Verlag G. Mainz – förlag

The present thesis deals with the development of immobilized Ruthenium-Triphos catalysts for the selective hydrogenation of esters, amides and CO2 as potential substrates in the context of the valorization of renewable carbon resources.The highly active molecular catalyst [Ru(Triphos)TMM] (C-1a), already investigated in various catalytic approaches and mechanistic studies, was defined as target structure for immobilization. In a detailed preparative study, two synthetic pathways towards [(Ru(Ether-Triphos)TMM] (C-2a) and [(Ru(Carbamate-Triphos)TMM] (C-3a) were developed, using a triethoxysilyl-functionality as anchoring-group in the catalysts backbone, enabling the covalent immobilization on oxidic supports (section 3.1). Subsequently, the complex [(Ru(Ether-Triphos)TMM] (C-2a) was tethered on amorphous SiO2,500, leading to the heterogenized catalyst [(Ru(Ether-Triphos)TMM]@SiO2,500 (C-2b, section 3.1).The immobilized catalyst was successfully tested in ester-, amide- and CO2 hydrogenation reactions in single-batch and continuous-flow experiments. In lactam hydrogenation reactions the immobilized catalyst showed superior long-term stability and activity in comparison to its homogeneous counterpart. Since the homogeneous complex quickly forms an inactive hydridebridged dimer under the employed reaction conditions, the active-site isolation of the tethered complexes on the surface prevents self-deactivation, thus maintaining its activity in several recycling steps. For long-term continuous-flow hydrogenation tests with C-2b, DL-lactide was evaluated as a suitable benchmark-substrate in an extensive ester screening. Here, different catalyst pre-treatments were tested to maximize catalyst activity. High TON (> 4700) over multiple days in flow were reached and the changes that the catalyst material underwent were characterized (section 3.3).After the catalytic evaluation of C-2b the observed Ru leaching was determined to be the major target for further catalyst optimization. Therefore, various modifications in catalyst preparation were performed, including changes in the synthesis order and reaction conditions, additive utilization as well as the incorporation of the complex C-2a into the framework mesostructured SBA-15 towards [(Ru(Ether-Triphos)TMM]@SBA-15 (C-2k, section 3.4). In a final investigation, selected catalysts were evaluated in the direct synthesis of dimethoxy methane (DMM) from CO2, H2 and MeOH. Catalyst C-2k was highly active and superior in terms of low Ru leaching-rates and reached unpreceded activity during recycling experiments (TON >1900 of DMM, section 3.5).

Long Term Evolution (LTE) standardized by 3GPP operates fully packet switched and does not have a circuit switched domain as older systems HSPA, UMTS and GSM. It must support a minimum number of voice calls to be recognized as IMT-Advanced system by ITU. Therefore ITU has published the IMT-Advanced Evaluation Guidelines describing evaluation scenarios and minimum quality of service demands for voice calls to be evaluated by system level simulation. Those guidelines require transmission of VoIP packets within 50 ms over the air interface and packet loss rate below 2 %.3GPP came to the conclusion Voice-over-LTE (VoLTE) with semi-persistent scheduling satisfies the ITU demands. This was verified by independent evaluation groups. Semi-persistent scheduling reserves radio resources for VoIP packets for the duration of a talk spurt while other packets, e.g. retransmissions of lost packets, individually receive resources (dynamic scheduling). Advantage is significantly decreased signaling overhead but the method is more complex than the classical approach individually assigning radio resources for each packet. Individual assignment of radio resources is also used for data traffic of so called Over-the-Top (OTT) VoIP applications.This thesis compares VoIP capacity of VoLTE and OTT focusing on the impact of the radio link. Radio resource assignment optimized for delay critical traffic like VoIP is assumed for OTT to allow a fair comparison. A concept developed by me to design complex VoIP schedulers from basic resource assignment algorithms is described. It is applied for VoLTE and OTT. The openWNS/IMTAPhy simulator developed at RWTH Aachen University ComNets Research Group and extended at TU Munich Chair of Communication Networks is enhanced for VoIP evaluation based on that concept. Methods to improve VoIP capacity are evaluated separately for up- and downlink. Different channel prediction strategies are applied for each direction and based on that transmission rate adaptation is performed.Results show improved capacity through aggressive rate adaptation and resource assignment resulting in higher data rates and therefore reduced resource demand while at the same time increasing packet error rate. Too aggressive parameters significantly reduce VoIP capacity while moderate adjustments increase it by ca. 15 % in the downlink and 10 % in the uplink for VoLTE. OTT cannot benefit from these improvements if the number of control channels is limited and becomes a bottleneck. If more control channels were available than defined by the current standard OTT VoIP capacity could be improved for downlink. High uplink interference variance makes channel prediction more difficult and prevents capacity gains for OTT as achieved with VoLTE.Besides simulation results an analytical discrete time Engset queueing model was described and solved for its waiting time distribution to determine VoIP capacity in scenarios without and with adjacent channel interference from neighbor cells.In the Urban Macrocell scenario lowest downlink VoIP capacity results 43 users / cell / MHz for OTT and 41 users / cell / MHz for VoLTE were obtained with unlimited number of control channels. The Downlink is the bottleneck and therefore determines the overall VoIP capacity. Allowing users with bad channel condition to transmit at higher data rates than suggested by the rate adaptation algorithm reduces resource occupation but at the same time increases packet error rate. VoIP capacity is increased to 48 and 47 users / cell / MHz this way for OTT and VolTE, respectively.From this it is concluded that dynamic scheduling used by OTT is capable to provide sufficient VoIP capacity without the complexity introduced by SPS given that sufficient control channels are available, as expected in reality.

Synthetic organic chemistry aims toward the development of methods for the practical synthesis of important molecules such as bioactive ingredients, pesticides, dyes, and polymers. Although extraction of organic molecules from natural resources is possible, organic synthesis often provides a cheaper and more practical access to them from readily accessible starting materials, as well as the possibility to artificially modify the structure of naturally-occurring molecules to increase their activity or modulate their properties. In medicinal chemistry, a standard approach to find the most potent active ingredient is the so-called combinatorial sythesis strategy: after identification of a promising structure, often a small molecule found in nature, a library of compounds with different substitution patterns is prepared and tested to find the bist hit.

