The aim of this study was to optimize production of MAG by lipase-catalyzed glycerolysis in a tert-pentanol system. Twenty-nine batch reactions consisting of glycerol, sunflower oil, tert-pentanol, and commercially available lipase (Novozym ® 435) were carried out, with four process parameters being varied: Enzyme load, reaction time, substrate ratio of glycerol to oil, and solvent amount. Response surface methodology was applied to optimize the reaction system based on the experimental data achieved. MAG, DAG, and TAG contents, measured after a selected reaction time, were used as model responses. Well-fitting quadratic models were obtained for MAG, DAG, and TAG contents as a function of the process parameters with determination coefficients (R 2 ) of 0.89, 0.88, and 0.92, respectively. Of the main effects examined, only enzyme load and reaction time significantly influenced MAG, DAG, and TAG contents. Both enzyme amount and reaction time showed a surprisingly nonlinear relationship between factors (process parameters) and responses, indicating a local maximum. The substrate ratio of glycerol to oil did not significantly affect the MAG and TAG contents; however, it had a significant influence on DAG content. Contour plots were used to evaluate the optimal conditions for the complex interactions between the reaction parameters and responses. The optimal conditions established for MAG yield were: enzyme load, 18% (w/w of oil); glycerol/oil ratio, 7:1 (mol/mol); solvent amount, 500% (vol/wt of oil); and reaction time, 115 min. Under these conditions, a MAG content of 76% (w/w of lipid phase) was predicted. Verification experiments under optimized reaction conditions were conducted, and the results agreed well with the range of predictions.

This work presents a cantilever based broadband piezoelectric magnetoelastic vibration energy harvester with increased mechanical robustness. The energy harvester is fabricated using KOH etching to define the cantilever and the proof mass is made using micromachined Fe foils which together with a pair of miniature magnets provides the magnetoelastic properties. KOH etching leads to very sharp corners at the anchoring point of the cantilever which makes the cantilever fragile. The mechanical robustness of the energy harvesters is increased using a lithography-free two-step fabrication process where a thermal oxidation is used for corner rounding. The corner rounding at the anchoring point lowers the stress concentration and thereby increases the robustness of the device. The radius of curvature for the corner depends linearly on the thickness of the oxide. Both enhanced and non-enhanced beams are excited at increasing frame accelerations. The conventional beams break at frame accelerations of around 3 g while the enhanced break at almost twice as much, 5.7 g. The devices are characterized electrically by impedance measurements in both their linear and non linear regime. The magnetoelastic behaviour can be adjusted by varying the beam-magnet distance which allows for both spring softening and spring hardening.

No abstract

Summary. The paper presents a measurement setup capable of collecting wheel/rail contact noise and vibration signals from a passenger train. A data analysis method based on machine learning is developed for detecting events from the acquired data and classifying them according to relevant railway track components and noise phenomena. A classification rate higher than 84 % is achieved. IntroductionThe interaction between wheel and rail is one of the main causes of noise generation when a train is passing by. A railway track typically consists of various components such as insulated joints, level crossings etc. Proper maintenance of the track, including its various components is important from a safety and reliability point of view. This is important as noise emission can be reduced significantly by an optimized maintenance strategy. Typically, defect detection related inspection and maintenance work is carried out in a routine manner (and as-per-need basis) periodically using a measurement train. This approach is costly and damages can occur in between measurement campaigns. Banedanmark's noise mitigation approach focuses on reducing the noise at the source. To investigate how the noise depends on the track components and their condition, a system capable of collecting and analyzing noise and vibration data measured from a train in running operation is desirable. Such system can also be used to detect critical wear and defects of the track and thus support the planning of maintenance work and development of new tracks with lower noise emission.Unlike the periodic inspection that is currenly state of the art; in recent times researchers have suggested monitoring of railway tracks using sensors installed on in-service trains [1,2]. Work presented in this paper presents a new strategy for continuously monitoring the railway track using acoustic and vibration sensors (along with a GPS) installed on an operational train. The main idea behind this project is that each track component (insulated joints, level crossings etc.) generates a specific sound or vibration signature characteristic of its own, which might change in case of deterioration or unusual conditions. Similarly, a damaged section of the track, e.g. due to corrugation, will also have a characteristic signature different from the track under normal conditions. Thus by monitoring the track continuously, a change in the signature over time can be

No abstract