Literature DB >> 26893178

Operator decision support system for integrated wastewater management including wastewater treatment plants and receiving water bodies.

Minsoo Kim1, Yejin Kim2, Hyosoo Kim3, Wenhua Piao1, Changwon Kim4.   

Abstract

An operator decision support system (ODSS) is proposed to support operators of wastewater treatment plants (WWTPs) in making appropriate decisions. This system accounts for water quality (WQ) variations in WWTP influent and effluent and in the receiving water body (RWB). The proposed system is comprised of two diagnosis modules, three prediction modules, and a scenario-based supporting module (SSM). In the diagnosis modules, the WQs of the influent and effluent WWTP and of the RWB are assessed via multivariate analysis. Three prediction modules based on the k-nearest neighbors (k-NN) method, activated sludge model no. 2d (ASM2d) model, and QUAL2E model are used to forecast WQs for 3 days in advance. To compare various operating alternatives, SSM is applied to test various predetermined operating conditions in terms of overall oxygen transfer coefficient (Kla), waste sludge flow rate (Qw), return sludge flow rate (Qr), and internal recycle flow rate (Qir). In the case of unacceptable total phosphorus (TP), SSM provides appropriate information for the chemical treatment. The constructed ODSS was tested using data collected from Geumho River, which was the RWB, and S WWTP in Daegu City, South Korea. The results demonstrate the capability of the proposed ODSS to provide WWTP operators with more objective qualitative and quantitative assessments of WWTP and RWB WQs. Moreover, the current study shows that ODSS, using data collected from the study area, can be used to identify operational alternatives through SSM at an integrated urban wastewater management level.

Entities:  

Keywords:  Decision support system (DSS); Diagnosis; Integrated wastewater management; Mathematical modeling; Multivariate analysis; Receiving water body (RWB)

Mesh:

Substances:

Year:  2016        PMID: 26893178     DOI: 10.1007/s11356-016-6272-6

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  12 in total

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  1 in total

1.  Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia.

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Journal:  Environ Sci Pollut Res Int       Date:  2018-02-17       Impact factor: 4.223

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