Dataset Information

Dataset on the cost estimation for spent filter backwash water (SFBW) treatment.

ABSTRACT: The dataset presented in this article are related to the research article entitled "Hybrid coagulation-UF processes for spent filter backwash water treatment: a comparison studies for PAFCl and FeCl3 as a pre-treatment" (Ebrahimi et al., 2017) [1]. This article reports the cost estimation for treating produced spent filter backwash water (SFBW) during water treatment in Isfahan- Iran by various methods including primary sedimentation, coagulation & flocculation, second clarification, ultra filtration (UF) and recirculation of settled SFBW to water treatment plant (WTP) entrance. Coagulation conducted by PAFCl and FeCl3 as pre polymerized and traditional coagulants. Cost estimation showed that contrary to expectations, the recirculation of settled SFBW to WTP entrance is more expensive than other method and it costs about $ 37,814,817.6. Versus the cheapest option related to separate primary sedimentation, coagulation & flocculation in WTP. This option cost about $ 4,757,200 and $ 950,213 when FeCl3 and PAFCl used as coagulant, respectively.

SUBMITTER: Ebrahimi A

PROVIDER: S-EPMC5695916 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Phytotoxins - toxins produced by plants - are contaminants with the potential to impair drinking water quality. They encompass a large group of toxic, partially persistent compounds that have been detected in seepage waters and in shallow wells used for drinking water production. If phytotoxins enter wells used for drinking water production, it is essential to know if the drinking water treatment processes will remove them from the water phase. However, it is currently unknown whether phytotoxins remain stable during traditional groundwater treatment using sand filters as the main treatment process. The objective of this study is to investigate removal potential of phytotoxins in biological sand filters and to asses if the removal potential is similar at different waterworks. Microcosms were set up with filter sand and drinking water collected at different groundwater-based waterworks. To be able to monitor phytotoxin removal ptaquiloside, caudatoside, gramine, sparteine, jacobine N-oxide, senecionine N-oxide and caffeine were applied at initial concentrations of 300 µg L-1, which is approx. two orders of magnitude higher than currently detected in environment, but expected to cover extreme environmental conditions. Removal was monitored over a period of 14 days. Despite the high initial concentration, all filter sands removed ptaquiloside and caudatoside completely from the water phase and at waterworks where pellet softening was implemented (pH 8.4) prior to rapid sand filtration, complete removal occurred within the first 30 min. All filter sands removed gramine and sparteine, primarily by a biological process, while jacobine N-oxide, senecionine N-oxide and caffeine were recalcitrant in the filter sands. During degradation of ptaquiloside and caudatoside we observed formation and subsequent removal of degradation products pterosin B and A. Filter sands with the highest removal potential were characterised by high contents of deposited iron and manganese oxides and hence large specific surface areas. Difference between bacterial communities investigated by 16S rRNA gene analyses did not explain different removal in the filter sands. All five investigated filter sands showed similar degradation patterns regardless of water chemistry and waterworks of origin. In drinking water treatment systems biological sand filters might therefore remove phytotoxin contaminants such as ptaquiloside, caudatoside, gramine, sparteine, while for other compounds e.g. jacobine N-oxide, senecionine N-oxide further investigations involving more advanced treatment options are needed.

Dataset Information

Dataset on the cost estimation for spent filter backwash water (SFBW) treatment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets