Evidence map›Paper›PMID 31823025›Full record

ArticleEnvironmental monitoring and assessment2019

Risk assessment of an industrial wastewater treatment and reclamation plant using the bow-tie method.

Razieh Analouei, Masoud Taheriyoun, Hamid Reza Safavi

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Article in Environmental monitoring and assessment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Exposure Route of TiONanomaterials (Basel, Switzerland) · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Razieh AnaloueiDepartment of Civil Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Masoud TaheriyounDepartment of Civil Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran. taheriyoun@iut.ac.ir.ORCID http://orcid.org/0000-0002-9228-6258
Hamid Reza SafaviDepartment of Civil Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

At present, wastewater reuse and recycling in water-stressed countries such as Iran has become a vital option for better management of water scarcity. However, despite the benefits of wastewater reuse, there is a probability of incomplete treatment of wastewater in accordance with the standards. Risk assessment of a wastewater treatment plant is required in order to determine the causes and consequences of failure in the treatment system. Among various methods used for risk assessment, the bow-tie (BT) approach is an efficient technique since it represents a failure event from causes to effects. In this method, for a wastewater treatment plant (WWTP), causes of an adverse event, including the violation of the effluent water quality from the standard limit is studied using fault tree analysis (FTA). Then, the impacts of these adverse events are classified in different scenarios by the use of event tree analysis (ETA). In this study, the BT technique was applied to assess the risk of the WWTP of the Moorchekhort industrial complex (MIC), located in the central part of Iran. The plant consists of two parts, including activated sludge and advanced treatment processes to provide reclaimed water for different industrial uses. Results showed a 41% risk of violation from the effluent standard limit in the MIC WWTP. In addition, analyzing the risk factors revealed that the human error had the largest contribution to the risk calculation of the WWTP failure. In this regard, a number of mitigation measures were proposed to improve the reliability of the WWTP.

Indexed as

WastewaterEnvironmental MonitoringHumansIndustryIranProbabilityRecyclingReproducibility of ResultsRisk AssessmentWaste Disposal, FluidWater PurificationWater QualityWastewaterBow-tie methodReclaimed waterRisk assessmentWastewater treatment plant

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.