eprintid: 17015
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/70/15
datestamp: 2023-12-19 03:23:29
lastmod: 2023-12-19 03:23:29
status_changed: 2023-12-19 03:07:18
type: article
metadata_visibility: show
creators_name: Irfan, M.
creators_name: Waqas, S.
creators_name: Arshad, U.
creators_name: Khan, J.A.
creators_name: Legutko, S.
creators_name: Kruszelnicka, I.
creators_name: Ginter-Kramarczyk, D.
creators_name: Rahman, S.
creators_name: Skrzypczak, A.
title: Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment
ispublished: pub
keywords: Biological water treatment; Bioreactors; Energy efficiency; Membrane fouling; Microfiltration; Neural networks; Rotating disks; Surface properties; Wastewater treatment, Artificial neural network; Artificial neural network modeling; Attached growth; Attached growth process; Disk rotational speed; Growth process; Operating parameters; Response surface methodology; Response-surface methodology; Rotating biological contactor, Membranes
note: cited By 15
abstract: Membrane fouling is a major hindrance to widespread wastewater treatment applications. This study optimizes operating parameters in membrane rotating biological contactors (MRBC) for maximized membrane fouling through Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). MRBC is an integrated system, embracing membrane filtration and conventional rotating biological contactor in one individual bioreactor. The filtration performance was optimized by exploiting the three parameters of disk rotational speed, membrane-to-disk gap, and organic loading rate. The results showed that both the RSM and ANN models were in good agreement with the experimental data and the modelled equation. The overall R2 value was 0.9982 for the proposed network using ANN, higher than the RSM value (0.9762). The RSM model demonstrated the optimum operating parameter values of a 44 rpm disk rotational speed, a 1.07 membrane-to-disk gap, and a 10.2 g COD/m2 d organic loading rate. The optimization of process parameters can eliminate unnecessary steps and automate steps in the process to save time, reduce errors and avoid duplicate work. This work demonstrates the effective use of statistical modeling to enhance MRBC system performance to obtain a sustainable and energy-efficient treatment process to prevent human health and the environment. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126302515&doi=10.3390%2fma15051932&partnerID=40&md5=34e1545524fd2564d693ca239be7fa4c
id_number: 10.3390/ma15051932
full_text_status: none
publication: Materials
volume: 15
number: 5
refereed: TRUE
issn: 19961944
citation:   Irfan, M. and Waqas, S. and Arshad, U. and Khan, J.A. and Legutko, S. and Kruszelnicka, I. and Ginter-Kramarczyk, D. and Rahman, S. and Skrzypczak, A.  (2022) Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment.  Materials, 15 (5).   ISSN 19961944