Evaluation of optimal conditions for anionic surfactant removal in wastewater

Evaluation of water quality using a Takagi-Sugeno fuzzy neural network and determination of heavy metal pollution index in a typical site upstream of the Yellow River

Assessment of river water quality is very important for understanding the impact of human activities on aquatic ecosystems. As the second-largest river in China, the Yellow River's water environment is closely related to the social development and water security of northern China. The Huangshui River is a major tributary of the upper Yellow River, and it supplies water to cities in the lower reaches. In this study, a Takagi-Sugeno (T-S) fuzzy neural network was used to evaluate water quality of the Huangshui River, and pollutant sources were analyzed. The heavy metal pollution index (HPI) was calculated to assess the heavy metal pollution level, and the health risks posed by heavy metal elements were assessed. The results indicated that the main contaminants in the Huangshui River were ammonia nitrogen (NH₃-N) and total phosphorus (TP), which was affected by various activities of industry, agriculture, and urbanization, and the maximum concentration of NH₃-N and TP was 5.90 mg/L and 0.36 mg/L, respectively. The T-S evaluation results of some points in the middle reaches were 3.317 and 3.197, which belonged to Level Ⅳ and the water quality was poor. The concentrations of Cu, Zn and Cr in the river were 0.57-44.58 μg/L, 10-122.50 μg/L and 2-28.67 μg/L, respectively, and they were relatively large. The T-S fuzzy neural network could evaluate water quality, avoiding extreme evaluation results by using fuzzy rules to reduce the influence of pollutant concentrations that are too high or too low. In addition to qualitative categorization of water quality, this approach can also quantitatively assess water quality within a single category. The results of water quality assessment could provide a scientific data support for river management.

The influence of environmental factor on the coagulation enhanced ultrafiltration of algae-laden water: Role of two anionic surfactants to the separation performance

With the acceleration of urbanization and the improvement of people's living standards, more chemicals that humans rely on are entering the city and surrounding water bodies. Anionic surfactants are one of the essential products for human beings. It is also one of the inducements that cause the eutrophication. The algae-laden water caused by eutrophication is a headache in the traditional water treatment process. To solve the problem, ultrafitration combined process was widely investigated to treat the algae-laden water. The presence of stimuli, low concentration anionic surfactant, probably interfere the performance of ultrafiltration process during algae-laden water treatment. In this study, the influence of two typical anionic surfactants, sodium dodecyl sulfate (SDS) and sodium dodecyl benzene sulfonate (LAS), on the performance of coagulation-enhanced ultrafiltration was investigated. The aluminum sulfate hydrate and iron sulfate hydrate were respectively employed as coagulant. Based on the residual turbidity and zeta potential, 4 mg/L Al and 8 mg/L Fe were determined as the optimal coagulant dosage. The floc morphology confirmed that Al-algae flocs with lower fractal dimension (D_f) were looser and more porous compared to Fe-algae flocs. More coagulant was depleted by LAS due to the better hydrophobicity of LAS. During the filtration process, LAS caused a larger flux reduction compared with SDS regardless of the coagulant that was used. More organic compounds penetrate into membrane pores and block the pores with the presence of LAS since algal cell aggregation was weakened. Finally, the rejection of organic compounds by the coagulation-enhanced ultrafiltration process was studied, and the co-existing surfactants can cause effluent deterioration. Therefore, the presence of surfactants has a negative effect to the ultrafiltration treatment of algae-laden water.