Mining will remain indispensable for the foreseeable future. For millennia, our society has been exploring and exploiting mineral deposits. Consequently, most of the easily exploitable high-grade deposits, which were of primary interest given their obvious technical and economic advantages, have already been depleted. For the future, the mining sector will have to efficiently produce metals and minerals from low-grade orebodies with complex mineralogical and microstructural properties -- these are generally referred to as complex orebodies. The exploitation of such complex orebodies carries significant technical risks. However, these risks may be reduced by applying modelling tools that are reliable and robust. In a broad sense, modelling techniques are already applied to estimate the resources and reserves contained in a deposit, and to evaluate the potential recovery (i.e., behaviour in comminution and separation processes) of these materials. This thesis focusses on the modelling of recovery processes, more specifically mineral separation processes, suited to complex ores. Despite recent developments in the fields of process mineralogy and geometallurgy, current mineral separation modelling methods do not fully incorporate the available information on ore complexity. While it is well known that the mineralogical and microstructural properties of individual particles control their process behaviour, currently widely applied modelling methods consider only distributions of bulk particle properties, which oftentimes require much simplification of the particle data available. Moreover, many of the methods used in industrial plant design and process modelling are based on the chemical composition of the samples, which is only a proxy for the mineralogical composition of the ores. A modelling method for mineral separation processes suited to complex ores should be particle-based, taking into consideration all quantifiable particle properties, and capable of estimating uncertainties. Moreover, to achieve a method generalizable to diverse mineral separation units (e.g., magnetic separation or flotation) with minimal human bias, strategies to independently weight the importance of different particle properties for the process(es) under investigation should be incorporated. This dissertation introduces a novel particle-based separation modelling method which fulfills these requirements. The core of the method consists of a least absolute shrinkage and selection operator-regularized (multinomial) logistic regression model trained with a balanced particle dataset. The required particle data are collected with scanning electron microscopy-based automated mineralogy systems. Ultimately, the method can quantify the recovery probability of individual particles, with minimal human input, considering the joint influence of particle shape, size, and modal and surface compositions, for any separation process. Three different case studies were modelled successfully using this new method, without the need for case-specific modifications: 1) the industrial recovery of pyrochlore from a carbonatite deposit with three froth flotation and one magnetic separation units, 2) the laboratory-scale magnetic separation of a complex skarn ore, and 3) the laboratory-scale separation of apatite from a sedimentary ore rich in carbonate minerals by flotation. Moreover, the generalization potential of the method was tested by predicting the process outcome of samples which had not been used in the model training phase, but came from the same geometallurgical domain of a specific ore deposit. In each of these cases, the method obtained high predictive accuracy. In addition to its predictive power, the new particle-based separation modelling method provides detailed insights into the influence of specific particle properties on processing behaviour. To name a couple, the influence of size on the recovery of different carbonate minerals by flotation in an industrial operation; and a comparison to traditional methodologies demonstrated the limitation of only considering particle liberation in process mineralogy studies -- the associated minerals should be evaluated, too. Finally, the potential application of the method to minimize the volume of test work required in metallurgical tests was showcased with a complex ore. The approach developed here provides a foundation for future developments, which can be used to optimize mineral separation processes based on particle properties. The opportunity exists to develop a similar approach to model the comminution of single particles and ultimately allow for the full prediction of the recovery potential of complex ores.:1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 State-of-the-art in particle-based separation models . . . . . . . . . . . 11 1.4 Moving forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.4.1 Particle data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.4.2 Mathematical tools required for the particle-based separation model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.4.3 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.5 Structure of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2 The method and its application to industrial operations 23 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.2.1 Assumptions and limitations . . . . . . . . . . . . . . . . . . . . 26 2.2.2 Data structure and required pre-treatment . . . . . . . . . . . . 