Technical-grade and mixed solid/liquid phase change materials (PCM) typically melt and solidify over a temperature range, sometimes exhibiting thermal hysteresis. Three phenomenological phase transition models are presented which are directly parametrized using data from complete melting and solidification experiments. They predict hysteresis phenomena and are used to calculate effective PCM properties. Two models have already been implemented in commercial building simulation and/or multiphysics software, but not the third novel model. Applications are presented for two commercial PCM: a paraffin, and a salt water mixture with additives. Numerical implementation aspects are discussed, and significant differences in the predicted absorbed and released heat are highlighted when simulating consecutive incomplete phase transitions. The models are linked with energy balance equations to predict recorded PCM temperatures of a thermal energy storage. The cross-validation with data from 26 partial load conditions clearly indicate a superior predictive performance of the novel hysteresis model. GRAPHICAL ABSTRACT

Monitoring of the state of charge of the thermal energy storage component in solar thermal systems for space heating and/or cooling in residential buildings is a key element from the overall system control strategy point of view. According to the literature, there is not a unique method for determining the state of charge of a thermal energy storage system that could generally be applied in any system. This contribution firstly provides a classification of the state-of-the-art of available techniques for the determination of the state of charge, and secondly, it presents an experimental analysis of different methods based on established sensor technologies, namely temperature, mass flow rates, and pressure measurements, tested using a lab-scale heat exchanger filled with a commercial phase change material for cooling applications. The results indicate that, depending on the expected accuracy and available instrumentation, each of the methods studied here can be used in the present application, the deviations between the methods generally being below 20%. This study concludes that a proper combination of two or more of these methods would be the ideal strategy to obtain a more reliable and accurate estimation of the state of charge of the latent heat thermal energy storage.

Modern biotechnological laboratories are equipped with advanced parallel mini‐bioreactor facilities that can perform sophisticated cultivation strategies (e.g., fed‐batch or continuous) and generate significant amounts of measurement data. These systems require not only optimal experimental designs that find the best conditions in very large design spaces, but also algorithms that manage to operate a large number of different cultivations in parallel within a well‐defined and tightly constrained operating regime. Existing advanced process control algorithms have to be tailored to tackle the specific issues of such facilities such as: a very complex biological system, constant changes in the metabolic activity and phenotypes, shifts of pH and/or temperature, and metabolic switches, to name a few. In this study we implement a model predictive control (MPC) framework to demonstrate: (1) the challenges in terms of mathematical model structure, state, and parameter estimation, and optimization under highly nonlinear and stiff dynamics in biological systems, (2) the adaptations required to enable the application of MPC in high throughput bioprocess development, and (3) the added value of MPC implementations when operating parallel mini‐bioreactors aiming to maximize the biomass concentration while coping with hard constrains on the dissolved oxygen tension profile.