Dynamic Parameters Estimation Method: Advanced Manometric Temperature Measurement Approach for Freeze-Drying Monitoring of Pharmaceutical Solutions

2008 ◽  
Vol 47 (21) ◽  
pp. 8445-8457 ◽  
Author(s):  
Salvatore A. Velardi ◽  
Valeria Rasetto ◽  
Antonello A. Barresi
Keyword(s):  
Temperature Measurement ◽  
Freeze Drying ◽  
Estimation Method ◽  
Parameters Estimation ◽  
Dynamic Parameters ◽  
Manometric Temperature Measurement ◽  
Measurement Approach

scholarly journals Cycle Development in a Mini-Freeze Dryer: Evaluation of Manometric Temperature Measurement in Small-Scale Equipment

2021 ◽  
Vol 22 (4) ◽  
Author(s):  
Tim Wenzel ◽  
Margit Gieseler ◽  
Ahmad M. Abdul-Fattah ◽  
Henning Gieseler
Keyword(s):  
Temperature Measurement ◽  
Temperature Control ◽  
Process Parameters ◽  
Wall Temperature ◽  
Freeze Drying ◽  
Small Scale ◽  
Residual Moisture ◽  
Product Temperature ◽  
Manometric Temperature Measurement ◽  
Freeze Dryer

AbstractThe objective of this research was to assess the applicability of manometric temperature measurement (MTM) and SMART™ for cycle development and monitoring of critical product and process parameters in a mini-freeze dryer using a small set of seven vials. Freeze drying cycles were developed using SMART™ which automatically defines and adapts process parameters based on input data and MTM feedback information. The freeze drying behavior and product characteristics of an amorphous model system were studied at varying wall temperature control settings of the cylindrical wall surrounding the shelf in the mini-freeze dryer. Calculated product temperature profiles were similar for all different wall temperature settings during the MTM-SMART™ runs and in good agreement with the temperatures measured by thermocouples. Product resistance profiles showed uniformity in all of the runs conducted in the mini-freeze dryer, but absolute values were slightly lower compared to values determined by MTM in a LyoStar™ pilot-scale freeze dryer. The resulting cakes exhibited comparable residual moisture content and optical appearance to the products obtained in the larger freeze dryer. An increase in intra-vial heterogeneity was found for the pore morphology in the cycle with deactivated wall temperature control in the mini-freeze dryer. SMART™ cycle design and product attributes were reproducible and a minimum load of seven 10R vials was identified for more accurate MTM values. MTM-SMART™ runs suggested, that in case of the wall temperature following the product temperature of the center vial, product temperatures differ only slightly from those in the LyoStar™ freeze dryer.

scholarly journals Experimental Evaluation of PSO Based Transfer Learning Method for Meteorological Visibility Estimation

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 828
Author(s):  
Wai Lun Lo ◽  
Henry Shu Hung Chung ◽  
Hong Fu
Keyword(s):  
Transfer Learning ◽  
Experimental Evaluation ◽  
Estimation Method ◽  
Image Features ◽  
Parameters Estimation ◽  
Image Feature ◽  
Support Vector ◽  
Learning Method ◽  
Feature Vectors ◽  
Visibility Estimation

Estimation of Meteorological visibility from image characteristics is a challenging problem in the research of meteorological parameters estimation. Meteorological visibility can be used to indicate the weather transparency and this indicator is important for transport safety. This paper summarizes the outcomes of the experimental evaluation of a Particle Swarm Optimization (PSO) based transfer learning method for meteorological visibility estimation method. This paper proposes a modified approach of the transfer learning method for visibility estimation by using PSO feature selection. Image data are collected at fixed location with fixed viewing angle. The database images were gone through a pre-processing step of gray-averaging so as to provide information of static landmark objects for automatic extraction of effective regions from images. Effective regions are then extracted from image database and the image features are then extracted from the Neural Network. Subset of Image features are selected based on the Particle Swarming Optimization (PSO) methods to obtain the image feature vectors for each effective sub-region. The image feature vectors are then used to estimate the visibilities of the images by using the Multiple Support Vector Regression (SVR) models. Experimental results show that the proposed method can give an accuracy more than 90% for visibility estimation and the proposed method is effective and robust.

Hybrid FE–IDQ–CG method for dynamic parameters estimation of multilayered half-space

2014 ◽  
Vol 56 ◽  
pp. 74-82 ◽  
Author(s):  
A.R. Vosoughi ◽  
Mo.R. Banan ◽  
Ma.R. Banan ◽  
P. Malekzadeh
Keyword(s):  
Half Space ◽  
Parameters Estimation ◽  
Dynamic Parameters
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