27 2.2.3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.3 Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3.1 Artificial test cases . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3.2 Real case study . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4 Discussion and final considerations . . . . . . . . . . . . . . . . . . . . 39 3 The robustness of the method towards compositional variations of new feed samples 45 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.2 Generalization potential of current Particle-based Separation Model (PSM) methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3 Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3.1 Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3.2 Dry magnetic separation tests . . . . . . . . . . . . . . . . . . . 53 3.3.3 Sample characterization . . . . . . . . . . . . . . . . . . . . . . 53 3.3.4 Particle-based separation models . . . . . . . . . . . . . . . . . 54 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4 Flotation kinetics of individual particles 67 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.2.1 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.2.2 Cumulative recovery probability . . . . . . . . . . . . . . . . . . 72 4.2.3 Particle-based kinetic flotation model . . . . . . . . . . . . . . . 74 4.3 Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.3.1 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . 75 4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.4 Discussion and final thoughts . . . . . . . . . . . . . . . . . . . . . . . 80 5 Conclusions and outlook 85 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.2 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Bibliography 89

Autonomes Fahren (AF) wird potenziell die Präferenzen für die im Auto verbrachte Zeit stark beeinflussen und dementsprechend den Wert der Reisezeit, der ein Schlüsselelement von Kosten-Nutzen-Analysen im Verkehr ist. Die Untersuchung dieses Aspekts des AF ist daher entscheidend für die Analyse potenzieller Auswirkungen der Technik auf die zukünftige Verkehrsnachfrage. Trotz der steigenden Anzahl an Studien zu diesem Thema, gibt es noch erhebliche Forschungslücken. Der Fokus der Dissertation ist die potenziellen Änderungen des Reisezeitwerts, die durch das AF entstehen, zu messen sowie ihre Determinanten zu analysieren. Es wurden sowohl qualitative Ansätze als auch quantitative Methoden verwendet. Dabei wurden zwei Konzepte von AF betrachtet: privates und geteiltes autnomes Fahrzeug. Die Ergebnisse der Analysen zeigen einen niedrigeren Wert der Reisezeitersparnis beim AF im Vergleich zum manuellen Fahren, allerdings nur auf Pendelwegen. Das private Fahrzeug wird als eine attraktivere Option als ein geteiltes Fahrzeug wahrgenommen, jedoch unterscheiden sich die Nutzerpräferenzen für geteilte Fahrzeug stark zwischen den durchgeführten Studien. Individuelle Charakteristiken, wie Erfahrung mit Fahrassistenzsystemen, beeinflussen stark die Wahrnehmung der Zeit im AF; andere sozio-demographischen Faktoren, wie Alter und Geschlecht haben vor allem einen indirekten Effekt auf den Reisezeitwert indem sie Einstellungen potenzieller Nutzer beeinflussen. Die Verbesserung des Fahrterlebnisses durch das AF und das Vertrauen in die Technik sind wichtige Determinanten der Reisezeitwahrnehmung. Fahrvergnügen und andere wahrgenommene Vorteile vom manuellen Fahren gleichen in einem gewissen Ausmaß den Nutzen vom AF aus. Es wurden Reisezeitwerte für unterschiedliche potenzielle Nutzersegmente berechnet. Abschließend wurden politische Implikationen, Empfehlungen für die Entwicklung von AF sowie Empfehlungen für künftige Studien und potenziellen Forschungsgebiete abgeleitet. / Autonomous driving will potentially strongly affect preferences for time spent in a vehicle and, consequently, the value of travel time savings (VTTS). As VTTS is a key element of cost-benefit analysis for transport, these interrelations are crucial for analysing the potential impact of the technology on future travel demand. Despite the increasing number of studies dedicated to this topic there are still many unanswered questions. The focus of the thesis is to measure potential changes in the VTTS resulting from the introduction of autonomous driving and analyse their determinants. Qualitative approaches and quantitative methods were used. Two concepts of AVs were considered: a privately-owned AV (PAV) and a shared AV (SAV). The analysis results suggest lower VTTS for autonomous driving compared to manual driving, but only on commuting trips. A PAV is perceived as a more attractive option than an SAV, but user preferences for SAVs vary between the conducted studies. Individual characteristics, such as experience with advanced driver assistance systems, strongly affect the perception of time in an AV; other socio-demographic factors, such as age and gender, affect mode choices and the VTTS mainly indirectly by influencing the attitudes of potential users. The improvement in travel experiences due to autonomous driving and trust in the technology are important determinants of the perception of travel time. Enjoyment of driving and other perceived benefits of manual driving partially counterbalance the utility of riding autonomously. VTTS for different potential user segments were calculated. In conclusion, several policy implications, development recommendations for AVs as well as recommendations for future studies and potential research avenues are derived from the findings.

