Fermentation database mining by pattern recognition

✍ Scribed by Gregory Stephanopoulos; Georg Locher; Michael J. Duff; Roy Kamimura; George Stephanopoulos

• Publisher: John Wiley and Sons
• Year: 1997
• Tongue: English
• Weight: 358 KB
• Volume: 53
• Category: Article
• ISSN: 0006-3592
• DOI: 10.1002/(sici)1097-0290(19970305)53:5<443::aid-bit1>3.0.co;2-h

✦ Synopsis

A large volume of data is routinely collected during the course of typical fermentation and other processes. Such data provide the required basis for process documentation and occasionally are also used for process analysis and improvement. The information density of these data is often low, and automatic condensing, analysis, and interpretation (''database mining'') are highly desirable. In this article we present a methodology whereby process variables are processed to create a database of derivative process quantities representative of the global patterns, intermediate trends, and local characteristics of the process. A powerful search algorithm subsequently attempts to extract the specific process variables and their particular attributes that uniquely characterize a class of process outcomes such as high-or low-yield fermentations.

The basic components of our pattern recognition methodology are described along with applications to the analysis of two sets of data from industrial fermentations. Results indicate that truly discriminating variables do exist in typical fermentation data and they can be useful in identifying the causes or symptoms of different process outcomes. The methodology has been implemented in a user-friendly software, named dbminer, which facilitates the application of the methodol- ogy for efficient and speedy analysis of fermentation process data.