Knowledge of the phase behavior of liquid–liquid systems is indispensable for the design and optimization of process equipment. However, the ab initio prediction of liquid–liquid equilibria (LLE) is not yet reliable. Therefore, experimental generation of LLE data is still necessary. Current methods for the characterization of LLE consume large amounts of material, and the large volumes lead to large diffusion paths and hence long equilibration times, unless the mixture is thoroughly stirred. The components are typically quantified chromatographically, because chromatographic methods allow for the analysis of a broad range of chemicals. Automated setups are able to reduce the overall system volume, but merely determine partition ratios. They often use spectrometric quantification, because only one component is of interest. The use of spectrometry instead of chromatography leads to shorter times for analysis, but restricts the range of chemicals to optically active compounds.In this work, the benefits of the automated setups and of the conventional characterization of LLE have been combined: a platform has been set up to reduce the need for human interaction from sample preparation, to chromatographic analysis, up to data processing and reduction. The system volumes could be reduced from typically 25mL in jacketed equilibrium cells to 1 mL. The developed calibration technique allows for a substantial reduction in material, because neither solvents nor standards are needed. Instead of long equilibration times, now analytics is the bottleneck in the characterization of LLE, because chromatographic methods are rather slow. Therefore, the use of model-based optimal experimental design was investigated for the characterization of LLE, so that the characterization time could be reduced by reducing the number of LLE experiments. The optimal designs concentrate on few distinct experimental settings, and a large fraction of the experimental effort is put on areas where the curvature of the binodal curve is large. By applying model-based optimal experimental design, the prediction accuracy could at least be improved by a factor 2. Consequentially, the number of experiments could be reduced by a factor 2 without losing prediction accuracy compared to conventional designs. The developed automated setup was used to investigate the phase behavior of novel biofuel-blends with water. It was found that the water solubility in pure biofuels was up to 4 orders of magnitude larger than in conventional fuels. However, by adding blend components, the water solubility could be drastically reduced. The biofuel solubility in water is much larger than the solubility of conventional fuels in water. The blend components did not influence the solubility of conventional fuels in water.

Ever since humans have existed, they have impacted the earth in many different ways (Redman, 1999). Currently, important impacts are associated with the excessive use of non-renewable fossil fuels such as coal, oil and natural gas. Most fossil fuels are used for electricity generation, heating and mobility (eia, 2011), and as feedstock in the chemical industry (IEA et al., 2013). Moreover, the use of fossil fuels is associated with carbon dioxide emissions (CO2) (IEA, 2014; Leimk¨uhler, 2010). Emitting CO2 into the atmosphere leads to global warming and disrupts the natural carbon cycle (Stocker et al., 2013). To close the disrupted carbon cycle, CO2 can be captured and re-utilized, thereby mitigating global warming and saving fossil resources (Styring et al., 2014).CO2 can be captured from current anthropogenic CO2 sources or directly from the atmosphere. Captured CO2 can then be utilized as valuable physical product “as such”or as alternative carbon feedstock for fuels, chemicals and materials. The general concept of CO2 Capture and Utilization (CCU) can be considered established: already today, CO2 is captured and utilized in processes in the chemical industry (Aresta et al., 2014). However, the scope of CO2 utilization is limited. Despite the existing industrial implementations as well as continuous progress and current efforts in CCU research, most CCU technologies are still in early stages of development. Besides the limited technological readiness, CCU is intrinsically challenging since both capture and utilization of CCU typically require substantial amounts of energy (Sakakura et al., 2007). If the provision of energy relies on fossil resources, indirect CO2 emissions are caused. Therefore, the intuitively expected environmental benefits from using CO2 are not given by default (Peters et al., 2011b). In fact, it cannot be ruled out that a tediously accomplished CCU process is finally environmentally less sustainable than a conventional fossil-based route. Therefore, it is desirable to know whether a specific CCU process is environmentally favorable. For this purpose, a reliable environmental assessment of CCU is required.As indicators for the environmental performance of CCU, a large variety of approaches are proposed ranging from qualitative design principles (Anastas andWarner, 1998) and metrics for ‘green’ chemistry (Constable et al., 2002) to CCU-specific ad-hoc criteria (Peters et al., 2011b; M¨uller and Arlt, 2014). These approaches are rather intended to guide the development towards ‘sustainable’ CCU processes than to systematically quantify the actual environmental impacts. In contrast to these approaches, Life-Cycle Assessment (LCA) is a systematic and standardized methodology to analyze the actual environmental impacts of products and processes (ISO 14040, 2009). Although LCA is frequently advocated for the environmental assessment of CCU (Aresta and Dibenedetto, 2007b; Peters et al., 2011b; Quadrelli et al., 2011), it is not yet standard practice (Sch¨affner et al., 2014). The reasons for this are the complexity of LCA as well as the limited data availability of many CCU processes at early design stages (Quadrelli et al., 2011). In this context, this thesis pursues two major goals: First, the thesis enables and supports the reliable environmental assessment for CCU processes using LCA. To overcome the complexity of LCA and to enable LCA novices to apply LCA to CCU, a jargon-free introduction is presented for LCA in the context of CCU. Furthermore, a framework for LCA of CCU is derived to avoid severe pitfalls in LCA of CCU. A case study for CO2-based polymers illustrates the application of LCA as well as the size and origin of environmental benefits of CCU. The second goal of this thesis is to provide an LCA-based approach to support the design of environmentally beneficial CCU processes at early stages. In summary, the thesis is intended to facilitate the utilization of LCA for CCU from early design stages to industrial implementation.

One of the most important tools in the metal forming technology is the integrated process and microstructual simulation using Finite Element Methods (FEM). It has become more and more popular in recent years, especially in the segment of hot metal forming, e.g. open die forging of large scaled and hardly deformable materials, such as nickel-based super alloys for turbine shaft. Theoretically using this method it is possible to calculate the microstructual evolution along the whole process chain in the numerical simulation of the considered metal forming process. Based on this knowledge a series of benefits can be achieved for the practice, such as to optimize a present metal forming process, to predict the mechanical properties of the final products under the given forming conditions, to detect the possible product failures prematurely, to assist the design of a new production chain and so on. In the face of these trends of the scientific research as well as the industrial demands, different material models have been released in the market to combine the commercial FEM programs specialized in the numerical simulation of metal forming. Among others microstructure-based ow stress models show outstanding performance. Through this kind of material model not only the microstructure such as recrystallization and grain size, but also the interaction between the microstructual evolution and the working hardening, effectively ow stress, can be quantitatively represented.Towards accurate and efficient material modeling, the model parameters have to be determined conveniently and reliably. For this propose a new Hybrid strategy combining the advantages both of direct and indirect methods has been proposed using the example of StrucSim, which is a very good representative of a mircostructure-based ow stress model. At first different aspects, which lead to the disadvantages of the conventional method, i.e. direct method, were discussed. In doing so a high manganese steel was characterized as an example by stepwise graphical and regression analysis. It was found that, the precondition of direct methods, namely recording ow curves under constant Zener-hollomon-parameter conditions, are basically not possible due to both limitations of test equipment and unconquerable physical mechanism like dissipation heating. The common solution to compensate these factors may lead to further inaccuracies, uncertainties and complexities despite large testing and evaluating efforts. Further in order to improve the model quality calibrated by the conventional direct method an efficient hybrid strategy has been derived by combining inverse analysis with offline calculation of ow stress and microstructure. Three different variations of the hybrid strategy were introduced to deal with different available experimental databases, such as isothermal and non-isothermal ow curves. To demonstrate the developed routines of these three hybrid possibilities two kinds of materials including a nickel-based super alloy and a high manganese steel have been taken into account. The investigation has shown that through the introduced hybrid methods better model quality can be achieved even with less experimental data. Owing to the convenience of the inverse technique much experimental and evaluating effort and complexities can be avoided. Finally, another inverse analysis based on inhomogeneous deformation has been proposed, in which hot compression tests with double cone specimen were employed. Thanks to the inhomogeneity of strain and microstructure distribution within the specimen, it becomes possible to get sufficient relevant information as constraints for the inverse parameterization through even fewer experiments. In addition, the established routine of a hybrid strategy as well as the inverse analysis based on non-uniform deformation enhances the transferability of determined model coefficients between two materials within the same group or among similar materials.

