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ijpcip

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About the Journal

International Journal of Prevention and Control of Industrial Pollution [Applied(e)] is a peer-reviewed open-access journal of engineering and scientific journals launched in 2015 which provides its readers with a swift yet complete awareness of the field of prevention and control of industrial pollution. The focus of the journal is on recent advancements in technologies like sustainable industrial design, remediation techniques, wastewater quality indicators, and other related fields. Journal is a peer-reviewed journal that publishes original research articles both experimental and theoretical, review articles, and relevant short communications

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Journal Particulars

Title
International Journal of Prevention and Control of Industrial Pollution

Journal Abbreviation
ijpcip

Publisher

Copyright

Subject
Chemical Engineering

Language
English

Publication Format

Copyright Licensing Policy

Type of Publication
Peer-reviewed Journal (Refereed Journal)

Website
https://journals.stmjournals.com/ijpcip

Address

Principal Contact
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Special Issue Topic

Editors Overview

maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

Open Access
Special Issue
Topic

Prediction of wastewater quality indicators

Abstract Submission Deadline : 30/11/2023

Manuscript Submission Deadline : 25/12/2023

Special Issue Description

Models created utilizing data mining techniques are suggested for forecasting the following wastewater quality indicators: biochemical and chemical oxygen demand, total suspended particles, total nitrogen, and total phosphorus at the intake to the wastewater treatment plant (WWTP). The models are built using data from prior time steps and daily wastewater intakes. Additionally, separate prediction systems are offered, which can be employed if the monitoring equipment stops working. Artificial neural networks (ANN) of the multilayer perceptron type coupled with the categorization model (SOM), as well as cascading neural networks, were used to construct models of wastewater quality indicators (CNN).ANN+SOM produced the lowest absolute and relative error values, whereas the MARS technique yielded the greatest error values. It was demonstrated that using the two created independent prediction methods, it is possible to get continuous predictions of particular wastewater quality indicators for the examined WWTP. When wastewater quality monitoring systems are down for repair or become inoperative, such models can guarantee dependable WWTP operation.

Keywords

Quality indicators
Phosphorus
Artificial neural networks
Wastewater
Biochemical
Chemical oxygen demand
Nitrogen
Multilayer perceptron

Manuscript Submission information

Manuscripts should be submitted online via the manuscript Engine. Once you register on APID, click here to go to the submission form. Manuscripts can be submitted until the deadline.
All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the email address:[email protected] for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a Double-blind peer-review process. A guide for authors and other relevant information for the submission of manuscripts is available on the Instructions for Authors page.

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Published articles

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