Abstract
Because of the time-varying characteristics of 2-keto-l-gulonic acid (2-KGA) fermentation processes, phase identification–based prediction of product formation is presented in this paper. Firstly, based on the online measurement data of fermentation processes, fermentation stage is identified with the modified principal component analysis method. The state variable such as the product formation is predicted using the support vector machine (SVM) approach. The obtained stage information is fed back to the state variable prediction model. In different fermentation phases, the training databases and parameters of SVM for the prediction model are applied differently. With the data from industrial 2-KGA fermentation processes, pseudo-online prediction is practiced. The results indicate that the prediction approach has good performance.
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References
Aiguo J, Peiji G (1998) Synthesis of 2-keto-L-gulonic acid from gluconic acid by co-immobilized Gluconobacter oxydans and Corynebacterium sp. Biotechnol Lett 20(10):939–942
Bremus C, Herrmann U, Bringer-Meyer S, Sahm H (2006) The use of microorganisms in L-ascorbic acid production. J Biotechnol 124(1):196–205
Browne MW (2000) Cross-validation methods. J Math Psychol 44(1):108–132
Cedeno MV, Rodriguez Aguilar LPF, Sanchez MC (2016) Bioprocess statistical control: identification stage based on hierarchical clustering. Process Biochem 51(12):1919–1929
Ge Z, Song Z, Gao F (2013) Review of recent research on data-based process monitoring. Ind Eng Chem Res 52(10):3543–3562
Hancock RD, Viola R (2002) Biotechnological approaches for L-ascorbic acid production. Trends Biotechnol 20(7):299–305
León-Roque N, Abderrahim M, Nuñez-Alejos L, Arribas SM, Condezo-Hoyos L (2016) Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks. Talanta 161:31–39
Liu G, Zhou D, Xu H, Mei C (2010) Model optimization of SVM for a fermentation soft sensor. Expert Syst Appl 37(4):2708–2713
Luttmann R, Bracewell DG, Cornelissen G, Gernaey KV, Glassey J, Hass VC, Kaiser C, Preusse C, Striedner G, Mandenius CF (2012) Soft sensors in bioprocessing: a status report and recommendations. Biotechnol J 7(8):1040–1048
Ning WZ, Tao ZX, Wang CH, Wang SD, Yan ZZ, Yin GL (1988) Fermentation process for producing 2-keto-L-gulonic acid. European Patent 0278447
Rosales-Colunga LM, García RG, Rodríguez ADL (2010) Estimation of hydrogen production in genetically modified E. coli fermentations using an artificial neural network. Int J Hydrog Energy 35(24):13186–13192
Wang X, Chen J, Liu C, Pan F (2010) Hybrid modeling of penicillin fermentation process based on least square support vector machine. Chem Eng Res Des 88(4):415–420
Yao Y, Gao F (2009) A survey on multistage/multiphase statistical modeling methods for batch processes. Annu Rev Control 33(2):172–183
Yin S, Li X, Gao H (2015) Data-based techniques focused on modern industry: an overview. IEEE Trans Ind Electron 62(1):657–667
Yuan JQ, Vanrolleghem PA (1999) Rolling learning-prediction of product formation in bioprocesses. J Biotechnol 69(1):47–62
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Cui, L., Xu, T. & Wang, Z. Phase Identification–Based Prediction of Product Formation for 2-Keto-l-Gulonic Acid Fermentation Processes. Process Integr Optim Sustain 3, 341–347 (2019). https://doi.org/10.1007/s41660-018-0077-7
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DOI: https://doi.org/10.1007/s41660-018-0077-7