associated with media ingredients and cell harvesting pose challenges to the scale-up of microalgae cultivation. This study evaluated the growth and nutrient removal efficiency (RE) of immobilized microalgae Tetradesmus obliquus in sodium alginate beads cultivated in swine manure-based wastewater compared to free cells. The main findings of this research include (i) immobilized cells outperformed free cells, showing approximately 2.3 times higher biomass production, especially at 10% effluent concentration; (ii) enhanced organic carbon removal was observed, with a significant 62% reduction in chemical oxygen demand (383.46-144.84 mg L^-1) within 48 h for immobilized cells compared to 6% in free culture; (iii) both immobilized and free cells exhibited efficient removal of total nitrogen and total phosphorus, with high REs exceeding 99% for phosphorus. In addition, microscopic analysis confirmed successful cell dispersion within the alginate beads, ensuring efficient light and substrate transfer. Overall, the results highlight the potential of immobilization techniques and alternative media, such as biodigested swine manure, to enhance microalgal growth and nutrient RE, offering promising prospects for sustainable wastewater treatment processes.

The transport of solid-liquid two-phase flow is widely used in water conservancy, environmental protection, and municipal engineering. Accurate pressure loss calculation is crucial for hydraulic transport pipelines, particularly in the case of bends, valves, and other deformation parts. These factors directly impact the energy consumption and the investment of the system. This paper employed the Euler-Euler multiphase flow model to investigate the characteristics of solid-liquid two-phase flow in vertically positioned combined elbows. The model was initially validated using data from the literature. Subsequently, based on the validated model, an investigation was conducted to determine the relationship between pressure loss and various factors, including flow velocity, combined angle, particle concentration, and particle size. Finally, the changes in velocity distribution, particle concentration, and turbulent kinetic energy were analyzed. The results indicate that the pressure loss increases with the flow velocity, tends to decrease with the combined angle, and increases with the particle concentration. The relationship between pressure loss and particle size is more complex. The velocity distribution, particle concentration, and turbulent kinetic energy exhibit the variations caused by different factors.