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BACKGROUND During recent years several computer‐based operator training simulators (OTS) have been developed that are suitable for the virtual training of operators and other professionals. In the field of bioprocess engineering OTS are rarely used. Furthermore, the effects of using training simulators in bioprocess applications have not been evaluated. RESULTS The OTS BioProcessTrainer was applied to bioreactor operations for two biological processes, Saccharomyces cerevisiae, for ethanol production, and recombinant Escherichia coli, for production of green fluorescence protein (GFP). The simulator used a multi‐shell model platform that described the biological and physical conditions of the bioreactor for the two bioprocess systems. The simulator enabled the user to plan, operate and control the processes in real or accelerated time. The training resulted in improved ability to manage the whole bioreactor procedure for the two processes. CONCLUSION The study showed that the simulator can be an efficient tool for training of operation, optimization and control of bioprocesses. The mathematical model framework of the simulator can be adapted to a variety of industrial bioprocesses. Thus, it appears likely that this type of OTS may serve as a useful resource in industry for training and continuing education of plant operators and engineers. © 2013 Society of Chemical Industry

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Process models can be used for model‐based control strategies, but model development is a time‐consuming and laborious task. To reduce the modeling effort, a new structured compartment model was developed, which may easily be adapted to different cultivation processes. The proposed six‐compartment model was used to describe the time courses of cultivations of bacteria, yeast, fungi, and mammalian cell lines, namely, Escherichia coli, Lactobacillus delbrueckii, Saccharomyces cerevisiae, Cyathus striatus, and a hybridoma mammalian cell line. The model can describe the time courses of important state variables and can be adapted to the cultivation processes by parameterization. This reduces the modeling effort for a new process significantly.