Energy balance survey for the design and auto-thermal thermophilic aerobic digestion of algal-based membrane bioreactor for Landfill Leachate Treatment(under organic loading rates): Experimental and simulation-based ANN and NSGA-II.

Algal-based membrane bioreactor Artificial neural network Biomass production Chemical oxygen demand Organic loading rates Wastewater treatment (landfill leachate)

Journal

Chemosphere
ISSN: 1879-1298
Titre abrégé: Chemosphere
Pays: England
ID NLM: 0320657

Informations de publication

Date de publication:
Jan 2024
Historique:
received: 14 06 2023
revised: 12 08 2023
accepted: 06 11 2023
medline: 6 12 2023
pubmed: 16 11 2023
entrez: 15 11 2023
Statut: ppublish

Résumé

Although algal-based membrane bioreactors (AMBRs) have been demonstrated to be effective in treating wastewater (landfill leachate), there needs to be more research into the effectiveness of these systems. This study aims to determine whether AMBR is effective in treating landfill leachate with hydraulic retention times (HRTs) of 8, 12, 14, 16, 21, and 24 h to maximize AMBR's energy efficiency, microalgal biomass production, and removal efficiency using artificial neural network (ANN) models. Experimental results and simulations indicate that biomass production in bioreactors depends heavily on HRT. A decrease in HRT increases algal (Chlorella vulgaris) biomass productivity. Results also showed that 80% of chemical oxygen demand (COD) was removed from algal biomass by bioreactors. To determine the most efficient way to process the features as mentioned above, nondominated sorting genetic algorithm II (NSGA-II) techniques were applied. A mesophilic, suspended-thermophilic, and attached-thermophilic organic loading rate (OLR) of 1.28, 1.06, and 2 kg/m

Identifiants

pubmed: 37967679
pii: S0045-6535(23)02922-3
doi: 10.1016/j.chemosphere.2023.140652
pii:
doi:

Substances chimiques

Water Pollutants, Chemical 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

140652

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Mehdi Rahimi Asrami (MR)

Department of Chemical Engineering, University of Science and Technology of Mazandaran, P. O. Box: 48518-78195, Behshahr, Mazandaran, Iran.

Ali Pirouzi (A)

Department of Chemical Engineering, University of Science and Technology of Mazandaran, P. O. Box: 48518-78195, Behshahr, Mazandaran, Iran. Electronic address: pirouzi_ali@yahoo.com.

Mohsen Nosrati (M)

Biotechnology Group, Faculty of Chemical Engineering, Tarbiat Modares University, P. O. Box: 14115-143, Tehran, Tehran, Iran.

Abolfazl Hajipour (A)

Biotechnology Group, Faculty of Chemical Engineering, Tarbiat Modares University, P. O. Box: 14115-143, Tehran, Tehran, Iran.

Sasan Zahmatkesh (S)

Tecnologico de Monterrey, Escuela de Ingenieríay Ciencias, Puebla, Mexico; Faculty of Health and Life Sciences, INTI International University, 71800, Nilai, Negeri Sembilan, Malaysia.

Articles similaires

Unsupervised learning for real-time and continuous gait phase detection.

Dollaporn Anopas, Yodchanan Wongsawat, Jetsada Arnin
1.00
Humans Gait Neural Networks, Computer Unsupervised Machine Learning Walking
Humans Shoulder Fractures Tomography, X-Ray Computed Neural Networks, Computer Female
Humans Artificial Intelligence Neoplasms Prognosis Image Processing, Computer-Assisted
Nigeria Environmental Monitoring Solid Waste Waste Disposal Facilities Refuse Disposal

Classifications MeSH