The design and optimization of complex chemical processes is a key challenge in chemical engineering and requires knowledge of the underlying kinetic model. This information can be obtained from experiments by inverting the reaction mechanism, which needs to be known therefore. Solving this inverse problem, however, is mathematically challenging, if not impossible, and the reaction mechanism is mostly unknown for novel compounds. Both of these challenges are addressed in the present thesis by proposing a novel methodology for forward reaction model development, which is based on exploring chemical space without the need for prior knowledge.The presently proposed methodology makes use of reactive molecular dynamics simulations to explore the chemistry of gas-phase compounds. In these dynamic simulations the chemical systems are allowed to evolve naturally, based on the atomistic interactions. During this evolution, bond formation and cleavage are traced based on the atomic connectivities and used to detect reaction events. For each reaction, molecular structures are extracted and high-level quantum mechanical calculations are used to predict reliable thermochemistry and kinetics. This novel chemistry exploration scheme is used to generate an ab initio reaction model for the well-studied high-temperature methane oxidation, which is used as a reference. The ab initio reaction model and a novel reaction pathway observed during simulation are validated against this reference and against high-level quantum mechanics.The comparison of the present ab initio reaction model obtained for high temperature methane oxidation to well-established literature reaction model shows striking agreement. This validation case demonstrates the potential of forward reaction model development using the present purely predictive methodology. Moreover, a reaction pathway previously not considered in kinetic modeling is discovered using the present chemistry exploration scheme and successfully validated in a detailed kinetic study.Potential extensions to the presented chemistry exploration scheme are derived, discussed, and implementations are outline. These extensions focus on the inclusion of effects resulting from microscopic balancing: Pressuredependence and reactions involving non-thermal intermediates. Conversion of high-pressure limit reaction models to pressure-dependent models is intended to be described by microcanonical properties obtained via transformation of canonical properties. A similar transformation is used to obtain information about hot reactions, i.e. the kinetics of non-thermal intermediates. Ultimately, these extensions will be implemented in the presently proposed chemistry exploration scheme to obtain even more accurate ab initio reaction models.In conclusion, the present thesis addresses the increasing need for reaction model development of novel chemical compounds by proposing a novel chemistry exploration scheme. The agreement of reaction pathways and rate constants with literature data reveals the potential of trajectory-based chemistry exploration for developing quantitative reaction models.

Optimal performance of chemical processes requires both optimized operating conditions and carefully selected molecules such as solvents. However, the search for optimal molecules and process concepts often has a limited focus: Either processes are optimized using a pre-defined set of molecules or molecules are selected for novel applications based on simplified process indicators. At the same time, the search for optimal molecules often relies on strongly simplified thermodynamic models that require experimentally determined group interaction parameters and confine the molecular design space. Overall, current design approaches often do not capture complex process trade-offs and are limited to prescriptive sets of molecules which likely results in suboptimal choices.To address the challenge of identifying optimal processes and molecules, this thesis presents an integrated computer-aided molecular and process design (CAMPD) approach. The design approach uses quantum mechanics (QM)-based property prediction by COSMO-RS and is thus independent of experimental determined group interaction parameters while not relying on group additivity. For reliable and fast evaluation of complex processes, advanced pinch-based process models are employed. These pinch-based process models account for the inherent trade-off in molecular properties while being both computationally efficient and accurate in comparison to rigorous process models. The integrated design approach in this thesis is stepwise extended from process-level molecular screenings towards molecular design for separation and reaction-separation processes. The application of the presented integrated design approach is illustrated for various examples of solvent selection and process optimization. In particular, process concepts and solvents are investigated for the purification of bio-based platform chemicals as well as the production of CO from CO2. Overall, this thesis successfully integrates COSMO-RS property prediction in CAMPD and thus significantly expands the range and applicability of current CAMPD approaches.

Most industrial processes for producing bulk and fine chemicals are transition metal catalyzed. Toxicity and high cost of platinum group metals are triggering the current search for sustainable catalysts. In this regard, nontoxic and earth-abundant alkali metals could become attractive alternatives. They are the most electropositive elements of the periodic table. With a single s electron in the valence shell and a low ionization enthalpy they easily form M+ ions. The vacant, low-energy orbital of the M+ ion is capable of accepting an electron pair. This Lewis acidic property can be utilized in molecular catalysis. Since alkali metal compounds easily dimerize, oligomerize or even polymerize (Scheme A.1-1), suitable ligands are crucial for the isolation of defined metal complexes in their monomeric form.

Optimal operation and control of batch and semi-batch processes has an increasing importance in the chemical process industry. However, the solution of dynamic uncertainties in the process model might lead to a high risk of constraint violations. In order to solve the dynamic optimization problem under parametric uncertainty, four different approaches are investigated in this work. Thereafter, two of them are used in a multi-scenario eNMPC in order to robustly control batch processes under parametric uncertainties.

