Large-scale conversion of lignin to liquid through formic acid assisted solvolysis in aqueous and ethanolic reaction media: comparison of yields and product compositions

Postponed access: the file will be accessible after 2020-07-17Biomass has recently received much attention as an attractive renewable energy resource and a promising alternative to fossil carbon resources for production of renewable biofuels and other value-added chemicals due to being the only viable feedstock for carbon based fuels and chemicals. Within the biorefinery concept, sustainable use of biomass involves optimal exploitation of all fractions of the raw material to make products with high value. Production of 2nd generation bioethanol from the carbohydrate fraction of non-edible lignocellulosic biomass is already established and the technical feasibility of this process is well demonstrated. However, this process leaves significant amounts of lignin, a cross-linked amorphous copolymer of phenylpropane units with unique properties as by-product. Lignin is the third most abundant biopolymer as well as the most important source of bio-based aromatics in nature, which accounts for 10–30 wt. % of the feedstock. Thus, the viability of lignocellulosic biorefinery is highly dependent on the development of efficient lignin valorisation routes. Production of value-added chemicals from lignin requires the simultaneous depolymerization of the lignin structures with subsequent hydrodeoxygenation of the lignin monomers and alkylation of aromatic rings to prevent repolymerization and char production. Thermochemical conversion of lignin through Lignin-to-Liquid (LtL) process is an innovative conversion method, which can be considered as a solvolytic process in a liquid or near-critical reaction medium at high temperature and high pressure, using an in situ hydrogen donor solvent instead of molecular hydrogen. The Lignin-to-Liquid process and the chemical composition and bulk properties of LtL-oils produced in small laboratory scale is well developed and there is ongoing research on this approach. However, in case of development towards industrial scale production, the effect of increasing the scale must be investigated and the conversion must be optimized at larger scale. Optimizing process conditions yielding high amount of the desired products is challenging and time-consuming, especially due to the interactions between different experimental conditions. Thus, some important reaction parameters such as shorter reaction time, lower reaction temperature, and reduction of low-value side stream products, i.e. gas and solid residues, need to be improved in order to make LtL-oils competitive with petroleum-based fuels and chemicals. The main focus in this thesis was therefore to evaluate the impact of upscaling on LtLprocess efficiency in terms of bio-oil yield and bio-oil composition. Lignin conversion conducted at small laboratory scale (0.025 L) was scaled up by a factor of 200 and reperformed using a 5 L stirred reactor to explore the effect of increased volume and stirred reaction on the product yield and product quality. Various reaction parameters were investigated and the relationship between the product yields and reaction conditions were systematically evaluated using principal component analysis (PCA). Additionally, the catalytic conversion of lignin through LtL-solvolysis was explored using two different types of catalysts, an alumina supported noble metal catalyst and an iron-based mineral catalyst. The overall results showed similar trends relative to reaction parameters at both reaction scales, but oil yields in some cases tended to decrease from small laboratory scale to 5 L scale when using water as reaction medium. The purest lignin feedstocks resulted in highest oil yields at both scales. Comparison of the investigated solvent systems (water vs. ethanol system) showed that the highest oil yields from eucalyptus lignin-rich residue were achieved from the ethanol system at reaction temperatures below 350 °C, indicating a higher tendency for repolymerzation of lignin components to give char formation at elevated temperatures. In addition, a major increase in oil yield and a significantly decrease in char yield was observed as a function of increased stirring rate and increased level of loading in the reactor. Goethite as catalyst did not shown good conversion efficiency, while Ru/Al2O3 was found to be very efficient with oil yields above 69 wt. % on lignin intake. Overall, the highest bio-oil yield and a significant low char yield was obtained from experiment Ru/Al2O3.S1000.Max.305, indicating that combination of high stirring rate with maximum loading in the reactor in the presence of Ru/Al2O3 as catalyst at low temperature is the most optimal condition investigated in this thesis. The bio-oil comprises a complex mixture of monomeric phenols, aromatics and more hydrogenated products, with a high H/C and a low O/C ratio. However, bio-oils from the ethanol system had higher H/C values due to the incorporation of the ethyl groups, which increased the number of alkyl units in the product. Based on results from GCMS analysis, there was no clear differences in the composition of LtL-oils from the same solvent system, while ethanol-based experiments generated bio-oils with a more complicated pattern of substitution than water-based experiments. However, concentration of the most abundant compounds identified in each solvent system showed to be mainly dependent on reaction temperature. Furthermore, the large product volume made it possible to test fractionation ability of the produced bio-oils by means of solid phase extraction (SPE) where 65–92 wt. % of the bio-oils were separated and recovered as polarity-based fractions. The most volatile fractions were then identified using GC-MS analysis, which showed good perspectives for further development. Moreover, the lignin-enriched eucalyptus residue was investigated as feedstock in a comparative study between a direct one-step hydrodeoxygenation (HDO) and a 2-step hydrothermal liquefaction-hydrodeoxygenation (HTL-HDO) approach using Ru/C and Pd/C as catalysts in terms of product yields, quality and composition of the produced bio-oils. A general observation was that bio-oil yields decreased as a function of increased temperature, while the volatility of the bio-oils as well as total monomer yields increased with temperature. 2-step HTL-HDO significantly improved the total monomer yields while preventing char formation. The identified compounds comprising the lignin-oils were classified as alkylphenolics, aromatics, naphthalenes, linear and cyclic alkanes, guaiacols, catechols and ketones. Alkylphenolics and aromatics were the main chemical groups identified. However, a significant increase in alkane formation was observed with increased temperature, which can be due to enhanced depolymerization and hydrogenation at more severe temperature conditions. In terms of interesting monomers, the preferred pathways were the 2-step HTL-HDO of LES using Ru/C at 410 °C, and the direct HDO of LES using Pd/C at 450 °C. Overall, the results obtained in this project showed that increase in reaction volume was a promising option in terms of product yields and product composition, giving oil yields up to 79 wt. % on lignin intake. However, further studies to map out the optimal experimental conditions towards desired bio-oil yield and bio-oil quality as well as the development of appropriate fractionation methods to separate bio-oil components into fractions with similar chemical properties are the next steps needed to strength the biooil potential as a source for platform chemicals

