Optimization of operational parameters using RSM, ANN, and SVM in membrane integrated with rotating biological contactor.

Autor: Waqas S; Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia. Electronic address: sharjeel_17000606@utp.edu.my., Harun NY; Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia. Electronic address: noorfidza.yub@utp.edu.my., Arshad U; Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia., Laziz AM; Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia., Sow Mun SL; Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia., Bilad MR; Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link BE1410, Brunei., Nordin NAH; Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia., Alsaadi AS; Chemical Engineering Department, University of Jeddah, Jeddah, 21589, Saudi Arabia.
Jazyk: angličtina
Zdroj: Chemosphere [Chemosphere] 2024 Feb; Vol. 349, pp. 140830. Date of Electronic Publication: 2023 Dec 04.
DOI: 10.1016/j.chemosphere.2023.140830
Abstrakt: Membrane fouling is a critical bottleneck to the widespread adoption of membrane separation processes. It diminishes the membrane permeability and results in high operational energy costs. The current study presents optimizing the operating parameters of a novel rotating biological contactor (RBC) integrated with an external membrane (RBC + ME) that combines membrane technology with an RBC. In the RBC + ME, the membrane panel is placed external to the bioreactor. Response surface methodology (RSM) is applied to optimize the membrane permeability through three operating parameters (hydraulic retention time (HRT), rotational disk speed, and sludge retention time (SRT)). The artificial neural networks (ANN) and support vector machine (SVM) are implemented to depict the statistical modelling approach using experimental data sets. The results showed that all three operating parameters contribute significantly to the performance of the bioreactor. RSM revealed an optimum value of 40.7 rpm disk rotational speed, 18 h HRT and 12.4 d SRT, respectively. An ANN model with ten hidden layers provides the highest R 2 value, while the SVM model with the Bayesian optimizer provides the highest R 2 . RSM, ANN, and SVM models reveal the highest R-square values of 0.97, 0.99, and 0.99, respectively. Machine learning techniques help predict the model based on the experimental results and training data sets.
Competing Interests: Declaration of competing interest The authors declare no conflict of interest.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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