Abstract
For efficient operation, modern control approaches for biochemical process engineering require information on the states of the process such as temperature, humidity or chemical composition. Those measurement are gathered from a set of sensors which differ with respect to sampling rates and measurement quality. Furthermore, for biochemical processes in particular, analysis of physical samples is necessary, e.g., to infer cellular composition resulting in delayed information. As an alternative for the use of this delayed measurement for control, so-called soft-sensor approaches can be used to fuse delayed multirate measurements with the help of a mathematical process model and provide information on the current state of the process. In this manuscript we present a complete methodology based on cascaded unscented Kalman filters for state estimation from delayed and multi-rate measurements. The approach is demonstrated for two examples, an exothermic chemical reactor and a recently developed model for biopolymer production. The results indicate that the the current state of the systems can be accurately reconstructed and therefore represent a promising tool for further application in advanced model-based control not only of the considered processes but also of related processes.
Highlights
In recent years, application of automatic control to bio-chemical manufacturing processes has become increasingly important to keep the required product quality in close bounds, guarantee process safety and decrease the corresponding environmental impact
Two examples are analyzed: In the first case, the unscented Kalman filters (UKF) is applied to a exothermic reactor described by a system of two nonlinear ODEs [22]
The second example is concerned with state estimation for microbial-based production of biopolymers in lab-scale setup
Summary
Application of automatic control to bio-chemical manufacturing processes has become increasingly important to keep the required product quality in close bounds, guarantee process safety and decrease the corresponding environmental impact. Examples are found in a wide range of industries including pharmaceutical and food manufacturing. For efficient operation, these control approaches require information on the current values of important process states and parameters. These control approaches require information on the current values of important process states and parameters Often, those are either corrupted by noise or not directly measurable. Measurements of different process quantities are gained from various sensor types which may differ in sampling rate, accuracy and lag. Many online sensors can provide instantaneous but lumped (e.g., mean particle size instead of the full particle size distribution for fluidized bed granulation) or indirect/inferential measurements gained from auxiliary variables (e.g., for drying processes, indirect measurement of product moisture content from moisture content of drying gas at the outlet) [1,2]. Offline measurements are generally more accurate but accompanied by significant measurement lags, e.g., resulting from sample drawing, preparation and analysis for biochemical processes
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