Classification of Sand-Binder Mixtures from the Foundry Industry Using Electrical Impedance Spectroscopy and Support Vector Machines.

Molding sand mixtures used in the foundry industry consist of various sands (quartz sands, chromite sands, etc.) and additives such as bentonite. The optimum control of the processes involved in using the mixtures and in their regeneration after the casting requires an efficient in-line monitoring method that is not available today. We are investigating whether such a method can be based on electrical impedance spectroscopy (EIS). To establish a database, we have characterized various sand mixtures by EIS in the frequency range from 0.5 kHz to 1 MHz under laboratory conditions. Attempts at classifying the different molding sand mixtures by support vector machines (SVM) show encouraging results. Already high assignment accuracies (above 90%) could even be improved with suitable feature selection (sequential feature selection). At the same time, the standard uncertainty of the SVM results is low, i.e., data assigned to a class by the presented SVMs have a high probability of being assigned correctly. The application of EIS with subsequent evaluation by machine learning (machine-learning-enhanced EIS, MLEIS) in the field of bulk material monitoring in the foundry industry appears possible.

Evaluation of Electrical Impedance Spectra by Long Short-Term Memory to Estimate Nitrate Concentrations in Soil.

Monitoring the nitrate concentration in soil is crucial to guide the use of nitrate-based fertilizers. This study presents the characteristics of an impedance sensor used to estimate the nitrate concentration in soil based on the sensitivity of the soil dielectric constant to ion conductivity and on electrical double layer effects at electrodes. The impedance of synthetic sandy soil samples with nitrate nitrogen concentrations ranging from 0 to 15 mg/L was measured at frequencies between 20 Hz and 5 kHz and noticeable conductance and susceptance effects were observed. Long short-term memory (LSTM), a variant of recurrent artificial neural networks (RNN), was investigated with respect to its suitability to extract nitrate concentrations from the measured impedance spectra and additional physical properties of the soils, such as mass density and water content. Both random forest and LSTM were tested as feature selection methods. Then, numerous LSTMs were trained to estimate the nitrate concentrations in the soils. To increase estimation accuracy, hyperparameters were optimized with Bayesian optimization. The resulting optimal regression model showed coefficients of determination between true and predicted nitrate concentrations as high as 0.95. Thus, it could be demonstrated that the system has the potential to monitor nitrate concentrations in soils in real time and in situ.

A Self-adaptive and Self-Sufficient Energy Harvesting System.

Self-adaptive vibration energy harvesters convert the kinetic energy from vibration sources into electrical energy and continuously adapt their resonance frequency to the vibration frequency. Only when the two frequencies match can the system harvest energy efficiently. The harvesting of vibration sources with a time-variant frequency therefore requires self-adaptive vibration harvesting systems without human intervention. This work presents a self-adaptive energy harvesting system that works completely self-sufficiently. Using magnetic forces, the axial load on a bending beam is changed and thus the resonance frequency is set. The system achieves a relative tuning range of 23% at a center frequency of 36.4 Hz. Within this range, the resonance frequency of the harvester can be set continuously and precisely. With a novel optimized method for frequency measurement and with customized electronics, the system only needs 22 µW to monitor the external vibration frequency and is therefore also suitable for environments with low vibration amplitudes. The system was verified on a vibrational test bench and can easily be tailored to a specific vibration source.

A Comparison of Methods to Measure the Coupling Coefficient of Electromagnetic Vibration Energy Harvesters.

Vibration energy harvesters transform environmental vibration energy into usable electrical energy. The transformation is only possible because of a coupling between the mechanical part of the energy harvester and the electric circuit. This paper compares several measurement methods to determine the electromagnetic coupling coefficient. These methods are applied to various implementations of an energy harvester and the results are compared with one another and with simulation data by analyzing the magnetic flux. The average deviation between the measurement methods and the simulation data in our study was 5%. This good agreement validates the methods. Based on this, we recommend determination of the coupling coefficient and the optimum load resistance for maximum power harvesting on the basis of simulations and the open circuit method, because this procedure leads to the shortest measurement times.

A Theory for Energy-Optimized Operation of Self-Adaptive Vibration Energy Harvesting Systems with Passive Frequency Adjustment.

Self-adaptive vibration energy harvesting systems vary their resonance frequency automatically to better exploit changing environmental conditions. The energy required for the adjustment is taken from the energy storage of the harvester module. The energy gained by an adjustment step has to exceed the energy expended on it to justify the adjustment. A smart self-adaptive system takes this into account and operates in a manner that maximizes the energy output. This paper presents a theory for the optimal operation of a vibration energy harvester with a passive resonance-frequency adjustment mechanism (one that only requires energy for the adjustment steps proper, but not during the hold phases between the steps). Several vibration scenarios are considered to derive a general guideline. It is shown that there exist conditions under which a narrowing of the adjustment bandwidth improves the system characteristics. The theory is applied to a self-adaptive energy harvesting system based on electromagnetic transduction with narrowband resonators. It is demonstrated that the novel optimum mode of operation increases the energy output by a factor of 3.6.

Determination of the NOx loading of an automotive lean NOx trap by directly monitoring the electrical properties of the catalyst material itself.

Recently, it has been shown that the degree of loading of several types of automotive exhaust aftertreatment devices can be directly monitored in situ and in a contactless way by a microwave-based method. The goal of this study was to clarify whether this method can also be applied to NOx storage and reduction catalysts (lean NOx traps) in order to obtain further knowledge about the reactions occurring in the catalyst and to compare the results with those obtained by wirebound NOx loading sensors. It is shown that both methods are able to detect the different catalyst loading states. However, the sensitivity of the microwave-based method turned out to be small compared to that previously observed for other exhaust aftertreatment devices. This may limit the practical applicability of the microwave-based NOx loading detection in lean NOx traps.

SAW RFID-Tags for Mass-Sensitive Detection of Humidity and Vapors.

One-port surface acoustic wave (SAW) devices with defined reflector patterns give characteristic signal patterns in the time domain making them identifiable and leading to so-called RFID-Tags. Each sensor responds with a burst of signals, their timed positions giving the identification code, while the amplitudes can be related to the analyte concentration. This paper presents the first combination of such a transducer with chemically sensitive layer materials. These include crosslinked polyvinyl alcohol for determining relative humidity and tert-butylcalix[4]arene for detecting solvent vapors coated on the free space between the reflectors. In going from the time domain to the frequency domain by Fourier transformation, changes in frequency and phase lead to sensor responses. Hence, it is possible to measure the concentration of tetrachloroethene in air down to 50 ppm, as well as 1% changes in relative humidity.

Cavities generated by self-organised monolayers as sensitive coatings for surface acoustic wave resonators.

Silanisation of quartz substrate surfaces with a mixture of two chlorosilanes, namely trimethylchlorosilane and 7-octenyldimethylchlorosilane, leads to sensitive coatings for volatile organic compounds (VOC) on surface acoustic wave (SAW) devices. In this way we created monolayers of molecular cavities engulfing the analytes according to host-guest chemistry directly on the device surfaces, and also confirmed the occurrence of such cavities by molecular modelling. We monitored the binding process of the silanes by using Fourier transform infrared (FTIR) spectrometry and atomic force microscopy (AFM). In order to increase the stiffness of the cavities, we crosslinked the terminal double bonds of the long spacers by heating the surface in the presence of a radical initiator. Compared to SAW delay lines silanised with trimethylchlorosilane, devices modified with the binary silane mixture lead to substantially higher frequency shifts when exposed to solvent vapour streams. Nearly instantaneous responses can be observed, which e.g. allows xylene detection down to a few ppm.