## Doctoral Thesis

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Towards a strategic management framework for engineering of organizational robustness and resilience
(2020)

Clathrate hydrates, or hydrates for short, are inclusion compounds in which water molecules form a hydrogen-bonded host lattice that accommodates the guest molecules. While vast amounts of hydrates are known to exist in seafloor sediments and in the permafrost on Earth, these occurrences might be dwarfed by the amounts of hydrates occurring in space and on celestial bodies. Since methane is the primary guest molecule in most of the natural occurrences on Earth, hydrates are considered a promising source of energy. Moreover, the ability of one volume of hydrate to store about 170 volumes of gas, make hydrates a promising functional material for various industrial applications. While the static properties of hydrates are reasonably well known, the dynamics of hydrate formation and decomposition are insufficiently understood. For instance, the stochastic period of hydrate nucleation, the memory effect, and the self-preservation phenomenon complicate the development of predictive models of hydrate dynamics. Additionally, the influence of meso- and macroscopic defects as well as the roles of mass and heat transport on different length scales remain to be clarified.
Due to its non-invasive and non-destructive nature and the high spatial resolution of approx. 1µm or even less, micro-computed X-ray attenuation tomography ( µCT ) seems to be the perfect method for the study of the evolving structures of forming or decomposing hydrates on the meso- and macroscopic length scale. However, for the naturally occurring hydrates of low atomic number guests the contrast between hydrate, ice, and liquid water is typically very weak because of similar X-ray attenuation coefficients. So far, good contrast was only restricted to synchrotron beamline experiments which utilize the phase information of monochromatic X-rays.
In this thesis it is shown that with the help of a newly developed sample cell, a contrast between the hydrate and the ice phase sufficiently good for the reliable segmentation of the materials can also be achieved in conventional tube-based µCT. An accurate pressure and temperature management, i.e., the added functionality of the cell, further allows for cross-correlation of structural and thermodynamic data. The capability of this µCT setup is demonstrated in a series of studies on the formation and decomposition of hydrates which yield new insights for the development of a novel route to hydrate synthesis. At last, this thesis points towards possibilities how better models of hydrate formation and decomposition can be developed with the aid of µCT and computer simulations.

Load shifting of resistive domestic hot water heaters has been done in Europe since the 1930s, primarily to ease the power supply during peak times. However, the pursued and already commenced energy transition in Europe changes the requirements for the underlying logic. In this more general context, demand side management is considered a viable approach to utilize the flexibility of thermal and electrochemical storage systems for buffering energy generated from renewables. In this work, an autonomous approach for demand side management of energy storage systems is developed, which is based on unidirectional communication of an incentive. This concept is then applied to the specific problem of resistive domestic hot water heaters.
The basic algorithms for an optimized operation are developed and evaluated based on simulation studies. The optimization problem considered, maps the search for the optimal heating schedule, while ensuring the temperature limits defined: Firstly, a maximum, which is defined by the hysteresis set point temperature; Secondly, during hot water draw offs, the outlet temperature should not fall below a set minimum. To establish this, the time series of hot water usage has to be predicted.
Depending on the complexity of the hot water heater model used, the formulation of the problem ranges from a linear to non-linear optimization with discontinuous constraints. The simulation studies presented, comprise a formulation as binary linear optimization problem, as well as a solution based on a heuristic direct method to solve the non-linear version. In contrast to the first linear approach, the latter takes stratification inside the tank into account. One-year simulations based on realistic hot water draw profiles are used to investigate the potentials with respect to load shift and energy efficiency improvements. Additional to assuming perfect prediction of user behavior, this work also considers the k-nearest neighbors algorithm to predict the time series. If compared to usual night-tariff switched operation, assuming perfect prediction shows 30 % savings on the electricity market when stratification is taken into account. The user prediction proposed leads to 16 % cost savings, while 6 % of the electric energy is conserved.
Based on the linear approach, a prototype is developed and used in a field test. A micro computer processes the sensor information for local data acquisition, receives electricity spot market prices up to 34 hours in advance, solves the optimization problem for this time horizon, and switches the power supply of the resistive heating element accordingly. Beside the temperature of the environment, the inlet and outlet temperatures, the temperature inside the tank is measured at five points, as well as the water volume flow rate and the electric power recorded. Two test runs of 18 days each, compare the night-tariff switched operation to the price-based optimization in a real-world environment. Results show a significant increase of 6 % in thermal efficiency during the operation based on the algorithm developed, which can be contributed to the optimization accounting for the usage expected.
To facilitate the technical and economic feasibility for retrofit-able implementations of the method proposed for autonomous demand side management, the sensors used must be kept to a minimum. A sufficiently accurate state estimation of the storage has to be achieved, to facilitate a useful model predictive control. Therefore, the last part of this work focuses on the aspect of automated system identification and state estimation of resistive domestic hot water heaters. To that end, real hot water usage profiles and schedules gathered in a field test are used in a lab setup, to collect data on the temperature distribution inside the tank during realistic operating conditions. Four different thermal models, common in literature, are considered for state estimation and system identification. Based on the data collected in the lab, they are evaluated with respect to robustness, computational costs, and estimation accuracy. Based on the observations made in the experiments, an extension of the one-node model by a single additional parameter is proposed. By this adaption, a linear temperature distribution in the lower part of the tank can be modeled during heating. The resulting model exhibits improved robustness and lower computational costs, when compared to the original model. At the same time, the average temperature in the storage tank is estimated nearly as accurate (6 % mean average percentage error) as in the case of the about 50 times more computationally expensive multi-layer model (4 % mean average percentage error).