A major application area of Nuclear Magnetic Resonance (NMR) is the study of porous materials which started in the early days of NMR when Torrey investigated the complex diffusion dynamics of fluids inside porous media. Since then, NMR relaxation has been explored extensively in studying porous materials such as cement and subsurface-rock formations. However, fibrous substrates and in particular thin fibrous materials have received much less attention. Such materials are employed in a wide range of products such as fuel-cells, paper, filters, fluid absorbents and barrier materials, textiles, diapers, and pads. In this thesis single-sided NMR has been used to study the interaction of Newtonian and non-Newtonian fluids with the fibrous porous materials. Furthermore, the experimental results are used to validate the numerical models that simulate fluid flow in these materials.In the first step, single-sided NMR with the NMR-MOUSE® is employed for quantification of the fluid amount. Using 2 mM/l of a Gd3+ relaxation agent the repetition time could be shortened to 250 ms, improving the correlation coefficient between liquid amount and signal amplitude from R2 = 0.893 to R2 = 0.982 for different liquid and porous materials. To assess reproducibility and instrument precision, calibration experiments were repeated several times and their variation was investigated. The results showed that the device is highly precise and robust with a standard deviation for liquid quantification of less than 1%. In the second step, the NMR-MOUSE® is used for dynamic measurements of drainage processes through thin porous layers. The experimental results from NMR were used to improve the continuum-scale modeling of liquid flow from the top to the bottom layer. Thin fibrous porous materials are mostly used in stacks of layers, each layer having a defined functionality. Since only a few pores exist across a layer a couple of hundred microns thick, the interface between layers may significantly affect the liquid ingress. The Fourier NMR-MOUSE® device with a low static gradient was used to profile a 2-mm thick slice in one shot. The liquid ingress into the thin fibrous layers and their interfaces was visualized by Fouriertransforming the NMR signal and processing the time-dependent 1D profiles with a newly developed mathematical method. The results show major differences in distributions and flow dynamics for the single and dual layer cases, which reveal the importance of the interface in fluid flowMoreover, a new device called Multivariable NMR Acquisition System (MNAS) has been developed and employed to study the liquid flow inside multi-layered swelling absorbent articles such as diapers. The experimental procedure which consist of a dynamic measurement and profiling measurement, was utilized to investigate the performance of four different absorbent products. The information obtained from these measurements could be used in developing new products. The flow of non-Newtonian liquids inside fibrous porous media was studied numerically as well as experimentally. It was shown that the single-sided NMR is a powerful tool to study the flow and distribution of non-Newtonian liquids in thin fibrous porous media.

Synthesis of energy systems is a complex design task with a plethora of decision options. To evaluate these decision options, mathematical optimization is often used to identify the optimal solution. However, for decision support, more information than just the optimal solution is required. The decision maker needs to know design alternatives and their trade-offs to make a well-informed decision. Hence, mathematical optimization should be used as tool to generate multiple design alternatives. One way to generate design alternatives is the exploration of the near-optimal solution space.In this thesis, a decision support system is proposed for decision support by analysis of the near-optimal solution space. The near-optimal solution space consists of the near-optimal design space and the near-optimal objective space. For exploration of the objective space, a method is proposed to efficiently identify solutions which reveal trade-offs in the objective functions. For the design space, a method is proposed to span all near-optimal design alternatives by minimizing and maximizing design variables. The decision support system provides a holistic analysis of the near-optimal solution space by combining solutions from the near-optimal objective space and the design space. All generated solutions are analyzed to reveal feasible ranges of variables and objective functions. Additionally, the analysis determines trade-offs between decisions in both the design space and the objective space. Based on the results of the analysis, the decision maker can derive preferences. In an interactive feedback loop, these preferences are added to the synthesis problem to support the final synthesis decision.The proposed decision support system is applied to two real-world case studies. The first case study originates from pharmaceutical industry and focuses on the supply side of an energy system; the second case study is a retrofit of an urban energy system and also takes into account demand-side measures such as investments in insulation. For these two entirely different case studies, the decision support system provides decision support by identifying feasible designs, their costs and emissions, and the most important design trade-offs. Thereby, the decision maker is enabled to take well-informed decisions in the synthesis of energy systems.

Sensors for stray-field nuclear magnetic resonance (NMR) have been employed to measure tangible cultural heritage since they were first conceived. Although NMR is a method with inherently low sensitivity and requires larger amounts of time than many other techniques, it is non-destructive and grants access to spin densities and relaxation times, physical quantities which are exclusive to the method. This thesis describes theory, instrumentation, and applications of unilateral NMR in the field of cultural heritage.The detection zone of an NMR-MOUSE, a sensor developed and maintained by past and present members of the group of Professor Blümich, is mapped through back-projection for the first time. Traditionally, the signal arising from the detection volume was averaged over multiple scans until the signal response was satisfactory. In this work, an algorithm is proposed to improve the signal-to-noise ratio in magnetization decays as a method of post processing an output, which contains the individual data from smaller sub-experiments.One of the most prominent fields of application for unilateral NMR is the investigation of porosity in stone and soil. In a study of ancient mortars in Herculaneum, a city buried by the ashes of Mount Vesuvius during the famous eruption of 79 AD, the profiles of over 60 sites, fragments and mock-ups were compared for the first time with methods known from statistics and pattern recognition. The effects of high temperatures up to 900 °C on low and high-density sandstone were determined in terms of their transverse relaxation times. Mock-ups were cross referenced with actual walls and fragments to assess the damage of the western wall of the burnt down Mackintosh Library in Glasgow.Studies of paint show how stray-field sensors can help evaluating solvent activity and their potential as cleaning agents. Relaxation times assess embrittlement and transient softening caused by such treatments. Furthermore, the potential to detect or even discriminate natural and artificial aging of binder polymers is explored.The impressive sound and quality of historic instruments by Stradivari and his contemporaries is still unsurpassed in the opinion of many musicians, but the reason for this remains controversial. When tested with unilateral NMR, violins and violas from the golden age of luthiers revealed a homogeneous wood density throughout their maple back plate. Shorter relaxation times at the wood surfaces may be traces of previous treatments.Even though unilateral NMR equipment was claimed to be portable for many years, it had relied on grid power and shielding from weather. This work presents the efforts of measuring biofilms in Yellowstone National Park, relying on battery power to perform the non-destructive tests. An NMR-MOUSE was constructed and waterproofed to detect biofilms under water and in-situ.

