Sustainable co-location solutions for offshore wind farms and fisheries need to account for socio-ecological trade-offs.

The spatial expansion of offshore wind farms (OWFs) is key for the transition to a carbon free energy sector. In the North Sea, the sprawl of OWFs is regulated by marine spatial planning (MSP) and results in an increasing loss of space for other sectors such as fisheries. Understanding fisheries benefits of OWFs and mitigating the loss of fishing grounds is key for co-location solutions in MSP. For the German exclusive economic zone (EEZ) of the North Sea we conducted a novel socio-ecological assessment of fisheries benefits which combines exploring potential spill-over from an OWF with an experimental brown crab (Cancer pagurus) pot fishery and an economic viability analysis of such a fishery. We arrayed a total of 205 baited pots along transects from an OWF located near the island of Helgoland. After a soaking time of 24 h we retrieved the pots and measured the carapace width (mm), weight (g), and sex of each individual crab. To conclude on cumulative spill-over potentials from all OWFs in the German EEZ and drivers of passive gear fisheries we analysed vessel monitoring system (VMS)-data and computed random forest regressions. Local spill-over mechanisms occurred up to distances of 300 to 500 m to the nearest turbines and revealed an increasing attraction of pot fishing activities to particular OWFs. This corresponds to the observation of constantly increasing fishing effort targeting brown crab likely due to both a growing international demand and stable resource populations at suitable habitats, including OWFs. Our break-even scenarios showed that beam trawlers have the capacities to conduct during summer an opportunistic but economically viable pot fishery. We argue that particularly in the North Sea, where space becomes limited, integrated assessments of the wider environmental and socio-economic effects of planning are crucial for a sustainable co-location of OWFs and fisheries.

Mechanistic Mathematical Models as a Basis for Digital Twins.

A future-oriented approach is the application of Digital Twins for process development, optimization and finally during manufacturing. Digital Twins are detailed virtual representations of bioprocesses with predictive capabilities. In biotechnology, Digital Twins can be used to monitor processes and to provide data for process control and optimization. Central and crucial components of Digital Twins are mathematical process models, which are capable to describe and predict cultivations with high fidelity. Detailed mechanistic models in particular are suitable for both use in Digital Twins and for the development of process control strategies.In this chapter the requirements that process models must fulfil in order to be used for process optimization and finally in Digital Twins will be described. Different types of models, including mechanistic as well as compartmentalized models, are outlined and their application in Digital Twins and for process optimization is explained. Finally, a structured, compartmentalized process model, which was specifically designed for process optimization and has already been used in Digital Twins, is highlighted.

Operator training in recombinant protein production using a structured simulator model.

Model-based operator training simulators (OTS) could be powerful tools for virtual training of operational procedures and skills of production personnel in recombinant protein processes. The applied model should describe critical events in the bioprocess so accurately that the operators' ability to observe and alertly act upon these events is trained with a high degree of efficiency. In this work is shown how this is accomplished in a structured multi-compartment model for the production of a recombinant protein in an Escherichia coli fed-batch process where in particular the induction procedure, the stress effects and overflow metabolism were highlighted. The structured model was applied on the OTS platform that virtually simulated the operational bioreactor procedures in real or accelerated time. Evaluation of training using the model-based OTS showed that trained groups of operators exhibited improved capability compared with the untrained groups when subsequently performing real laboratory scale cultivations. The results suggest that this model-based OTS may provide a valuable resource for enhancing operator skills in large scale recombinant protein manufacturing.