Predictive modelling of methane yield in biochar-amended cheese whey and septage co-digestion: Exploring synergistic effects using Gompertz and neural networks.
Artificial neural network
Biogas production rate
Optimisation
Septage-derived biochar
Total solids
Journal
Chemosphere
ISSN: 1879-1298
Titre abrégé: Chemosphere
Pays: England
ID NLM: 0320657
Informations de publication
Date de publication:
26 Feb 2024
26 Feb 2024
Historique:
received:
23
11
2023
revised:
10
02
2024
accepted:
24
02
2024
medline:
29
2
2024
pubmed:
29
2
2024
entrez:
28
2
2024
Statut:
aheadofprint
Résumé
This study performed bench scale studies on anaerobic co-digestion of cheese whey and septage mixed with biochar (BC) as additive at various dosages (0.5 g, 1 g, 2 g and 4 g) and total solids (TS) concentrations (5%, 7.5%, 10%,12.5% and 15%). The experimental results revealed 29.58% increase in methane yield (486 ± 11.32 mL/gVS) with 27% reduction in lag phase time at 10% TS concentration and 50 g/L of BC loading. The mechanistic investigations revealed that BC improved process stability by virtue of its robust buffering capacity and mitigated ammonia inhibition. Statistical analysis indicates BC dosage had a more pronounced effect (P < 0.0001) compared to the impact of TS concentrations. Additionally, the results were modelled using Gompertz model (GM) and artificial neural network (ANN) algorithm, which revealed the outperformance of ANN over GM with MSE 17.96, R
Identifiants
pubmed: 38417486
pii: S0045-6535(24)00451-X
doi: 10.1016/j.chemosphere.2024.141558
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
141558Informations de copyright
Copyright © 2024. Published by Elsevier Ltd.
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.