The conductor-like screening model for realistic solvation (COSMO-RS) is applied to study three case studies of complex chemical systems. In the first chapter, cellulose solubility in ionic liquid (IL) solvents is considered and a molecular model for cellulose is presented for the calculation of its solubility in ILs and their mixtures with organic molecular solvents. The model is based on cellobiose units which were themselves obtained by a conformation search for a cellotetraose unit. The conformations that make up the model hence can take into account interactions of a dissolved cellulose repeating unit while making sure that the conformations are so that the repeating unit resembles a unit in the middle of a cellulose chain. It is shown that the model contains representations of all of intra-molecular hydrogen bonds as well as open hydrogen bonding sites for accepting inter-molecular ones witnessed experimentally. Relative cellulose solubilities in IL systems with cations [Amim], [Apyr], [C2OHmim], [C2OHpyr], [C2OC1mim], and [C4mim] and anions [Cl], [Br], [N(CN)2], [CH3CHOHCOO], [CH3COO], [HOCH2COO], [(C6H5)COO], [H2NCH2COO], [C2N3], [(C2H5)2PO3OH] and [HCOO] have been studied. In addition, cellulose solubility in mixtures of [C2mim][CH3COO] and 14 molecular solvents could be reproduced. Finally, a solvent screening over a large database of cation/anion pairs was carried out, providing for a design and discovery guide pertaining to novel IL systems for cellulose dissolution.

The increasing worldwide concerns about the danger of climate change have pushed goverments to strictly regulate CO2 emissions of automobiles. This development has led the auto industry to focus on design concepts that can reduce fuel consumption, and subsequently emissions. The power generation and tranmission systems, which lead the fuel consumption inefficiency in a car, are the leading factors for high emissions. The conventionally bulky structure of automobiles comes second. Thus improvements on autobody design can decrease the emissions significantly.

For the design and control of chemical processes, models that can accurately describe the physicochemical interactions of the process are needed. However, even with systematic modeling frameworks, the validation of predictive models is time and cost consuming. The generation of experimental data required for model validation often ensues in considerable effort and costs, which can be reduced via optimal experimental design (OED) methods. This thesis addresses the question on how to obtain predictive models for reactive complex chemical processes with the least experimental effort for a given application.The different challenges encountered during modeling complex reactive systems are discussed in Chapter 2. To this end a tutorial is presented for modeling chemical systems exhibiting dynamics on different time scales owing to fast (equilibrium-limited), and slow (kinetically-limited) reactions. The presented systematic modeling approach, complementing existing literature, is based on index reduction of differential-algebraic-equation systems and is easily incorporated into typical modeling procedures. The approach is illustrated using Michaelis-Menten kinetics and is used for parameter estimation in the methyl benzoate hydrogenation case study.In Chapter 3 OED for bounded-error estimation is covered. The worst-case OED formulation is based on literature and results in a bilevel optimization problem. In this thesis, an improved rigorous solution method for boundederror OED is proposed that can guarantee a global solution. The min-max bilevel OED problem is solved using an adaptation of a generalized semi-infinite program via restriction of the right hand side. The algorithm employed has the advantage that it guarantees a global solution for the OED assuming that both the upper-level and lower-level problems are solved globally. In the case of a local solution for the upper-level and a global solution of the lower-level problem, the solution is feasible, however, it is an upper bound of the global solution. For simple chemical reactions the OED problem can be solved globally in a couple of seconds. However, for more complex problems (modified prey-predator example) a global solution cannot be guaranteed with state-of-the-art global solvers, since the upper-level problem does not converge.The bounded-error OED formulation is extended to a new goal-oriented OED formulation in Chapter 4 that tailors the model precision to its intended end application. In comparison to typical OED methods, instead of trying to improve the precision of all parameters, the proposed method aims at minimizing a metric of the intended process. The method is developed for model-based process design and aims at mitigating a worst-case realization of the process cost, and is called therefore, optimal experimental design for optimal process design (OEDOPD). The OED-OPD formulation results in a min-max-min problem and is solved using an ad-hoc solution method, since algorithms for general nonconvex, nonlinear trilevel problems do not yet exist. The solution method is based on the discretization of upper-level variables. The benefit of using tailored OED methods in comparison to typical (classical) OED formulations is demonstrated via two examples: a simple illustrative example and the van de Vusse reaction. Both examples can be solved globally and fairly quickly, in 200 CPU seconds in average for each discretization point. The two examples showed that the experimental costs could be reduced in comparison to classical OED methods while still resulting in a satisfactory process design.

In the context of this work, three unilateral NMR sensors were designed, constructed and characterized. For the new sensors, the concept was chosen to vary the angle of the polarization of the magnets. This affects the size and the position of the sensitive slice. The MOUSEPAD, based on the modified design of the Profile NMR-MOUSE with 5 mm penetration depth, achieved a penetration depth of 17 mm. The Mini-MOUSEPAD is based on the Mini-MOUSE and has an increased penetration depth of 2.5 mm. The penetration depth is a crucial parameter for investigations with unilateral NMR, since the object of interest can be arbitrary shaped and the relevant areas at higher depths might be difficult to access. Therefore, these sensors might show a strategy for further miniaturization of state-of-the-art unilateral NMR sensors. The special magnet arrangement of both sensors results in a reduction of the magnetic field strenght (4.9MHz for the MOUSEPAD and 9.0 MHz for the Mini-MOUSEPAD) and much lower gradient strenght compared to prior state of the art sensors. Consequently, the weaker diffusive attenuation leads to a decreased diffusion weighting. Furthermore, in combination with the extended lateral size of the sensitive area, it leads to an increased SNR, compared to the PM5 and Mini-MOUSE. Sensors with higher sensitivity benefit from shorter measurement times and a higher information density. The third sensor is the Hexarray. It is a compact (110 x 95 x 30 mm³, 1.6 kg) unilateral sensor with two sensitive clices. It operates at 4.9 MHz at 4 mm above the sensor surface. If one coil is oerated while the other is in the waiting period and vice versa it is possible to perform measurements quasi-simultaneously without any mutual interactions. The setup can be used to reduce the measurements time. If both spots measure a similar sample, this setup can be used to increase the SNR by adding up both decays. To exploit the full potential of these sensors, further work is necessary.