The Effects of GSPT1 Degradation on Serum Calcium, Parathyroid Hormone, and Fibroblast Growth Factor 23 Concentrations in Human Cereblon Knock-in Mice

Test article CC-325 is a potent oral cereblon (CRBN) modulator that has shown potent G1 to S phase transition 1 (GSPT1) degradation and anti-tumor activity in pre-clinical models. One of the adverse effects associated with CC-325 was dose dependent hypocalcemia, which was determined to be an on-target toxicity. To investigate the mechanism of hypocalcemia, we conducted a toxicity study in human cereblon (huCRBN) knock-in (KI) mice with CC-325. The huCRBN KI mice are transgenic mice engineered to express human cereblon that is capable of binding to CRBN and degrading GSPT1. Four groups of mice were treated with vehicle (0 mg/kg), CC-325 (50 mg/kg BID), NPS 2143 (120 mg/kg), or CC-325 + NPS 2143. The NPS 2143 is an oral negative allosteric modulator of calcium sensing receptor (CaSR), which upon administration to mice significantly increased plasma ionized calcium (iCa2+) and parathyroid hormone (PTH). Mice treated with CC-325 alone had significant decreases in serum iCa2+ and PTH, while mice treated with NPS 2143 alone as expected had significant increases in serum iCa2+ and PTH. Treatment of mice with CC-325 + NPS 2143 did not reverse the decreases in serum iCa2+ and PTH caused by CC-325, indicating that CC-325 prevents the increase of PTH. To investigate the mechanism of hypocalcemia, we stained parathyroid gland for PTH by immunohistochemistry (IHC) and showed significantly lower PTH in parathyroid with CC-325 treated mice compared to vehicle or NPS 2143 treated mice. To further investigate the cause of low PTH in parathyroid gland in mice treated with CC-325, we stained parathyroid with in-situ hybridization (ISH) probes for PTH mRNA. Results from this analysis showed significantly lower PTH mRNA in parathyroid of CC-325 mice compare to vehicle or NPS 2143 mice, indicating that lower serum PTH in CC-325 treated mice were due to decreased PTH mRNA in Chief cells. These data collectively indicate that hypocalcemia caused by CC-325 is due to reduction in PTH, which leads to hypocalcemia. Additionally, mice treated with CC-325 are unable to restore normocalcemia because their parathyroid gland did not synthesize sufficient PTH for release into blood stream. Lack of PTH synthesis is caused by diminished level of PTH mRNA in parathyroid gland. We also measured the level of FGF23 in mice treated with CC-325. Our data indicated that decrease in PTH significantly decreased FGF23 levels even in presence of hyperphosphatemia, indicating that PTH plays a big role in controlling FGF23 during hypoparathyroidism. The cause of decrease in PTH mRNA in parathyroid, whether it is related to lower transcription of PTH mRNA or lack of stability of PTH mRNA, remains to be determined

