3. Control Strategies for bioprocesses

All  the  bioreactors  used  at  the  present  time   use   control   strategies   for   three   basic environmental  factors:  pH,  temperature  and  dissolved  oxygen. Invariably, these control implementations  are  achieved  via  regulation  of  flow  rate  of acid/base, flow rate of fluid through  the cooling coil, and agitation respectively. Needless to say, these three parameters are  extremely  important  for optimal cellular activity. But they alone do not guarantee the maximum  productivity,  which  is  the  objective  for  most  of the industrial fermentations.
This paper will explore the control strategies which are used to accomplish this goal.
 

Before attempting to understand the details of the control strategies used for bioprocesses, one  should  be  familiar  with  the  common  features  in  the  field  of  controls. One  of  the integrated  feature  in  any  control  system  is  control  algorithm.  The control algorithm is that part of the control system that takes the available  measurements   and level of  process  understanding   and   decides   on  the  best  way  to influence the process with the available manipulated variable to achieve the desired objective.⁴
 
A  control  system  can  not   be   implemented   unless   the   process   under   consideration  is understood. An  efficient  way of understanding the  process is  a mathematical model of the process. A good process model is an invaluable tool to deveolp a control algorithm. It is  not  implied  that  controllers  can  not control poorly understood processes; indeed, that is   often   their   function. However,  an  expensive,  and time – consuming  trial  and  error adjustment of the control algorithm is required in that case.⁴

A  common  approach  to  obtain  a simple, empirical model for controller design is to make small  step  changes  in  the  inputs  and  observe  the dynamic behavior of the outputs. One can  then  obtain  a  linear,  time – invariant  process  model  in  a  straightforward  fashion. A more  fundamental  approach  is  to  formulate mass and energy balances for different components, resulting in a set of nonlinear ordinary differential equations. The latter approach has an advantage that the nonlinear model may better represent the process over a significant range of state values, whereas the linear empirical model resulted from the former approach may not be reliable for process states away from the state at which model is identified. This is particularly important in case of batch and fed – batch fermentations in which the process state changes significantly during operation. The disadvantage of the latter approach is  that the available controller - design tools are less developed for the nonlinear models.⁴

There are three modes of bioreactor operation: batch, fed-batch and continuous. As discussed in subsequent sections of this paper, each of these modes presents different challenges to the control algorithm. Sectoin 4 descibes the salient features relevant to control in case of each type of bioreactror.
 
The proper choice of the control algorithm will be dictated by the performance objective, process model, available measurements, and manipulated variables. Many tools to develop control algorithms are discussed in this paper. Section 5 discusses simple feedback control algorithm, i.e., PID control algorithm. Section 6 focuses on optimal control, whose implementation has been greatly facilitated by the recent development of powerful and inexpensive computer hardware. Adaptive control theory is reviewed in section 7. Finally, state estimation techniques to deal with the dearth of on - line sensors for fermentation processes are covered in section 8. Section 9 details the practical issues that relate to control of bioprocesses and further elaborates on real-time control processes by taking a case study. Section 10 describes future trends in the field of control of bioprocesses.