A Soft Sensor Modelling of Biomass Concentration during Fermentation using Accurate Incremental Online ν-Support Vector Regression Learning Algorithm
Binjie Gu and Feng Pan
DOI : 10.3844/ajbbsp.2015.149.159
American Journal of Biochemistry and Biotechnology
Volume 11, Issue 3
In order to model real fermentation process, a soft sensor modelling of biomass concentration during fermentation using accurate incremental online ν-Support Vector Regression (ν-SVR) learning algorithm was proposed. Firstly, an accurate incremental online ν-SVR learning algorithm was proposed. This algorithm solved the two complications introduced in the dual problem based on the equivalent formulation of ν-SVR. Moreover, it addressed the infeasible updating path problem during the adiabatic incremental process by relaxed adiabatic incremental adjustments and accurate incremental adjustments. Then, the proposed algorithm is used to predict the biomass concentration of glutamic acid fed-batch fermentation process online. The results of simulation experiment showed that the soft sensor modelling of biomass concentration during fermentation using the proposed algorithm was of better generalization ability and cost less training time than that of ν-SVR.
© 2015 Binjie Gu and Feng Pan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.