By Lei Zhi Chen Dr., Xiao Dong Chen Professor Dr., Sing Kiong Nguang Professor Dr. (auth.)
ISBN-10: 354030634X
ISBN-13: 9783540306344
ISBN-10: 3540324933
ISBN-13: 9783540324935
This publication provides logical techniques to tracking, modelling and optimization of fed-batch fermentation tactics in keeping with synthetic intelligence tools, particularly, neural networks and genetic algorithms. either machine simulation and experimental validation are established during this ebook. The methods proposed during this booklet will be quite simply followed for various approaches and regulate schemes to accomplish greatest productiveness with minimal improvement and creation bills. those methods can cast off the problems of getting to specify thoroughly the buildings and parameters of hugely nonlinear bioprocess versions.
The ebook starts with a old creation to the sector of bioprocess regulate in line with synthetic intelligence methods, through chapters overlaying the optimization of fed-batch tradition utilizing genetic algorithms. on-line biomass soft-sensors are built in bankruptcy four utilizing recurrent neural networks. The bioprocess is then modelled in bankruptcy five by means of cascading soft-sensor neural networks. Optimization and validation of the ultimate product are particular in Chapters 6 and seven. the overall conclusions are drawn in bankruptcy 8.
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Extra info for Modelling and Optimization of Biotechnological Processes: Artificial Intelligence Approaches
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This specified range is the most sensitive area of the sigmoidal function, which is the hidden layer activation function. 5 (a) Square-wave feed DO (mol/L) 5 20 Biomass (g/L) Biomass (g/L) 1000 1 0 15 10 2000 1500 500 0 −4 x 10 3 15 10 DO (mol/L) DO (mol/L) 1000 0 −4 x 10 3 45 20 10 0 0 (e) Random-steps feed Fig. 3. Plots of simulation data for five different feed rates. 46 4 On-line Softsensor Development be in the range [-1,1]. A post-processing procedure has to be performed in order to convert the output back to its original unit.
Identification of system parameters The time used for parameter identification was the first two days of the fedbatch fermentation. 4 in Appendix A. 4 Numerical Results START Initialize Population of Parameters Feed Single Stream to Bioreactor Calculate Values of State Variables Measure Values of State Variables Evaluate Objective Function J I Identification Procedure Run Genetic Algorithms N Termination Condition Reached? Y Obtain fermentation parameters Initialize Population of Control Profiles Evaluate Objective Function J o Run Genetic Algorithm N Optimization Procedure Termination Condition Reached?
2 Softsensor Structure Determination and Implementation D Data preU processing 43 Activation feedback D Input F, V, DO H Yˆ Data postprocessing D D Hidden Output layer layer D Output feedback Estimated biomass Tapped delay line Fig. 1. Structure of the proposed recurrent neural softsensor. output neuron, feed-forward paths and feedback paths. All connections could be multiple paths. In order to enhance dynamic behaviors of the sensor, outputs from the output layer (output feedback) and the hidden layer (activation feedback) are connected to the input layer through TDLs.
Modelling and Optimization of Biotechnological Processes: Artificial Intelligence Approaches by Lei Zhi Chen Dr., Xiao Dong Chen Professor Dr., Sing Kiong Nguang Professor Dr. (auth.)
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