Batch Fermentation

Illustrating techniques in model development, signal processing, data reconciliation, process monitoring, quality assurance, intelligent real-time process supervision, and fault detection and diagnosis, Batch Fermentation offers valuable simulation and control strategies for batch fermentation applications in the food, pharmaceutical, and chemical industries. The book provides approaches for determining optimal reference trajectories and operating conditions; estimating final product quality; modifying, adjusting, and enhancing batch process operations; and designing integrated real-time intelligent knowledge-based systems for process monitoring and fault diagnosis.

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.