Introduction

Automatic control of a process can be defined as control of the variables without the necessity of manual intervention. The term automation has a similar meaning but also refers to control on a higher level, involving a sequence of tasks or operations that are scheduled and performed by a computer system. Control and automation of processes offer several benefits (Beyeler et al. 2000). Firstly, optimal conditions for productivity and product quality can be maintained accurately and continuously, even in processes with a high level of complexity. Secondly, recipe-controlled operations also maximise the process reproducibility and minimize run-to-run variations in product quality. Thirdly, safety during production is increased, since the automation system can also continuously check critical parameters and handle some failures according to predefined routines. Even during manual operations, the reliability of personnel can be enhanced by the support of an automation system, via alarms, check lists, etc. Finally, automation can also help to reduce the need for personnel and the duration of operations. For these reasons, control and automation have become key elements of modern pilot- and large-scale biotechnology manufacturing facilities. In upstream processing, automation is actually not only applied to the culture steps per se but also to many side operations, such as the supply of medium and gases, the cleaning- and sterilization-in-place of equipment, the transfer of process fluids, etc. (see Chapter 14). In research and development, automatic control is also a very powerful tool for metabolic studies, for the verification of process models, and for the improvement of bioprocesses (Sonnleitner 1997).

The tools used for control and automation can be viewed as being an essential part of the various information systems used in a manufacturing organization or even a whole company. These systems are often classified and organized in a computer-integrated manufacturing (CIM) pyramid, with four or five different functional levels. Each level has the ability to communicate with the level above or beneath in different ways, depending on the degree of automation of the manufacturing organization. Figure 13.1 shows a typical schematic representation of a CIM pyramid.

The field level is discussed in Section 13.2, below, with emphasis on the monitoring of cell cultures; this area has been the object of significant development efforts in the last decade, but is still a weak point in the automation of bioprocesses due to the difficulties in obtaining reliable sensors. The process-control level is discussed in Section 13.3. The higher levels are described in Section 13.4, but only in summary, since they are not specific to animal cell cultivation. This chapter is

Medicines from Animal Cell Culture Edited by G. Stacey and J. Davis © 2007 John Wiley & Sons, Ltd

Enterprise-management ERP tool level Server

Workstations

Production-management MRP II tool level Server

Workstations

Supervision level

Supervision tool Server

Control stations

Process-control level

Programmable logic controllers (PLC)

Field level

Sensors

Actuators

Field bus system

pH

Pü2

7.21

ft

51 %

Figure 13.1 Schematic representation of a computer-integrated manufacturing (CIM) pyramid for a manufacturing organization. Only the main elements used at each level are listed and depicted. Double arrows represent, in a simplified way, communication between the levels. Dotted lines indicate communication that is often manual, except in highly automated plants (see Section 13.4.2 for details).

completed by a discussion on the regulatory aspects of automation systems, with emphasis on the requirements set by the US FDA (21 CFR Part 11, 1997).

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