ABSTRACT
Process plants of all biotechnology and fermentation related products require collection and handling of a huge quantity of data, both online and offline. These data are unique because of their heterogeneous nature and ability to reveal patterns and other information. Bioprocess data consists of several physical and chemical parameters like material input, process output, control actions, and working conditions. Systematic collection and analysis of these data can help in the improvement and optimization of a process from all aspects. This task has been made much easier in recent times owing to machine learning and various data mining techniques. This chapter will focus on the chronological developments of bioprocess data mining techniques, their applications and significance in the biotechnology industry.
