The following report with recommendations is the result of an expert panel meeting on soft sensor applications in bioprocess engineering that was organized by the Measurement, Monitoring, Modelling and Control (M3C) Working Group of the European Federation of Biotechnology ‐ Section of Biochemical Engineering Science (ESBES). The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for the furthering of the successful development of soft sensor methods in bioprocess engineering research and for industrial applications, in particular with focus on biopharmaceutical applications. It concludes with a set of recommendations, which highlight current prospects for the extended use of soft sensors and those areas requiring development.

Although fed-batch suspension culture of animal cells continues to be of industrial importance for the large scale production of pharmaceutical products, existing control concepts are still insufficient. Changes in cell metabolism during cultivation and between similar cultivations, the complexity of the cell metabolism, and the lack of on-line state variables restrict the transfer of available control strategies established in bioprocess engineering. A process control strategy designed to achieve optimized process control must account for all these difficulties and fit sophisticated requirements toward adaptability and flexibility. The combination of a fed-batch process and an Open-Loop-Feedback-Optimal (OLFO) control provides a new approach for cell culture process control that couples an efficient cultivation concept to a capable process control strategy. The application of an adaptive, model-based OLFO controller to a hybridoma cultivation and experimental results are presented.

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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