4.7 Article

DOC signal-based alum dose control for drinking water treatment plants

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Environmental Sciences

A new process to further remove dissolved organic matter and disinfection by-product formation potential during drinking water treatment

Chongtian Lei et al.

Summary: This study investigates the mechanism by which biological activated carbon (BAC) affects effluent water quality. It was found that soluble microbial products (SMPs) are mainly included in the transitional hydrophilic neutral (TPIN) fraction, which is the main cause of dissolved organic matter (DOC) increase. A new combined process using coagulation effectively removes TPIN and reduces trihalomethane formation potential (THMFP). Compared with the old process, the new combined process has higher removal rates of DOC, THMFP, and haloacetic acid formation potential (HAAFP).

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2023)

Article Green & Sustainable Science & Technology

Assessment of surface water quality and monitoring in southern Vietnam using multicriteria statistical approaches

Thanh Giao Nguyen et al.

Summary: This study analyzed surface water quality fluctuations in the southern region of Vietnam and found that it was contaminated with organics, nutrients, and iron. Some locations exceeded the allowable limit for lead, but other indicators met the standard. Water quality was classified from bad to very good based on the Water Quality Index. Cluster analysis and principal component analysis helped optimize the monitoring program and reduce costs. The study provides valuable information for decision-making regarding environmental quality monitoring in the southern region of Vietnam.

SUSTAINABLE ENVIRONMENT RESEARCH (2022)

Article Engineering, Environmental

Enhanced electrocoagulation process for natural organic matter removal from surface drinking water sources: coagulant dose control & organic matter characteristics

Hiua Daraei et al.

Summary: This study presents an enhanced electro-coagulation technique for removing natural dissolved organic matter (DOM) from surface drinking water sources. The study compares the efficiency of DOM removal by electro-coagulation with that by enhanced chemical coagulation. The results show that electro-coagulation is comparable to chemical coagulation in terms of DOM removal efficiency. The study also investigates the impact of process parameters and DOM characteristic variations.

ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY (2022)

Review Environmental Sciences

State of the Art of Online Monitoring and Control of the Coagulation Process

Harsha Ratnaweera et al.

Article Engineering, Environmental

Coagulation modeling using artificial neural networks to predict both turbidity and DOM-PARAFAC component removal

Marla J. Kennedy et al.

JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING (2015)

Article Engineering, Environmental

Modification of jar testing protocol combined with mEnCo model predicted dose to predict dissolved organic matter removal for surface water

Mohamad Fared Murshed et al.

WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY (2014)

Article Engineering, Chemical

Characterizing DOM and removal by enhanced coagulation: A survey with typical Chinese source waters

Dongsheng Wang et al.

SEPARATION AND PURIFICATION TECHNOLOGY (2013)

Article Engineering, Chemical

Response surface methodological approach to optimize the coagulation-flocculation process in drinking water treatment

Thuy Khanh Trinh et al.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2011)

Article Engineering, Civil

Development and implementation of the software mEnCo (c) to predict coagulant doses for DOC removal at full-scale WTPs in South Australia

J. van Leeuwen et al.

JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA (2009)

Article Engineering, Environmental

Simultaneous removal of turbidity and humic acid from high turbidity stormwater

G Annadurai et al.

ADVANCES IN ENVIRONMENTAL RESEARCH (2004)

Article Computer Science, Interdisciplinary Applications

Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters

HR Maier et al.

ENVIRONMENTAL MODELLING & SOFTWARE (2004)