Weak phase retrieval and phaseless reconstruction

Phase retrieval and phaseless reconstruction for Hilbert space frames is a very active area of research. Recently, it was shown that these concepts are equivalent. In this thesis, we make a detailed study of a weakening of these concepts to weak phase retrieval and weak phaseless reconstruction. We will give several necessary and/or su cient conditions for frames to have these weak properties. We will prove three surprising results: (1) Weak phaseless reconstruction is equivalent to phaseless reconstruction. I.e. It was never weak; (2) Weak phase retrieval is not equivalent to weak phaseless reconstruction; (3) Weak phase retrieval requires at least 2m - 1 vectors in an m-dimensional Hilbert space. We also gives several examples illustrating the relationship between these concepts.by Dorsa GhoreishiIncludes bibliographical reference

Nicotinamide Riboside and the Aortic Response to Angiotensin II Infusion in Mice

Damage to vascular cells of the aorta drives vascular dysfunction and disease. Nicotinamide adenine dinucleotide (NAD+) is a cellular metabolite critical to cellular health, but NAD+ levels decline during oxidative insults and aging. The NAD+ precursor, nicotinamide riboside (NR) can augment NAD+ levels. Herein, I determined whether orally administrated NR could protect the aorta of middle-aged mice from acute and sustained angiotensin II (Ang II) infusion-induced damage. I demonstrate that orally administered NR can protect the aorta from damage imposed by Ang II

Phase retrieval in frame theory

This dissertation is the study of phase retrieval in frame theory. The first part is concerned with the analysis of phase retrieval and the complete classification of norm retrieval. Norm retrieval is essential to transfer the properties of phase retrieval to the complement space. The first section includes the results regarding projections and also the characterization of phase retrieval and norm retrieval for subspaces. The second part is the study of weak phase retrieval which was motivated by the idea of reducing the number of vectors satisfying the properties close to phase retrieval. The last section provides the correlation between weak phase retrieval and phase retrieval properties along with the examples illustrating the relationship between weak phase retrieval and the related concepts.Includes bibliographical reference

