Water Cycle Algorithm for Modelling of Fermentation Processes
Abstract
:1. Introduction
2. Materials and Methods
2.1. Fermentation Processes
2.1.1. Escherichia Coli Fed-Batch Fermentation Process
2.1.2. S. cerevisiae Fed-Batch Fermentation Process
2.2. Metaheuristic Algorithms
2.2.1. Genetic Algorithm
2.2.2. Water Cycle Algorithm
3. Results and Discussion
3.1. Algorithms Implementation for Model Parameter Identification
- Case study 1: Parameters identification of an E. coli fed-batch FP model, presented by Equations (1)–(3) with an optimisation criterion expressed by Equation (8); and
- Case study 2: Parameters identification of a S. cerevisiae fed-batch FP model, presented by Equations (4)–(7) with an optimisation criterion expressed by Equation (8).
3.2. Case Study 1: E. coli Fed-Batch Fermentation Process
3.3. Case Study 2: S. cerevisiae Fed-Batch Fermentation Process
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Parameter | GA | WCA |
---|---|---|
) | 200 | 200 |
) | - | 50 |
Generation gap (GGAP) | 0.97 */0.8 ** | - |
Maximal number of iterations (Iter_Max) | 400 | 400 |
) | 0.7 */0.95 ** | - |
) | 0.05 */0.1 ** | - |
) | - | 1 × 10–15 |
GA | WCA | |||||||
---|---|---|---|---|---|---|---|---|
Parameter | Best | Worst | CI | SD | Best | Worst | CI | SD |
J | 4.392 | 4.535 | 4.468 ± 0.0218 | 0.0363 | 4.222 | 4.477 | 4.352 ± 0.0437 | 0.0727 |
, [h−1] | 0.489 | 0.493 | 0.4898 ± 0.0018 | 0.0030 | 0.478 | 0.501 | 0.4875 ± 0.0054 | 0.0091 |
, [g·L−1] | 0.012 | 0.013 | 0.0123 ± 0.0003 | 0.0005 | 0.010 | 0.015 | 0.0116 ± 0.0010 | 0.0017 |
, [g·g−1] | 2.019 | 2.019 | 2.0206 ± 0.0007 | 0.0012 | 2.018 | 2.019 | 2.0194 ± 0.0006 | 0.0011 |
GA | WCA | |||||||
---|---|---|---|---|---|---|---|---|
Parameter | Best | Worst | CI | SD | Best | Worst | CI | SD |
J | 1.338 | 5.107 | 1.508 ± 0.4299 | 0.7155 | 1.326 | 1.371 | 1.345 ± 0.0081 | 0.0134 |
, [h−1] | 0.839 | 1.000 | 0.9748 ± 0.0223 | 0.0371 | 0.594 | 1.000 | 0.8284 ± 0.1042 | 0.1735 |
, [h−1] | 0.093 | 0.224 | 0.1129 ± 0.0143 | 0.0239 | 0.050 | 0.124 | 0.0930 ± 0.0146 | 0.0243 |
, [g·L−1] | 0.074 | 0.143 | 0.1180 ± 0.0066 | 0.0110 | 0.050 | 0.128 | 0.0928 ± 0.0193 | 0.0321 |
, [g·L−1] | 0.472 | 0.995 | 0.8762 ± 0.0691 | 0.1150 | 0.128 | 1.000 | 0.6918 ± 0.1896 | 0.3155 |
, [g·g−1] | 2.093 | 2.276 | 2.2222 ± 0.0158 | 0.0262 | 2.224 | 2.231 | 2.2275 ± 0.0011 | 0.0019 |
, [g·g−1] | 1.202 | 4.459 | 1.3956 ± 0.3498 | 0.5821 | 1.169 | 1.300 | 1.2389 ± 0.0210 | 0.0350 |
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Roeva, O.; Angelova, M.; Zoteva, D.; Pencheva, T. Water Cycle Algorithm for Modelling of Fermentation Processes. Processes 2020, 8, 920. https://0-doi-org.brum.beds.ac.uk/10.3390/pr8080920
Roeva O, Angelova M, Zoteva D, Pencheva T. Water Cycle Algorithm for Modelling of Fermentation Processes. Processes. 2020; 8(8):920. https://0-doi-org.brum.beds.ac.uk/10.3390/pr8080920
Chicago/Turabian StyleRoeva, Olympia, Maria Angelova, Dafina Zoteva, and Tania Pencheva. 2020. "Water Cycle Algorithm for Modelling of Fermentation Processes" Processes 8, no. 8: 920. https://0-doi-org.brum.beds.ac.uk/10.3390/pr8080920