Inversion based state estimation in bioleaching process with measurement noises

In this paper, the “assumed inherent sensor” inversion (AISI) that is a soft-sensor in essence is used to perform the on-line estimation of the directly immeasurable state variables in the bioleaching process performed by Thiobacillus Ferrooxidans (T. ferrooxidans). The “assumed inherent sensor” (AIS) of the bioleaching process is first constructed successfully according to the systematic modeling algorithm proposed in our pervious work, and then the mathematical model of the AISI is obtained eventually after proving the invertibility of the AIS. Finally, the simulations are performed by using the AISI for such a bioleaching process with measurement noises. The simulation results show the effectiveness of the AISI.

[1]  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 .

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

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

[4]  E. Donati,et al.  Iron-oxidizing and leaching activities of sulphur-grown Thiobacillus ferrooxidans cells on other substrates: effect of culture pH. , 2000, Journal of bioscience and bioengineering.

[5]  J. Gauthier,et al.  A simple observer for nonlinear systems applications to bioreactors , 1992 .

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

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

[8]  Jorge A. Solsona,et al.  State estimation in batch processes using a nonlinear observer , 2006, Math. Comput. Model..

[9]  Xiao Fan Wang,et al.  Soft sensing modeling based on support vector machine and Bayesian model selection , 2004, Comput. Chem. Eng..

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

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

[12]  K. S. Gandhi,et al.  Modelling of Fe2 + oxidation by Thiobacillus ferrooxidans , 1990, Applied Microbiology and Biotechnology.

[13]  Guy Mercier,et al.  Metal bioleaching prediction in continuous processing of municipal sewage with Thiobacillus ferrooxidans using neural networks , 2000 .

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

[15]  Xianzhong Dai,et al.  "Assumed inherent sensor" inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process , 2006, Comput. Chem. Eng..

[16]  M. Soroush.  Nonlinear state-observer design with application to reactors , 1997 .

[17]  Luigi Fortuna,et al.  Soft sensors for product quality monitoring in debutanizer distillation columns , 2005 .

[18]  A. Cheruy,et al.  A software sensor of biological activity based on a redox probe for the control of Thiobacillus ferrooxidans cultures , 1994 .

[19]  Michel Fick,et al.  Models of bacterial leaching , 1995 .

[20]  M. Boon,et al.  The ferrous iron oxidation kinetics of Thiobacillus ferrooxidans in batch cultures , 1999, Applied Microbiology and Biotechnology.