Ziel dieser Arbeit war die Erhöhung der Temperaturtoleranz des Cyanobakteriums Synechocystis sp. PCC 6803 mittels ungerichteter Mutagenese und adaptiver Evolution. Trotz des erneuten Interesses an Cyanobakterien und Mikroalgen in den letzten Jahren, gibt es nur relativ wenige aktuelle Studien zum Einsatz dieser Methoden an Cyanobakterien. Zur Analyse eines mittels Mutagenese erzeugten Gemischs an Stämmen, ist es von großem Vorteil Hochdurchsatz-Methoden zur Kultivierung und zum Screening einsetzen zu können. Auf Basis eines Pipettierroboters wurde solch eine Plattform für phototrophe Mikroorganismen neu entwickelt und folgend stetig verbessert. Die Kultivierung erfolgt in 2,2ml Deepwell-Mikrotiterplatten innerhalb einer speziell angefertigten Kultivierungskammer. Schüttelbedingungen, Beleuchtung, Temperatur und CO2-Atmosphäre sind hierbei vollständig einstellbar.Die Plattform erlaubt semi-kontinuierliche Kultivierungen mit automatisierten Verdünnungen von hunderten Kulturen gleichzeitig. Automatisierte Messungen des Wachstums, des Absorptionsspektrums, der Chlorophyllkonzentration, MALDI-TOF-MS sowie eines neu entwickelten Vitalitätsassays wurden etabliert. Für die Mutagenese wurden die Letalität- und die nicht-letale Punktmutationsrate von ultravioletter Strahlung und Methylmethansulfonat für Synechocystis charakterisiert. Synechocystis wurde mit den so ermittelten optimalen Dosen mehrfach behandelt und anschließend einer in vivo Selektion unterzogen. Somit wurde dessen Temperaturtoleranz um bis zu 3°C erhöht. Über die Screeningplattform wurden die thermotolerantesten monoklonalen Stämme identifiziert. Nach einer Validierung wurde das vollständige Genom der Stämme sequenziert. Hierdurch wurden erstmals Mutationen in verschiedenen Genen mit der Langzeittemperaturtoleranz von Synechocystis in Verbindung gebracht. Bei einigen dieser Gene ist es sehr unwahrscheinlich, dass sie mittels anderer Verfahren hätten identifiziert werden können. / The goal of this work was the increase of the thermal tolerance of the cyanobacteria Synechocystis sp. PCC 6803 via random mutagenesis and adaptive evolution. Even with the renewed interest in cyanobacteria in the recent years, there is relatively limited current research available on the application of these methods on cyanobacteria. To analyse a mixture of various strains typically obtained through random mutagenesis, a method allowing high-throughput miniaturized cultivation and screening is of great advantage. Based on a pipetting robot a novel high-throughput screening system suitable for phototrophic microorganisms was developed and then constantly improved. The cultivation was performed in 2,2 ml deepwell microtiter plates within a cultivation chamber outfitted with programmable shaking conditions, variable illumination, variable temperature, and an adjustable CO2 atmosphere. The platform allows semi-continuous cultivation of hundreds of cultures in parallel. Automated measurements of growth, full absorption spectrum, chlorophyll concentration, MALDI-TOF-MS, as well as a novel vitality measurement protocol, have been established. Prior to the mutagenesis, the lethality and rate of non-lethal point mutations of ultraviolet radiation and methyl-methanesulphonate were characterized for Synechocystis. The thus determined optimal dosages were applied to Synechocystis followed by in vivo selection in four rounds of mutagenesis, thereby raising its temperature tolerance by 3°C. The screening platform was used to identify the most thermotolerant monoclonal strains. After validation, their whole genomes were sequenced. Thus mutations in various genes were identified which promote the strains'' thermal tolerance. For some of the genes it is very unlikely that their link to high thermal tolerance could have been identified by other approaches.

