Solid state electrochemical cells are to date developed as solid oxide fuel and electrolyser cells operating at temperatures above 500 °C and converting chemical energy into electrical energy and vice versa. Compared to low temperature fuel cells and electrolysers, these cells offer several advantages such as fast reaction kinetics, high conversion and efficiency, and the ability to convert carbon containing gases that results in fuel flexibility and potential for CO2 utilisation. The solid oxide cell (SOC) is an all-solid-state multilayer assembly with different (and also multiple) functionalities within the individual layers. Furthermore, the high operating temperatures pose several additional requirements on the materials, fi. matching chemical and physical properties to keep the solid-state cell intact during operation as well as during start-up and shut down. State-of-the-art electrode materials suffer from compromising functional, structural, and physical-chemical properties, which still triggers the search for alternative electrode materials and concepts.
In this presentation, the key requirements for both the fuel and air electrodes in solid oxide cells will be briefly introduced and approaches to novel, optimized electrodes presented. The design strategies start from chemical modification of the materials tailoring both, electronic and ionic conductivity, up to structural modifications leading to graded microstructures and allow to decouple the various functions in the electrode, esp. structural support, electronic conductivity and electro catalytic activity. The presentation will give examples on how different functionalities can be individually tailored in these multilayer cells and which design options are available for technically relevant electrode architectures and fabrication routes.

The increasing amount of sensors data in technical systems as well as larger computing capabilities provide many opportunities for data-based methods and machine learning algorithms in a wide range of system theory. In many cases, the interpretability and generalizability of data-based methods are limited compared to classical model-based methods. This fact motivates hybrid models, which combine model- and data-based methods to combine their benefits. This talk presents two exemplary applications of data-based and hybrid methods in system dynamics and control.
The first example deals with detection of anomalies and fault diagnosis in a paint shop by bipartite graphs. The structure of the bipartite graph is derived by mutual information using data from an active system excitation. The joint probability density function between the identified sets of stochastically depend variables is estimated with Gaussian Mixture Models.  The parametrized structure enables the evaluation of the likelihood for real-time data to detect and locate anomalies. Experimental results from an air supply unit illustrate the effectiveness of the approach.
The last example is the diagnosis of actuator and sensor faults in adaptive high-rise buildings using a hybrid approach. It combines the model-based method of parity equation with the data-based method of principal component analysis (PCA). PCA characterizes the unknown disturbance in the residual data derived by parity equations and determines orthogonal directions of decreasing variance in the residuals. These directions are decoupled to decrease the sensitivity of the residual to disturbances and maintain the diagnosability of faults.

Reactor design and optimization are crucial aspects of chemical and biochemical engineering. With the advent of additive manufacturing, advanced reactor geometries are now possible, offering improved operational efficiency and cost-effectiveness. However, due to this extra flexibility, optimizing over these designs is even more challenging. In this talk, we discuss work that integrates computational fluid dynamics (CFD) simulations with a multi-fidelity Bayesian optimization. We introduce an approach that not only recommends optimal reactor configurations and operating conditions but also determines fidelity (level of accuracy of the CFD simulator) levels based on statistical likelihoods and information content, optimizing accuracy and computational efficiency.
The methodology discussed focuses on plug-flow reactors but can be extended to various reactor types. By maximizing plug-flow performance, we identify crucial design characteristics and validate two novel geometries through 3D printing and experimental validation. Through this data-driven optimization of highly parameterized reactors, we aim to establish a framework for next-generation reactors, highlighting how machine learning and advanced manufacturing processes can revolutionize the performance and sustainability of future chemical processes.