AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge

Individual optimization of the dialysis process requires the (open-loop or closed-loop) control of many different variables, e.g. plasma ion concentrations, acid base state, volemic state and hemodynamic quantities. For this purpose a general concept for multiple-input-multiple-output (MIMO) control of the dialysis process is presented. The controlled variables have been differentiated into variables which can be modeled mechanistically (primary controlled variables, PCVs) and (hemodynamic) variables for which no mechanistic model has been developed up to now (secondary controlled variables, SCVs). Accordingly the controller is decomposed into two stages. Stage 1 contains an expert system which links the PCVs to the SCVs and provides the generation of optimal profiles for the PCVs with respect to maximum hemodynamic stability of the patient. Stage 2 is a tracking controller for the PCVs. An algorithm for the multidimensional tracking problem at stage 2 has been developed. It can be used for open-loop and future closed-loop control. The algorithm has been tested for 4 controlled (plasma Na+, plasma K+, plasma volume and ratio between intra- and extracellular volume) and 3 control variables (dialysate Na+, dialysate K+, ultrafiltration rate) up to now. It renders possible the exact tracking of the prescribed trajectories as long as all points are reachable under consideration of all physical and physiological boundary conditions. If they are not, appropriate weighting of the conflicting optimization goals must be applied. An extension towards more than 4 controlled variables is possible on principle. Main advantages of the method are its mathematical simplicity and the applicability of standard optimization subroutines.

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