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In comparison to crude oil, biorefinery raw materials are challenging in concerns of transport and storage. The plant raw materials are more voluminous, so that shredding and compacting usually are necessary before transport. These mechanical processes can have a negative influence on the subsequent biotechnological processing and shelf life of the raw materials. Various approaches and their effects on renewable raw materials are shown. In addition, aspects of decentralized pretreatment steps are discussed. Another important aspect of pretreatment is the varying composition of the raw materials depending on the growth conditions. This problem can be solved with advanced on-site spectrometric analysis of the material.
The development of a cost-effective hydrolysis for crude cellulose is an essential part of biorefinery developments. To establish such high solid hydrolysis, a new solid state reactor with static mixing is used. However, concentrations >10% (w/w) cause a rate and yield reduction of enzymatic hydrolysis. By optimizing the synergetic activity of cellulolytic enzymes at solid concentrations of 9%, 17% and 23% (w/w) of crude Organosolv cellulose, glucose concentrations of 57, 113 and 152 g L⁻¹ are reached. However, the glucose yield decreases from 0.81 to 0.72gg⁻¹ at 17% (w/w). Optimal conditions for hydrolysis scale-up under minimal enzyme addition are identified. As result, at 23% (w/w) crude cellulose the glucose yield increases from 0.29 to 0.49gg⁻¹. As proof of its applicability, biobutanol, succinic and itaconic acid are produced with the crude hydrolysate. The potential of the substrate is proven e.g. by a high butanol yield of 0.33gg⁻¹.
Due to the interfering effects of acetic acid in many fermentation processes, a gas-diffusion technique was developed for the online determination of acetic acid. The measurements were accomplished with a flow diffusion analysis (FDA) unit from the TRACE Analytics GmbH, Braunschweig, Germany. The diffusion analysis is based on the UV-absorbance of acetic acid at 205 nm. The measurement was achieved by the separation of an acceptor and a carrier stream (acidified fermentation broth) using a gas permeable polytetrafluoroethylene (PTFE) membrane, whereby broth constituents that would otherwise disturb the UV-measurement of acetic acid, are held back efficiently. Merely, the fermentation by-products, e.g. formic acid, is capable of diffusing through the membrane. While formic acid can disturb the measurement, carbon dioxide does not absorb at 205 nm. The method operates with time-dependent sample enrichment. During the analysis, a small volume of the acceptor stream is stopped for a defined time interval in the acceptor chamber. During this period, the gaseous acetic acid diffuses through the membrane and is enriched in the acceptor chamber. Subsequently after the enrichment, the acceptor stream flows through a UV-detector. The intensity of the signal is proportional to the acetic acid concentration. Online measurements in bioreactors via a sterile filtration probe have been accomplished. A linear calibration in the range of 0.5–5.0 g/L acetic acid with a relative standard deviation of <5 % was obtained. A sampling rate of 8 samples per hour was possible. The system was applied for the determination of acetic acid in E. coli fermentation broth. The instrument is easy to clean, very user-friendly and does not require any toxic or expensive reagents.
Biotechnological downstream processing is usually an elaborate procedure, requiring a multitude of unit operations to isolate the target component. Besides the disadvantageous space-time yield, the risks of cross-contaminations and product loss grow fast with the complexity of the isolation procedure. A significant reduction of unit operations can be achieved by application of magnetic particles, especially if these are functionalized with affinity ligands. As magnetic susceptible materials are highly uncommon in biotechnological processes, target binding and selective separation of such particles from fermentation or reactions broths can be done in a single step. Since the magnetizable particles can be produced from iron salts and low priced polymers, a single-use implementation of these systems is highly conceivable. In this article, the principles of magnetizable particles, their synthesis and functionalization are explained. Furthermore, applications in the area of reaction engineering, microfluidics and downstream processing are discussed focusing on established single-use technologies and development potential.
Even the shortest flight through unknown, cluttered environments requires reliable local path planning algorithms to avoid unforeseen obstacles. The algorithm must evaluate alternative flight paths and identify the best path if an obstacle blocks its way. Commonly, weighted sums are used here. This work shows that weighted Chebyshev distances and factorial achievement scalarising functions are suitable alternatives to weighted sums if combined with the 3DVFH* local path planning algorithm. Both methods considerably reduce the failure probability of simulated flights in various environments. The standard 3DVFH* uses a weighted sum and has a failure probability of 50% in the test environments. A factorial achievement scalarising function, which minimises the worst combination of two out of four objective functions, reaches a failure probability of 26%; A weighted Chebyshev distance, which optimises the worst objective, has a failure probability of 30%. These results show promise for further enhancements and to support broader applicability.
This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance.
Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one of the undeformed reference state of a specimen and another of the deformed target state, the relative displacement between those two states is determined. DIC is well known and often used for post-processing analysis of in-plane displacements and deformation of specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and extend the field of use of this technique.
Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether real-time analysis is possible with these methods. To reflect improvements in computing technology different hardware settings were also analysed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm such that it becomes practically slower than a suboptimal algorithm. The Newton-Raphson algorithm in combination with a modified Particle Swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss-Newton algorithm is superior. As expected, the Brute Force Search algorithm is the least effective method. We also found that the correct choice of parallelization tasks is crucial to achieve improvements in computing speed. A poorly chosen parallelisation approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode the correct choice of combinations of integerpixel and sub-pixel search algorithms is decisive for an efficient analysis. Using currently available hardware realtime analysis at high framerates remains an aspiration.