New automated vehicles have the chance of high improvements to road safety. Nevertheless, from today's perspective, accidents will always be a part of future mobility. Following the “Vision Zero”, this thesis proposes the quantification of the driving situation's criticality as the basis to intervene by newly integrated safety systems. In the example application of trajectory planning, a continuous, real-time, risk-based criticality measure is used to consider uncertainties by collision probabilities as well as technical accident severities. As result, a smooth transition between preventative driving, collision avoidance, and collision mitigation including impact point localization is enabled and shown in fleet data analyses, simulations, and real test drives. The feasibility in automated driving is shown with currently available test equipment on the testing ground. Systematic analyses show an improvement of 20-30 % technical accident severity with respect to the underlying scenarios. That means up to one-third less injury probability for the vehicle occupants. In conclusion, predicting the risk preventively has a high chance to increase the road safety and thus to take the “Vision Zero” one step further.:Abstract Acknowledgements Contents Nomenclature 1.1 Background 1.2 Problem statement and research question 1.3 Contribution 2 Fundamentals and relatedWork 2.1 Integral safety 2.1.1 Integral applications 2.1.2 Accident Severity 2.1.2.1 Severity measures 2.1.2.2 Severity data bases 2.1.2.3 Severity estimation 2.1.3 Risk assessment in the driving process 2.1.3.1 Uncertainty consideration 2.1.3.2 Risk as a measure 2.1.3.3 Criticality measures in automated driving functions 2.2 Operational motion planning 2.2.1 Performance of a driving function 2.2.1.1 Terms related to scenarios 2.2.1.2 Evaluation and approval of an automated driving function 2.2.2 Driving function architecture 2.2.2.1 Architecture 2.2.2.2 Planner 2.2.2.3 Reference planner 2.2.3 Ethical issues 3 Risk assessment 3.1 Environment model 3.2 Risk as expected value 3.3 Collision probability and most probable collision configuration 4 Accident severity prediction 4.1 Mathematical preliminaries 4.1.1 Methodical approach 4.1.2 Output definition for pedestrian collisions 4.1.3 Output definition for vehicle collisions 4.2 Prediction models 4.2.1 Eccentric impact model 4.2.2 Centric impact model 4.2.3 Multi-body system 4.2.4 Feedforward neural network 4.2.5 Random forest regression 4.3 Parameterisation 4.3.1 Reference database 4.3.2 Training strategy 4.3.3 Model evaluation 5 Risk based motion planning 5.1 Ego vehicle dynamic 5.2 Reward function 5.3 Tuning of the driving function 5.3.1 Tuning strategy 5.3.2 Tuning scenarios 5.3.3 Tuning results 6 Evaluation of the risk based driving function 6.1 Evaluation strategy 6.2 Evaluation scenarios 6.3 Test setup and simulation environment 6.4 Subsequent risk assessment of fleet data 6.4.1 GIDAS accident database 6.4.2 Fleet data Hamburg 6.5 Uncertainty-adaptive driving 6.6 Mitigation application 6.6.1 Real test drives on proving ground 6.6.2 Driving performance in simulation 7 Conclusion and Prospects References List of Tables List of Figures A Extension to the tuning process