Cell culture process monitoring and control?a key to process optimization

1994 ◽  
Vol 14 (3) ◽  
pp. 155-156 ◽  
Author(s):  
Wei-Shou Hu
Author(s):  
Letha Chemmalil ◽  
Dhanuka P. Wasalathanthri ◽  
Xin Zhang ◽  
June Kuang ◽  
Chun Shao ◽  
...  

2012 ◽  
Vol 59 (1) ◽  
Author(s):  
Mohd Helmi Sani ◽  
Frank Baganz

At present, there are a number of commercial small scale shaken systems available on the market with instrumented controllable microbioreactors such as Micro–24 Microreactor System (Pall Corporation, Port Washington, NY) and M2P Biolector, (M2P Labs GmbH, Aachen, Germany). The Micro–24 system is basically an orbital shaken 24–well plate that operates at working volume 3 – 7 mL with 24 independent reactors (deep wells, shaken and sparged) running simultaneously. Each reactor is designed as single use reactor that has the ability to continuously monitor and control the pH, DO and temperature. The reactor aeration is supplied by sparging air from gas feeds that can be controlled individually. Furthermore, pH can be controlled by gas sparging using either dilute ammonia or carbon dioxide directly into the culture medium through a membrane at the bottom of each reactor. Chen et al., (2009) evaluated the Micro–24 system for the mammalian cell culture process development and found the Micro–24 system is suitable as scaledown tool for cell culture application. The result showed that intra-well reproducibility, cell growth, metabolites profiles and protein titres were scalable with 2 L bioreactors.


2020 ◽  
Vol 72 (2) ◽  
pp. 259-269 ◽  
Author(s):  
Zhibing Weng ◽  
Jian Jin ◽  
ChunHua Shao ◽  
Huazhong Li

Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalada Rao ◽  
Hui Yang

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.


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