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Dead-end filtration of yeast suspensions: correlating specific resistance and flux data using artificial neural networks

Ní Mhurchú, Jenny and Foley, Greg orcid logoORCID: 0000-0002-2284-4218 (2006) Dead-end filtration of yeast suspensions: correlating specific resistance and flux data using artificial neural networks. Journal of Membrane Science, 281 (1-2). pp. 325-333. ISSN 0376-7388

The specific cake resistance in dead-end filtration is a complex function of suspension properties and operating conditions. In this study, the specific resistance of resuspended dried bakers yeast suspensions was measured in a series of 150 experiments covering a range of pressures, cell concentrations, pHs, ionic strengths and membrane resistances. The specific resistance was found to increase linearly with pressure and exhibited a complex dependence on pH and ionic strength. The specific resistance data were correlated using an artificial neural network containing a single hidden layer with nine neurons employing the sigmoidal activation function. The network was trained with 104 training points, 13 validation points and 33 test points. Excellent agreement was obtained between the neural network and the test data with average errors of less than 10%. In addition, a network was trained for prediction of the filtrate flux directly from the system inputs and this approach is easily extended to crossflow filtration by adding inputs such as the crossflow velocity and channel height. An attempt was made to interpret the network weights for both the specific resistance and flux networks. The effective contribution of each input to the system output was computed in each case and showed trends that were as expected. Although network weights, and consequently the computed effect of each parameter, is different each time a network is changed (depending on the initial weights used in the training process), the variation was low enough for information contained in the network to be interpreted in a meaningful way.
Item Type:Article (Published)
Uncontrolled Keywords:dead-end filtration; specific cake resistance; artificial neural network; bakers yeast;
Subjects:Biological Sciences > Biotechnology
Mathematics > Mathematical models
Computer Science > Algorithms
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Biotechnology
Research Institutes and Centres > National Institute for Cellular Biotechnology (NICB)
Official URL:http://dx.doi.org/10.1016/j.memsci.2006.03.043
Copyright Information:Copyright © 2006 Elsevier B.V.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:15767
Deposited On:01 Nov 2010 13:37 by Jenny Lawler . Last Modified 23 Mar 2021 11:14

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