| Title |
Estimation of optimal feeding strategies for fed-batch bioprocesses
|
|---|---|
| Published in |
Bioprocess and Biosystems Engineering, June 2005
|
| DOI | 10.1007/s00449-005-0415-3 |
| Pubmed ID | |
| Authors |
Ezequiel Franco-Lara, Dirk Weuster-Botz |
| Abstract |
A generic methodology for feeding strategy optimization is presented. This approach uses a genetic algorithm to search for optimal feeding profiles represented by means of artificial neural networks (ANN). Exemplified on a fed-batch hybridoma cell cultivation, the approach has proven to be able to cope with complex optimization tasks handling intricate constraints and objective functions. Furthermore, the performance of the method is compared with other previously reported standard techniques like: (1) optimal control theory, (2) first order conjugate gradient, (3) dynamical programming, (4) extended evolutionary strategies. The methodology presents no restrictions concerning the number or complexity of the state variables and therefore constitutes a remarkable alternative for process development and optimization. |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 2 | 4% |
| Canada | 2 | 4% |
| United Arab Emirates | 1 | 2% |
| Chile | 1 | 2% |
| Mexico | 1 | 2% |
| Unknown | 43 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 22% |
| Student > Ph. D. Student | 8 | 16% |
| Student > Master | 6 | 12% |
| Student > Bachelor | 5 | 10% |
| Professor | 4 | 8% |
| Other | 12 | 24% |
| Unknown | 4 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 19 | 38% |
| Agricultural and Biological Sciences | 14 | 28% |
| Chemical Engineering | 5 | 10% |
| Biochemistry, Genetics and Molecular Biology | 2 | 4% |
| Medicine and Dentistry | 2 | 4% |
| Other | 4 | 8% |
| Unknown | 4 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 May 2014.
All research outputs
#22,830,981
of 25,457,858 outputs
Outputs from Bioprocess and Biosystems Engineering
#8
of 8 outputs
Outputs of similar age
#66,500
of 68,301 outputs
Outputs of similar age from Bioprocess and Biosystems Engineering
#3
of 3 outputs
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So far Altmetric has tracked 8 research outputs from this source. They receive a mean Attention Score of 2.2. This one scored the same or higher as 0 of them.
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We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.