Open AccessData Mining for Bioprocess Optimization
Author(s) -
Rommel S.,
Schuppert A.
Publication year - 2004
Publication title -
engineering in life sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.547
H-Index - 57
eISSN - 1618-2863
pISSN - 1618-0240
DOI - 10.1002/elsc.200420059
Subject(s) - troubleshooting , process (computing) , bioprocess , computer science , data mining , process optimization , component (thermodynamics) , process control , biochemical engineering , process engineering , engineering , physics , chemical engineering , environmental engineering , thermodynamics , operating system
Although developed for completely different applications, the great technological potential of data analysis methods called “data mining” has increasingly been realized as a method for efficiently analyzing potentials for optimization and for troubleshooting within many application areas of process, technology. This paper presents the successful application of data mining methods for the optimization of a fermentation process, and discusses diverse characteristics of data mining for biological processes. For the optimization of biological processes a huge amount of possibly relevant process parameters exist. Those input variables can be parameters from devices as well as process control parameters. The main challenge of such optimizations is to robustly identify relevant combinations of parameters among a huge amount of process parameters. For the underlying process we found with the application of data mining methods, that the moment a special carbohydrate component is added has a strong impact on the formation of secondary components. The yield could also be increased by using 2 m 3 fermentors instead of 1 m 3 fermentors.