Data collection during the progress of a batch is necessary for monitoring and controlling process operation for optimal cultivation and production. An essential element of effective monitoring and control is high quality data obtained during the progress of the batch via appropriate instrumentation/sensors. The information collected may also be used for modeling the process or improving the process design and production policies.
This chapter focuses on a number of important topics about experimental data collection. Section 3.1 outlines desirable properties of sensors and discusses various on-line and off-line sensors used in fermentation processes. Section 3.2 presents data acquisition systems and describes computer-based data collection and control. Section 3.3 presents introductory concepts in statistical design of experiments. Outliers in data and signal noise may have strong influence on the parameter values and structure of models developed, decisions about process status in monitoring, and regulatory action selected in process control. Consequently, outlier detection and data pretreatment such as reconciliation and denoising of data are critical for developing accurate models. Section 3.4 introduces various techniques on outlier detection and data reconciliation. Process data may contain various levels of signal noise. Section 3.5 introduces wavelets and discusses various noise reduction techniques. Section 3.6 outlines methods used in theoretical confirmation of data, in particular stoichiometric balances and thermodynamics of cell growth.
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