| Title: | Mathematical model-based optimization of trace metal dosage in anaerobic batch bioreactors |
|---|
| Authors: | ID Kegl, Tina (Author)
ID Paramasivan, Balasubramanian (Author)
ID Maharaj, Bikash Chandra (Author) |
|---|
| Files: |  bioengineering-12-00117-v2.pdf (4,66 MB)
MD5: 32CFA929A9FBB8C1C1B21699F677A718
|
|---|
| Language: | English |
|---|
| Work type: | Article |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FKKT - Faculty of Chemistry and Chemical Engineering
|
|---|
| Abstract: | Anaerobic digestion (AD) is a promising and yet a complex waste-to-energy technology. To optimize such a process, precise modeling is essential. Developing complex, mechanistically inspired AD models can result in an overwhelming number of parameters that require calibration. This study presents a novel approach that considers the role of trace metals (Ca, K, Mg, Na, Co, Cr, Cu, Fe, Ni, Pb, and Zn) in the modeling, numerical simulation, and optimization of the AD process in a batch bioreactor. In this context, BioModel is enhanced by incorporating the influence of metal activities on chemical, biochemical, and physicochemical processes. Trace metal-related parameters are also included in the calibration of all model parameters. The model’s reliability is rigorously validated by comparing simulation results with experimental data. The study reveals that perturbations of 5% in model parameter values significantly increase the discrepancy between simulated and experimental results up to threefold. Additionally, the study highlights how precise optimization of metal additives can enhance both the quantity and quality of biogas production. The optimal concentrations of trace metals increased biogas and CH4 production by 5.4% and 13.5%, respectively, while H2, H2S, and NH3 decreased by 28.2%, 43.6%, and 42.5%, respectively. |
|---|
| Keywords: | anaerobic digestion, batch bioreactor, methane production, model parameters calibration, active set optimization method, perturbation of model parameter, gradient based optimization, trace metals |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Submitted for review: | 21.01.2025 |
|---|
| Article acceptance date: | 23.01.2025 |
|---|
| Publication date: | 26.01.2025 |
|---|
| Publisher: | MDPI |
|---|
| Year of publishing: | 2025 |
|---|
| Number of pages: | 22 str. |
|---|
| Numbering: | Vol. 12, no. 2, [art. no.] 117 |
|---|
| PID: | 20.500.12556/DKUM-91723  |
|---|
| UDC: | 66 |
|---|
| ISSN on article: | 2306-5354 |
|---|
| COBISS.SI-ID: | 224480003 |
|---|
| DOI: | 10.3390/bioengineering12020117 |
|---|
| Copyright: | © 2025 by the authors |
|---|
| Publication date in DKUM: | 30.01.2025 |
|---|
| Views: | 208 |
|---|
| Downloads: | 7 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
