The best method for process control is the use of model‐based solutions, based on process analytical technology for online monitoring of critical process variables, product quality attributes, or a holistic process state estimation. Mechanistic models as well as data‐driven techniques are essential for real‐time process monitoring. Their main characteristics, advantages and disadvantages, and the link between both are discussed as well as the synergetic effects, benefits, and drawbacks resulting from their combination. Aspects and differences of the computational model life cycle management are highlighted.

Prozessanalytik und die ihr zugrundeliegende Prozessanalysentechnik müssen auf die spezifischen Anforderungen an Prozesse in der Life-Science-Industrie eingehen, um dort künftig umfassend eingesetzt zu werden. Dies hängt einerseits mit den vielfältigen regulatorischen Anforderungen zusammen, aber auch mit der Komplexität und anderen Herausforderungen der einzelnen Mess-und Regelaufgaben. Beispiele für aktuell erzielte Ergebnisse werden im vorliegenden Beitrag vorgestellt. Echtzeitinformationen -gegebenenfalls auch stoffspezifisch -als Grundlage eines besseren Prozessverständnisses und einer überlegenen, automatisierten Prozessführung zu gewinnen, gehört dabei zu den wichtigsten Anforderungen.Process analytics and the process analyzer technology it is based on need to address specific requirements of industrial life-science processes in order to become extensively employed in this area. This is due to a variety of regulatory demands, but also due to the complexity and to other challenges of the individual measurement and control tasks. Examples of recent achievements are presented here. Real time information -substance-specific where appropriate -as a basis for an improved understanding of the processes and for superior automated process control are among the most prominent requirements.

Process analytics and process analyzer technology are the basis need to address specific requirements of industrial life‐science processes in order to become extensively employed in this area. This is due to a variety of regulatory demands, but also due to the complexity and other challenges of the individual measurement and control tasks. Examples of recent achievements are presented here. Real time information – substance‐specific where appropriate – as a basis for an improved understanding of the processes and for superior automated process control are among the most prominent requirements.