Bayesian Optimization in Multi-Information Source and Large-Scale Systems

The advancements in science and technology in recent years have extended the scale of engineering problems. Discovery of new materials with desirable properties, drug discovery for treat-ment of disease, design of complex aerospace systems containing interactive subsystems, conducting experimental design of complex manufacturing processes, designing complex transportation systems all are examples of complex systems. The significant uncertainty and lack of knowledge about the underlying model due to the complexity necessitate the use of data for analyzing these systems. However, a huge time/economical expense involved in data gathering process avoids ac-quiring large amount of data for analyzing these systems. This dissertation is mainly focused on enabling design and decision making in complex uncertain systems. Design problems are pervasive in scientific and industrial endeavors: scientists design experiments to gain insights into physical and social phenomena, engineers design machines to execute tasks more efficiently, pharmaceutical researchers design new drugs to fight disease, and environ-mentalists design sensor networks to monitor ecological systems. All these design problems are fraught with choices, choices that are often complex and high-dimensional, with interactions that make them difficult for individuals to reason about. Bayesian optimization techniques have been successfully employed for experimental design of these complex systems. In many applications across computational science and engineering, engineers, scientists and decision-makers might have access to a system of interest through several models. These models, often referred to as “information sources", may encompass different resolutions, physics, and modeling assumptions, resulting in different “fidelity" or “skill" with respect to the quantities of interest. Examples of that include different finite-element models in design of complex mechanical structures, and various tools for analyzing DNA and protein sequence data in bioinformatics. Huge computation of the expensive models avoids excessive evaluations across design space. On the other hand, less expensive models fail to represent the objective function accurately. Thus, it is highly desirable to determine which experiment from which model should be conducted at each time point. We have developed a multi-information source Bayesian optimization framework capable of simultaneous selection of design input and information source, handling constraints, and making the balance between information gain and computational cost. The application of the proposed framework has been demonstrated on two different critical problems in engineering: 1) optimization of dual-phase steel to maximize its strength-normalized strain hardening rate in materials science; 2) optimization of NACA 0012 airfoil in aerospace. The design problems are often defined over a large input space, demanding large number of experiments for yielding a proper performance. This is not practical in many real-world problems, due to the budget limitation and data expenses. However, the objective function (i.e., experiment’s outcome) in many cases might not change with the same rate in various directions. We have introduced an adaptive dimensionality reduction Bayesian optimization framework that exponentially reduces the exploration region of the existing techniques. The proposed framework is capable of identifying a small subset of linear combinations of the design inputs that matter the most relative to the objective function and taking advantage of the objective function representation in this lower dimension, but with richer information. A significant increase in the rate of optimization process has been demonstrated on an important problem in aerospace regarding aerostructural design of an aircraft wing modeled based on the NASA Common Research Model (CRM)

Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual Phase Materials

Integrated Computational Materials Engineering (ICME) calls for the integration of computational tools into the materials and parts development cycle, while the Materials Genome Initiative (MGI) calls for the acceleration of the materials development cycle through the combination of experiments, simulation, and data. As they stand, both ICME and MGI do not prescribe how to achieve the necessary tool integration or how to efficiently exploit the computational tools, in combination with experiments, to accelerate the development of new materials and materials systems. This paper addresses the first issue by putting forward a framework for the fusion of information that exploits correlations among sources/models and between the sources and `ground truth'. The second issue is addressed through a multi-information source optimization framework that identifies, given current knowledge, the next best information source to query and where in the input space to query it via a novel value-gradient policy. The querying decision takes into account the ability to learn correlations between information sources, the resource cost of querying an information source, and what a query is expected to provide in terms of improvement over the current state. The framework is demonstrated on the optimization of a dual-phase steel to maximize its strength-normalized strain hardening rate. The ground truth is represented by a microstructure-based finite element model while three low fidelity information sources---i.e. reduced order models---based on different homogenization assumptions---isostrain, isostress and isowork---are used to efficiently and optimally query the materials design space.Comment: 19 pages, 11 figures, 5 table

Dynamic simulation of carbochlorination of zirconia in a pilot plant fluidized-bed reactor

Zirconium tetrachloride was produced via the chlorination of zirconia in a fluidized bed reactor in the presence of carbon. The fluidized bed zirconia carbochlorination reactor was modeled dynamically via a three-phase hydrodynamic flow assumption. The operating temperature (800-1200 °C), reaction time (30-120 min) and mean particle size (MPS) (70-130 μm) were chosen as the range of operating conditions. The comparison of reaction conversion with experimental measurements was used for model validation. An acceptable compatibility was observed between simulation results and experimental data. Investigation of fluidized bed operating conditions with the validated model was accomplished and the results indicated that smaller zirconia size, higher reactor temperature and reaction time improved chlorination rate. The optimum operating conditions were found to be 1200 ºC, 120 min and 70 μm, in which the maximum zirconia conversion of 91.8% was obtained.Keywords: Zirconia, Fluidized bed reactor, Carbochlorination, zirconium tetrachlorid