Consequential Life Cycle Assessment (CLCA) aims at capturing the environmental consequences of decisions such as the introduction of a new technology, the implementation of a policy, or the purchase of a product. CLCA combines technical and economic modeling approaches to track the consequences of decisions throughout the economy, considering both technical relationships within industrial production systems and market-mediated effects. However, although CLCA is well defined at a conceptual level, a commonly accepted modeling framework for CLCA is still missing, leading to wide differences in CLCA practice.To promote the systematization of the CLCA approach, this thesis proposes a comprehensive modeling framework for CLCA: the Technology Choice Model (TCM). Compared to existing approaches, TCM captures market-mediated effects in multiple markets at a substantially higher level of technical detail, while systematically considering constraints in factor availability, uncertainty, and suboptimal decisions. Due to its higher level of technical detail, TCM can model changes in technology mixes through both capacity adaptions and substitution effects among competing technologies. These changes in technology mixes are shown to substantially affect the CLCA results in two illustrative case studies on the introduction of new technologies and climate policy. Furthermore, the consideration of uncertainties and suboptimal decisions provides the basis for a first comprehensive uncertainty assessment in CLCA.The practical application of TCM is demonstrated in a large-scale industrial case study on novel Carbon Capture and Utilization (CCU) technologies in the chemical industry. These technologies use carbon dioxide from industrial point sources or ambient air as alternative carbon feedstock for chemical production. The case study shows that CCU in the chemical industry can reduce up to 3.5 Gt CO2-eq greenhouse gas emissions per year by 2030 and highlights potential barriers for CCU implementation. The results provide a strong scientific basis for the integration of CCU into international policy frameworks and research agendas. The application of TCM in this case study demonstrates the ability of CLCA to provide sound environmental decision support.

Transition metal catalysis plays a crucial role in the development of new chemical transformations, which can be broadly applied in organic synthesis, medicinal chemistry, synthesis of biologically relevant molecules, pharmaceuticals and other related fields. In recent years, the need for application of sustainable methods is significantly growing due to the necessity of waste-free transformations. Plenty of industrially run processes still apply classical procedures, which often lead to production of tons of waste as a consequence of multistep synthesis. Thus, development of novel catalytic systems which would afford complex molecular structures via straightforward processes is still desired. Hence, metal-catalyzed transformations play a crucial role in the development of new synthetic strategies, as they can easily lead to the reduction of synthetic steps and, ipso facto, reduction of waste leading to atom-economic transformations. Application of transition metals particularly in homogenous catalysis was initiated by industrial processes such as carbonylation of alkenes and alkynes by metal carbonyls, production of polyethylene and polypropylene by the Ziegler-Natta catalysts or conversion of ethylene into acetaldehyde in the Wacker’s process. This breakthrough inspired investigation of other synthetic reactions, which could be catalyzed by transition metals. Soon after, the field was dominated by application of catalysts containing precious-metal centers leading to the facile synthesis of higher molecules. Significant role of transition metals in chemistry was highlighted by Nobel prizes in 2001 (K. B. Sharpless, W. S. Knowles, R. Noyori) for metal-catalyzed enantioselective hydrogenation and oxidation reactions, in 2005 (R. Schrock, R. Grubbs, Y. Chauvin) for application of transition metals in metathesis and in 2010 (R. F. Heck, A. Suzuki, E. Negishi) for application of Pd-based systems in cross-coupling reactions, which with enormous impact modernized organic synthesis in research laboratories and in industry. However, the application of late and noble metal catalytic systems exhibits crucial drawbacks. Noble metals are expensive, due to their low abundance in the earth crust (Figure 1)[4] and related challenges in their extraction. Moreover, they are toxic and lead to formation of toxic waste.Taking into account the huge impact of catalysis nowadays and how it can influence our life and environment, modern chemical society focuses on finding more economically and environmentally friendly methods. As an alternative, implementation of base metal catalysis gained a lot of attention as it can lead to most cost-effective and more environmentally friendly protocols. Moreover, base metals can demonstrate similar reactivity as noble metals or exhibit new features, leading to other interesting transformations.

The audio bandwidth of digital landline and mobile telephone networks is still mostly restricted to 200 Hz to 3.4 kHz. This is due to compatibility requirements during the transition phase from analogue to digital transmission technology. The resulting characteristic "telephone speech" is widely accepted, but the intelligibility of syllables is only 91%.Meanwhile, improved coding standards for so-called “HD voice” or “Wideband Speech” have been developed which are gradually being introduced into the networks. They support an audio frequency bandwidth of 50 Hz to 7.0 kHz with significantly increased audio quality and speech intelligibility. For a very long time however, new HD-telephones and old narrowband telephones have to co-exist. If an HD-terminal is connected over a narrowband link to an old telephone, the improved coding scheme cannot be used.In this thesis, signal processing concepts are developed for improving audio quality and intelligibility of narrowband speech by artificial bandwidth extension (ABWE). These algorithms can be applied in the HD terminals or in the network, to transform narrowband speech to HD voice. Based on the source-filter model of speech production and a priori knowledge of the characteristics of speech signals, the missing frequency components between 3.4 kHz and 7 kHz are reconstructed. In comparison to the state-of-the-art ABWE approaches, the main contributions are:• new concepts of estimating the wideband spectral envelope, e.g., in terms of the model filter by interpolation in the acoustic tube domain• algorithms for spectral extension of the excitation signal• new insights concerning the relative importance of the excitation, the temporal envelope and the spectral envelope• remarkable improvements of the audio quality • significant low computational complexity• efficient and effective training and estimation algorithms The improvements are verified by objective evaluation and subjective listening tests.

The perception of one’s own voice is distorted when telephoning with headsets, or wearing hearing aids. The reason for this is the so-called occlusion effect, which occurs when ear canals are completely or partially closed by the headset or hearing aid. The occlusion causes amplification at low frequencies, and attenuation at high frequencies of one’s own voice. The unnatural perception of one’s own voice and of noise caused by chewing and swallowing are among the most common complaints of users. Furthermore, environmental noise might impair perception. In this thesis, both the unnatural perception of one’s own voice and the disturbance by environmental noise are tackled by a novel signal processing approach. The proposed solution solves the problem of the occlusion effect by actively emitting a compensation signal through the integrated loudspeaker. The approach is called Occlusion Effect Cancellation (OEC) and significantly improves the perception of one’s own voice and of the acoustic environment. This novel approach combines methods of active noise cancellation (ANC, Noise Cancelling Headphone) with a personalized design. The bilateral headset contains two additional microphones per side, one inner and one outer, to acquire signals for the calculation of the compensation signals. A correctly balanced processing of the two microphone signals results in a "digital ear opening" and a much more natural perception of both one’s own voice and of the environment. The extent of the digital ear opening is controllable. The system can also be operated as a noise cancelling headphone by changing the parameters to a conventional design to create an acoustic isolation from the environment.This thesis proposes a novel robust approach based on digital filtering to solve the described problems. A combination of feedback and feedforward filter design allows for either approaching personal silence or a natural perception of one’s own voice and the acoustic environment.

