Cell mass concentration variance resulting from fluctuations in the incoming nutrient concentration to a chemostat

1985 ◽  
Vol 47 (3) ◽  
pp. 337-342 ◽  
Author(s):  
M. M. Domach ◽  
R. Armstrong ◽  
M. S. Jhon
Keyword(s):  
Mass Concentration ◽  
Nutrient Concentration ◽  
Cell Mass ◽  
Concentration Variance ◽  
Cell Mass Concentration

scholarly journals Measurement of cell mass concentration with a continuous-flow viscometer

1979 ◽  
Vol 21 (3) ◽  
pp. 519-523 ◽  
Author(s):  
Christopher R. Perley ◽  
James R. Swartz ◽  
Charles L. Cooney
Keyword(s):  
Continuous Flow ◽  
Mass Concentration ◽  
Cell Mass ◽  
Cell Mass Concentration

Computer control of cell mass concentration in continuous culture

Automatica ◽  
1989 ◽  
Vol 25 (2) ◽  
pp. 243-249 ◽  
Author(s):  
Gyeongbeom Yi ◽  
Young Bo Hwang ◽  
Ho Nam Chang ◽  
Kwang Soon Lee
Keyword(s):  
Continuous Culture ◽  
Mass Concentration ◽  
Computer Control ◽  
Cell Mass ◽  
Cell Mass Concentration

Neural Network Model to Estimate and Predict Cell Mass Concentration in Lipase Fermentation

2014 ◽  
pp. 303-316
Author(s):  
David K. Daniel ◽  
Vikramaditya Bhandari
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Mass Concentration ◽  
Cell Mass ◽  
Back Propagation ◽  
Activation Function ◽  
Training Data ◽  
Network Configuration ◽  
Cell Mass Concentration

Lipase is an industrially important enzyme with major use in food industries. The demand of lipase is increasing every year. An online prediction of cell mass concentration is of great value in real time process involving the production of lipase. In the current work, the use of a back-propagation multilayer neural network to predict cell mass during lipase production by Rhizopus delemar NRRL 1472 is targeted. Network training data with respect to time is generated by carrying out experiments in laboratory. The fungus is grown in erlenmeyer flasks at initial pH of 5.6, temperature of 30ºC, and at 150 rpm. During the experiments, readings for cell mass growth are collected in specific period of time. By the training data, an artificial neural network model programmed in MATLAB for Windows is trained and used for prediction of cell mass. The Levenberg-Marquardt algorithm with back-propagation is used in the network to get the optimized weights. The optimum network configuration with different activation function and the number of nodes in the hidden layer are identified by trial and error method. Sigmoid unipolar activation function is 2-5-1, whereas logarithmoid and sigmoid bipolar is 2-3-1. These are chosen according to the values of Sum of Square of Errors (SSE), Root Mean Square (RMS) training and testing. The sigmoid unipolar activation function gives a good fit for estimated value with network configuration 2-5-1, which could be used for generalization.

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