Batch Fermentation

Ali Cinar (Illinois Institute of Technology, Chicago, USA) (Author) , Satish J. Parulekar (Illinois Inst. of Technology) (Author) , Cenk Undey (Illinois Institute of Technology, Chicago, Illinois, USA) (Author) , Gulnur Birol (Northwestern University, Evanston, Illinois USA) (Author)

Introduction: characteristics of batch processes; focus areas of the book; penicillin fermentation; outline of the book. Kinetics and process models: introduction and background; mathematical representation of bioreactor operation; bioreactoroperation modes; conservation equations for a single bioreactor; unstructured kinetic models; structured kinetic models; case studies. Experimental data collection: sensors; computer-based data acquisition; statistical design of experiments; datapretreatment - outliers and data reconciliation; data pretreatment - signal noise reduction; theoretical confirmation/stoichiometry and energetics of growth. Linear data-based model development: principal components analysis; multivariable regressiontechniques; input-output modelling of dynamic processes; functional data analysis; multivariate statistical paradigms for batch process Modelling; artificial neural networks; extensions of linear modelling techniques to nonlinear model development. Systemscience methods nonlinear model development: deterministic systems and chaos; nonlinear time series analysis; model development; software resources. Statistical process monitoring: SPM based on univariate techniques; SPM of continuous processes withmultivariate statistical techniques; data length equalization and determination of phase landmarks in batch fermentation; multivariable batch processes; on-line monitoring of batch/fed-batch fermentation processes; monitoring of successive batch runs.Process control: introduction; open-loop (optimal) control; forced periodic operations; feedback control; optimal linear-quadratic feedback control; model predictive control. Fault diagnosis: contribution plots; statistical techniques for fault diagnosis;model-based fault diagnosis techniques; model-free fault diagnosis techniques. Related developments: role of metabolic engineering in process improvement; contributions of MFA and MCA to modelling; dynamic optimization of batch process operations;integrated supervisory KBS for on-line process supervision.