In the last decades, C–H activation became an increasingly powerful tool for C–C bond formation. It provided a valid alternative to conventional cross coupling reactions with maximised atom- and step-economy for synthetic reactions. Even though a lot of great advances have been made, some challenges in C–H activations remain. C–H bonds are highly stable (110 kcal/mol), often harsh reaction conditions are needed to break the bond and form a new C–C compound. Furthermore, regioselectivity has been proven to be a difficult issue since a number of C–H bonds are present in organic molecules. Also, chemoselectivity still remains a difficulty to overcome in C–H activation reactions. Many groups focus their research on developing mild reaction conditions that proceed at “ambient temperature, neutral conditions and in absence of strong oxidants or reductants”.[1b] By choosing milder reaction conditions also problems of selectivity can be managed. In general, the catalytic cycle of C–H activation reactions follows four reaction steps: first is the C–H activation itself, where a substrate binds to the metal that catalyses the reaction process (I). Next is the functionalisation of the organometal intermediate with an appropriate reaction partner (II). By releasing the product (III) and regeneration of the catalyst, e.g. with an oxidant (IV), the catalytic cycle is closed.

The global demand for cooling increases and poses a challenge for the stability of electrical grids and the emission targets. Thermally-driven adsorption chillers can meet this challenge by substituting electricity with thermal energy. However, although adsorption chillers are primarily driven by thermal energy, practical experience shows that the electricity savings often hardly compensate the electricity demand of the auxiliaries, such as pumps and fans. Thus, smart systems integration is crucial. Systems integration of adsorption chillers is challenging due to several features: (1) intrinsic dynamics; (2) close interaction between various system components; and (3) strong coupling of design, control, and operating conditions. These features lead to a variety of degrees of freedom, which make it difficult to use simple design and control rules for systems integration.To move towards optimal systems integration of adsorption chillers, this thesis provides appropriate models and methods. First of all, a model of an adsorption chiller is developed, which is accurate enough to consider quantities needed on system level, but also computationally sufficiently efficient for model-based optimizations. To demonstrate the model accuracy, the model is calibrated and validated for two types of adsorption chillers, a lab-scale one-bed adsorption chiller and a commercial two-bed adsorption chiller.Subsequently, the presented model is used for two areas of systems integration: the integration in hybrid systems and the integration within the auxiliaries. To exemplify the integration in a hybrid system, an adsorption chiller is integrated into a CO2 vapor compression cycle to efficiently provide refrigeration with natural refrigerants only. The exemplified integration identifies a significant potential for energy savings, which can be fully exploited by optimal design and control of the hybrid system. The demonstrated integration in hybrid systems is also an example for a conceptual integration, which quantifies the potential of a specific application.To integrate the adsorption chiller within the auxiliaries, an optimization-based method is presented for detailed sizing and control of all system components including auxiliaries. The presented method is demonstrated for a solar-thermally-driven adsorption chiller system and determines both, an energetically and an economically optimal design and control. In summary, the thesis provides a comprehensive framework for optimal systems integration of adsorption chillers: from model validation over conceptual integration to detailed design and control.

Nuclear Magnetic Resonance (NMR) techniques have proliferated in many fields of science and technology like bio-sensing, chemical reaction monitoring and material characterization. Since the inception of NMR as an analytical tool, improving the sensitivity by increasing the field strength has been the primary development goal. However, in order to reduce cost and environmental impact, the trend to miniaturized NMR devices and its diverse application fields enjoys increasing interest. The first part of this thesis introduces novel insights into low-power rf-excitation, which is one crucial aspect for enabling further development in this direction, by employing Frank sequences. Based on experimental data, a detailed evidence of the power savings by excitation in the linear regime is given aiming at future elimination of the rf-amplifier from the NMR spectrometer so as to allow further mobility improvements. Selective excitation by colored Frank-sequence is reported, which bears promise for solvent signal suppression and motion tagging in magnetic resonance imaging (MRI). To this end, spectroscopic quality as well as image resolution with Frank excitation was significantly improved. The aim of the second part is to provide quantitative 3D moisture content patterns of natural soil samples on the microscale, which are essential for improving geological simulations on the field scale. In the course of this, the standardized imaging sequences ’zero echo time’ (ZTE) and ’ultra-short echo time’ (UTE) were employed but also Frank-sequence excitation was implemented, reflecting its first genuine application. In order to characterize water-retention behavior of the soil samples, ZTE experiments were combined with standardized geological methods like Multi-Step Outflow (MSO) and tensiometric measurements. Compared to the established way of acquiring water-retention data, the introduced method provides a fast and precise method with low effort.

Plant virus nanoparticle (VNP) is a useful platform with biocompatibility and versatile monodisperse protein structures that can be engineered with bioactive cues, offering opportunities to functionalize bioinert hydrogels for tissue engineering. The hypothesis in this study was that osteogenesis of human mesenchymal stem cells (hMSCs) and biomineralization could be enhanced by incorporating VNPs, which were engineered with osteogenesis-associated or cell-adhesive peptides, into cell-laden hydrogels. Cellular responses to VNPs were examined in both 2D and 3D cultures, including VNP-cell distribution, cell attachment, morphology, and osteogenesis. VNP-laden agarose or agarose-collagen hydrogels were characterized in terms of release rate, mineralization effect, mechanical properties, and usage as bioink. The results revealed enhanced osteogenic differentiation when cells were cultured on VNP-coated surfaces, and attachment of VNPs to cells as well as at least 84 % of VNP retention were observed in hydrogels. Mineralization effect was pronounced in VNP-laden hydrogels, which also demonstrated the superiority of the enriched peptides on VNPs over free peptides and VNPs with fewer peptides. Finally, VNP-laden hydrogels showed good bioprinting reproducibility. Prospectively, VNPs could be conjugated with vasculogenesis-inducing factors, thereby potentially induce more effectively 3D-printed in vitro pre-vascularization in hydrogels.

Results of life cycle assessment (LCA) studies are affected by uncertainties from various sources. These uncertainties decrease the reliability of LCA results. While uncertainty cannot be avoided, the impact of uncertainty can be quantified using uncertainty analysis. Still, despite recommendations to consider uncertainties, most LCA studies neglect uncertainty analysis.This thesis aims to facilitate uncertainty analysis in LCA through practical applications and enhanced methods. In a first step, basic uncertainty analysis includes parameter variations and scenario analysis. Basic uncertainty analysis is feasible even for low data availability and can easily be integrated in every LCA study. More sophisticated quantitative uncertainty analysis can employ analytical uncertainty analysis based on Taylor series expansion. However, the current first-order Taylor series expansion gives only a linear approximation of the uncertainty and limits the validity of the analytical approach to small uncertainties. To overcome this limitation, analytical uncertainty analysis is extended towards a second-order approximation. The developed second-order approximation provides higher accuracy at slightly increased computational cost as long as the LCA problem is small-scale.To assess uncertainty in large-scale LCA, uncertainty information of the background system can be gathered from LCA databases. In the widely used ecoinvent LCA database, the deterministic default values are represented by the median values of the log-normally distributed uncertain data. However, LCA results from studies with and without uncertainty analysis differ if the input uncertainties are represented by the median values. Using mean values for any uncertain data instead of median values is proposed to ensure consistency and comparability across LCA results with and without uncertainty analysis.The presented methods for uncertainty analysis in LCA are applied to a case study concerned with a novel hydrogen production process by methane pyrolysis. The case study shows that the developed methods for uncertainty analysis can be applied easily and increase trust into the results of the case study. Therefore, the presented methods meet the goal of this thesis and will hopefully enhance reliability of LCA results.

