16 Instrumentation and Control Systems John p King 1.0 INTRODUCTION The widespread use of advanced control and process automation for biochemical applications has been lagging as compared with industries such as refining and petrochemicals whose feedstocks are relatively easy to characterize and whose chemistry is well understood and whose measure- ments are relatively straightforward Biological processes are extraordinarily complex and subject to con siderable variability. The reaction kinetics cannot be completely determined in advance in a fermentation process because of variations in the biological properties of the inoculant. Therefore, information regarding the activity of the process must be gathered as the fermentation progresses. Directly measuring all the necessary variables which characterize and govern the competing biochemical reactions, even under optimum laboratory condi ions, is not yet achievable. Developing mathematical models which can be utilized to infer the biological processes underway from the measurements available, although useful, is still not sufficiently accurate. Add to this the constraints and compromises imposed by the manufacturing process and the task of accurately predicting and controlling the behavior of biological production processes is formidable indeed16 Instrumentation and Control Systems John R King 1.0 INTRODUCTION The widespread use of advanced control and process automation for biochemical applications has been lagging as compared with industries such as refining and petrochemicals whose feedstocks are relatively easy to characterize and whose chemistry is well understood and whose measure￾ments are relatively straightforward. Biological processes are extraordinarily complex and subject to con￾siderable variability. The reaction kinetics cannot be completely determined in advance in a fermentation process because of variations in the biological properties of the inoculant. Therefore, information regarding the activity of the process must be gathered as the fermentation progresses. Directly measuring all the necessary variables which characterize and govern the competing biochemical reactions, even under optimum laboratory condi￾tions, is not yet achievable. Developing mathematical models which can be utilized to infer the biological processes underway from the measurements available, although useful, is still not sufficiently accurate. Add to this the constraints and compromises imposed by the manufacturing process and the task of accurately predicting and controlling the behavior of biological production processes is formidable indeed. 675