Computer Control of Fermentation With a Vision-Based Sensing System
Ren, Jinliang
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Permalink
https://hdl.handle.net/2142/72463
Description
Title
Computer Control of Fermentation With a Vision-Based Sensing System
Author(s)
Ren, Jinliang
Issue Date
1992
Doctoral Committee Chair(s)
Reid, John F.
Department of Study
Agricultural Engineering
Discipline
Agricultural Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Agriculture, Food Science and Technology
Engineering, Agricultural
Computer Science
Abstract
A computer controlled fermentation system with an on-line vision sensing system was designed and implemented. The system consisted of a central process controller, a bench scale fermentor system, a computer-controlled automatic sampling system and a computer vision sensing system. A fermentor controller, part of the fermentor system, maintained the process variables during the fermentation process. The primary process variables were temperature, stir rate, pH and dissolved oxygen.
The vision sensing system and the associated automatic sampling system were integrated with the fermentation system to provide cellular information, correlated to the cell concentration, cell growth rate, sporulation, on-line and help the process controller to achieve better control. The automatic sampling system took samples from the fermentor, prepared the sample by adding diluent and/or dye if necessary, and delivered the sample to the microscope stage for the vision system to acquire images.
The central process controller (CPC) supervised all other subsystems via digital communications. The CPC informed the sampling system to take samples at defined time intervals or by special request. When the sample was ready, the CPC activated the vision system to begin image acquisition. The images were processed by the vision system afterwards and the feature information was saved. Control decisions were made by the process control program in terms of the processing variable setpoint adjustment.
Experimental evaluation showed that the dilution accuracy of the sampling system was satisfactory, especially at the low dilution ratios. An evaluation of the sampling system and vision system showed that at least 5 images must be acquired for a reliable average cell-count for the sample and the proper concentration of the prepared sample was about 10 to 30 cells per image for yeast.
The supervisory controller could easily apply user defined process control rules to control the fermentation processes without human interaction such that complete system automation was achieved. The vision and automatic sampling system were successfully implemented to supply microscopic information for the complete system control.
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