Monitoring of Bioprocesses: Mechanistic and Data-Driven Approaches

Abstract

Nowadays, bioprocesses play a key role in the production of high-added value products in the pharmaceutical industry and measurements of the main component concentrations are of great importance for monitoring cell cultures. Although some hardware sensors are readily available, they often have several drawbacks, including purchase and maintenance costs, sample destruction, discrete-time measurements (instead of continuous ones), processing delay, calibration, sterilization, disturbances in the hydrodynamic conditions inside the bioreactor, etc. It is therefore of interest to use software sensors which reconstruct on-line some component concentrations in continuous time. Software sensors are based on the theory of state estimation. In this chapter, some state estimation techniques are reviewed, and two important situations are distinguished: (a) some component concentrations can be measured and a dynamic model of the bioprocess can be established and (b) only basic operating signals, such as pH, base addition, stirrer speed, feed rates, can be measured on-line and it is difficult (or even impossible) to build a mechanistic model linking these variables. In the latter case, a neural network approach appears particularly suitable, and is largely illustrated in this chapter by real-life experimental applications.