"Assumed inherent sensor" inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process

Abstract An artificial neural network (ANN) soft-sensing method, based on the “assumed inherent sensor” and its inversion concepts, is proposed and used to estimate some crucial process variables which would be very difficult to be measured directly. For a real biochemical process whose mathematical model is a general nonlinear dynamic system, one may assume that, in its interior, there exists an “inherent sensor” subsystem whose inputs are exactly the process variables to be estimated while whose outputs are the directly measurable ones. To verify this assumption, this paper presents an algorithm to construct the mathematical model of the “assumed inherent sensor” and furthermore presents a global invertibility condition of the “assumed inherent sensor” which guarantees the existence of the inversion of such an “assumed inherent sensor” in theory. The “assumed inherent sensor” inversion consists of a set of nonlinear functions and a series of differentiators and could be treated as the dynamic soft-sensing model because its outputs are capable of reproducing the input variables of the “assumed inherent sensor”, or the process variables to be estimated. To overcome the difficulty in constructing the above “assumed inherent sensor” inversion in an analytic manner, a static ANN is used to approximate the nonlinear function so that the ANN-inversion dynamic soft-sensing model or the desired soft-sensor is finally completed. This makes the proposed ANN-inversion soft-sensor stricter in construction principle and more credible in practical use than most proposed soft-sensors. The soft-sensor consisting of a static ANN and a set of differentiators has been put into use of estimating such crucial biochemical variables as mycelia concentration, sugar concentration and chemical potency in erythromycin fermentation process. The field results show that the soft-sensing values approximately coincide with the offline analyzing ones sampled from the production process.

[1]  C. A. Kent,et al.  A structured model for penicillin production on mixed substrates , 1998 .

[2]  Xianzhong Dai,et al.  MIMO system invertibility and decoupling control strategies based on ANN /spl alpha/th-order inversion , 2001 .

[3]  Karl Schügerl,et al.  Application of an extended Kalman filter for state estimation of a yeast fermentation , 1986 .

[4]  W. Ramirez,et al.  On‐Line State Estimation and Parameter Identification for Batch Fermentation , 1996 .

[5]  Kazuyuki Shimizu,et al.  A tutorial review on bioprocess systems engineering , 1996 .

[6]  J Glassey,et al.  Bioprocess supervision: neural networks and knowledge based systems. , 1997, Journal of biotechnology.

[7]  Hongjian Zhang,et al.  Time-delay neural network for the prediction of carbonation tower's temperature , 2003, IEEE Trans. Instrum. Meas..

[8]  Eduardo Gómez-Sánchez,et al.  Automatization of a penicillin production process with soft sensors and an adaptive controller based on neuro fuzzy systems , 2004 .

[9]  Sirish L. Shah,et al.  Adaptive multirate state and parameter estimation strategies with application to a bioreactor , 1995 .

[10]  Martin T. Hagan,et al.  Neural network design , 1995 .

[11]  M. Reuss,et al.  Evaluation of feeding strategies in carbon‐regulated secondary metabolite production through mathematical modeling , 1981 .

[12]  D. Dochain,et al.  On-Line Estimation and Adaptive Control of Bioreactors , 2013 .

[13]  Tianyou Chai,et al.  Soft sensing based on artificial neural network , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[14]  I Yet-Pole,et al.  Neural network modelling for on-line state estimation in fed-batch culture of l-lysine production , 1996 .

[15]  Michael L. Mavrovouniotis,et al.  Hierarchical neural networks , 1992 .

[16]  Klaus Fritzsche,et al.  Several Complex Variables , 1976 .

[17]  Gary A. Montague,et al.  Application of radial basis function and feedforward artificial neural networks to the Escherichia coli fermentation process , 1998, Neurocomputing.

[18]  L. T. Fan,et al.  Monitoring the process of curing of epoxy/graphite fiber composites with a recurrent neural network as a soft sensor , 1998 .

[19]  A Chéruy,et al.  Software sensors in bioprocess engineering , 1997 .

[20]  D. Wilson,et al.  Experiences implementing the extended Kalman filter on an industrial batch reactor , 1998 .

[21]  Gülnur Birol,et al.  A modular simulation package for fed-batch fermentation: penicillin production , 2002 .

[22]  Xianzhong Dai,et al.  Artificial neural networks inversion based dynamic compensator of sensors , 2004, International Conference on Information Acquisition, 2004. Proceedings..

[23]  Rubens Maciel Filho,et al.  Soft sensors development for on-line bioreactor state estimation , 2000 .

[24]  Denis Dochain,et al.  State and parameter estimation in chemical and biochemical processes: a tutorial , 2003 .

[25]  Xianzhong Dai,et al.  Application of ANN-Inversion Soft-Sensing Method in Biochemical Fermentation , 2004, Int. J. Inf. Acquis..

[26]  Jian Li,et al.  The application of neural network soft sensor technology to an advanced control system of distillation operation , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..