Structure and Parameter Identification of a Batch Fermentation Process using Non-Linear Modelling

Author(s):  
M. Keulers
2013 ◽  
Vol 06 (06) ◽  
pp. 1350044
Author(s):  
YAN WANG ◽  
XIAOHONG LI ◽  
ENMIN FENG ◽  
ZHILONG XIU

Based on 1,3-propanediol production from batch fermentation of glycerol by Klebsiella pneumoniae, a multistage dynamic system and its parameter identification are discussed in this paper. The batch fermentation process is divided into three stages exhibiting different dynamic behaviors and characteristics, from which a corresponding nonlinear multistage dynamic system is built. We then propose a parameter identification optimization model whose objective function is the average relative error. The model is solved by particle swarm optimization weighted by inertia, and the result shows that the relative error of our proposed model is 2–10% smaller than those of existing models.


2013 ◽  
Vol 35 (1-2) ◽  
pp. 255-278 ◽  
Author(s):  
Stefano Pagano ◽  
Riccardo Russo ◽  
Salvatore Strano ◽  
Mario Terzo

2018 ◽  
Vol 81 (2) ◽  
pp. 167-173 ◽  
Author(s):  
Luciano Rodrigo Lanssanova ◽  
Sebastião do Amaral Machado ◽  
Alexandre Techy de Almeida Garrett ◽  
Izabel Passos Bonete ◽  
Allan Libanio Pelissari ◽  
...  

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242336
Author(s):  
Peter R. Browne ◽  
Carl T. Woods ◽  
Alice J. Sweeting ◽  
Sam Robertson

Representative learning design proposes that a training task should represent informational constraints present within a competitive environment. To assess the level of representativeness of a training task, the frequency and interaction of constraints should be measured. This study compared constraint interactions and their frequencies in training (match simulations and small sided games) with competition environments in elite Australian football. The extent to which constraints influenced kick and handball effectiveness between competition matches, match simulations and small sided games was determined. The constraints of pressure and time in possession were assessed, alongside disposal effectiveness, through an association rule algorithm. These rules were then expanded to determine whether a disposal was influenced by the preceding disposal. Disposal type differed between training and competition environments, with match simulations yielding greater representativeness compared to small sided games. The subsequent disposal was generally more effective in small sided games compared to the match simulations and competition matches. These findings offer insight into the measurement of representative learning designs through the non-linear modelling of constraint interactions. The analytical techniques utilised may assist other practitioners with the design and monitoring of training tasks intended to facilitate skill transfer from preparation to competition.


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