Soft microgels with temperature-responsive behavior are of growing interest in drug delivery, tissue engineering, and chemical engineering. Spherical and anisometric microgels are exciting materials in this variety of possible applications due to their compelling properties. Besides their responsiveness, they can provide softness, bio-compatibility, and an open pore structure. Complex-shaped microgels further present a promising building block for microgel assemblies, for example, in the field of tissue engineering. The numerous possible applications and compelling properties lead to increasing demand for microgels. Yet, the standard microgel fabrication methods do not offer high throughput rates for spherical or anisometric microgels. Therefore, this thesis aims to develop fabrication methods with high throughput for large amounts of spherical and anisometric microgels.First, a high throughput fabrication method for spherical temperature-responsive microgels is developed by transferring the typical batch precipitation polymerization into a continuous process. In this context, a continuous tubular flow reactor is established and investigated, along with a detailed comparison of the properties of the microgels from standard batch and novel continuous synthesis. Microgels with similar properties are indeed fabricated continuously. The inner structure stays unaltered by the applied fabrication methods.In a second approach, the fabrication of anisometric microgels is enhanced by adapting the common stop-flow lithography, developing a new temperature-responsive polymerization system, and investigating the process limitations. For the first time, the fabrication of soft temperature-responsive microgels of complex shapes is presented using NIPAmmonomer. It is found that a threshold amount of 10 wt% crosslinker in comparison to the monomer amount is required for the fabrication of stable particles with the stated properties. Above that threshold, the crosslinker amount allows tailoring the stiffness of the responsive microgels from very soft to comparably stiff. Of particular interest is the dynamic swelling behavior of the fabricated NIPAm microgels with complex shapes. A fast contortion during swelling before snapping back into the original shape is evident.In further investigations of the process limits, it is shown that the process has numerous influencing parameters with complex coherency. Significantly, the diffusion of components needs to be considered in a three-dimensional way to predict particle shapes and process stability precisely. Also, the fluidic parameters are essential for process stability, as automated fabrication shows a start-up behavior.Finally, the properties of anisometric microgels are broadened by tailoring the porosity of their polymeric network with methanol and adding inorganic nanoparticles for conductivity and magnetic remote control.Ultimately, this work lays the foundation towards industrial fabrication rates of spherical and anisometric microgels with various properties for multiple applications.

To avoid greenhouse gas (GHG) emissions and mitigate climate change, low-carbon technologies must be used to provide renewable energy and replace fossil fuels. However, this system transition is very material-intensive and leads to high demand for critical materials. Copper is such a material that is essential for electrical applications and many low-carbon technologies. The production of copper itself is an energyintensive process. Thus, two challenges arise that are addressed in this thesis: the flexible process operation in a fluctuating renewable energy system and the avoidance of process-based GHG emissions.The flexible operation of electricity-intensive processes can support the power grid and provide economic benefits. Demand response (DR) describes operational adjustments based on an economic incentive, such as fluctuating electricity prices. Our initial analysis shows a large DR potential of two electricity-intensive process steps in copper production. To consider the DR potential of the entire production process and to capture the dependencies of the many process steps, we formulate a detailed scheduling model of a representative copper production process. The developed mixed-integer linear program (MILP) allows minimizing the electricity costs without reducing the production volume. This process-wide scheduling enables significant DR potential, reducing annual electricity costs by up to 14.2% and shifting large parts of the electricity demand.Avoiding process-based GHG emissions is challenging because fossil fuels are hard to substitute in some processes. These processes use fossil fuels as high-temperature process heat and as chemical reducing agents. A promising alternative for these use cases is hydrogen (H2), when H2 is produced from renewable electricity using water electrolysis (Power-to-H2). The oxygen produced as a by-product offers further benefits as it can be utilized in copper production. To optimally design a power-to-H2 system, we formulate a MILP that minimizes the total annualized cost. The resulting CO2 abatement costs are 201EUR/t CO2-eq, which exceeds the current prices of EU allowances. However, a sensitivity analysis shows great potential through further development of water electrolysis.Decarbonization through Power-to-H2 offers additional DR potential. Our scheduling model of the decarbonized copper production shows that DR strongly contributes to low CO2 abatement costs. Consequently, this work identifies the potential of decarbonized copper production that provides a critical material for low-carbon technologies and supports the power grid through DR.

Due to the growing awareness of climate change, the chemical industry is increasingly considering not only economic but also ecological criteria in process development. Thus, environmental assessment methods are required that can be applied in early process development stages to support decision-making. An accepted, ISOnormed environmental assessment method is Life Cycle Assessment (LCA). However, LCA requires detailed information on mass and energy balances, which is usually not available in early process development. Furthermore, when assessing emerging technologies, future changes not only of the technology itself but also in the background system, e.g., the energy supply, have to be considered.In this thesis, the example of sector coupling of the steel and chemical industries is first used to investigate how changes in the background system can be considered in the assessment of emerging technologies and how these changes affect the assessment result. Afterwards, a framework for predictive LCA is presented that allows LCA to be integrated at early stages of process development. In contrast to existing approaches from literature using machine learning for component-specific predictions, this work combines machine learning regression models with methods of automated process design. Thereby, process-specific predictions of environmental impacts are enabled only based on the molecular structure of the desired product and the reaction equation. For this purpose, descriptors are determined purely predictively using quantum mechanics and statistical thermodynamics, as well as process shortcuts. In addition, an encoder-decoder neural network is used to increase the information density in the molecular descriptors. As regression models, an artificial neural network and a Gaussian process regression are trained on a consistent data set.The method is exemplarily integrated into computer-aided molecular and process design and used for the design of ecologically optimal solvents. In addition, the process-specific prediction is discussed for the example of CO2-based methanol. The results show that the integration of process-specific features into the LCA prediction increases the prediction accuracy and enables process-specific predictions. The presented method thus enables the integration of ecological criteria in the early development of chemical processes.