A Versatile Capacitive Sensing Platform for the Assessment of the Composition in Gas Mixtures

New Ways for the Advanced Quality Control of Liquefied Natural Gas

Currently, gas chromatography is the most common analytical technique for natural gas (NG) analysis as it offers very precise results, with very low limits of detection and quantification. However, it has several drawbacks, such as low turnaround times and high cost per analysis, as well as difficulties for on-line implementation. With NG applications rising, mostly thanks to its reduced gaseous emissions in comparison with other fossil fuels, the necessity for more versatile, fast, and economic analytical methods has augmented. This work summarizes the latest advances to determine the composition and physico-chemical properties of regasified liquid natural gas, focusing on infrared spectroscopy-based techniques, as well as on data processing (chemometric techniques), necessary to obtain adequate predictions of NG properties.

Design, Implementation and Simulation of a Fringing Field Capacitive Humidity Sensor

The world population is growing in an accelerated way urging the need for a more efficient and sustainable agricultural industry. Initially developed for smart cities which face the same challenges caused by an increasing population, Internet of Things (IoT) technologies have evolved rapidly over the last few years and are now moving successfully to agriculture. Wireless Sensor Networks (WSNs) have been reported to be used in the agri-food sector and could answer the call for a more optimized agricultural management. This paper investigates a PCB-made interdigited capacitive (IDC) soil humidity sensor as a low-price alternative to the existing ones on the market. An in-depth comparative study is performed on 30 design variations, part of them also manufactured for further investigations. By measurements and simulations, the influence of the aspect ratio and dielectric thickness on the sensitivity and capacitance of the sensor are studied. In the end, a Humidity and Temperature Measurement Wireless Equipment (HTMWE) for IoT agriculture applications is implemented with this type of sensor.

Diagnosis of Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas Sensors

Varroosis is a dangerous and difficult to diagnose disease decimating bee colonies. The studies conducted sought answers on whether the electronic nose could become an effective tool for the efficient detection of this disease by examining sealed brood samples. The prototype of a multi-sensor recorder of gaseous sensor signals with a matrix of six semiconductor gas sensors TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from FIGARO was tested in this area. There were 42 objects belonging to 3 classes tested: 1st class—empty chamber (13 objects), 2nd class—fragments of combs containing brood sick with varroosis (19 objects), and 3rd class—fragments of combs containing healthy sealed brood (10 objects). The examination of a single object lasted 20 min, consisting of the exposure phase (10 min) and the sensor regeneration phase (10 min). The k-th nearest neighbors algorithm (kNN)—with default settings in RSES tool—was successfully used as the basic classifier. The basis of the analysis was the sensor reading value in 270 s with baseline correction. The multi-sensor MCA-8 gas sensor signal recorder has proved to be an effective tool in distinguishing between brood suffering from varroosis and healthy brood. The five-time cross-validation 2 test (5 × CV2 test) showed a global accuracy of 0.832 and a balanced accuracy of 0.834. Positive rate of the sick brood class was 0.92. In order to check the overall effectiveness of baseline correction in the examined context, we have carried out additional series of experiments—in multiple Monte Carlo Cross Validation model—using a set of classifiers with different metrics. We have tested a few variants of the kNN method, the Naïve Bayes classifier, and the weighted voting classifier. We have verified with statistical tests the thesis that the baseline correction significantly improves the level of classification. We also confirmed that it is enough to use the TGS2603 sensor in the examined context.

Capacitive and Infrared Gas Sensors for the Assessment of the Methane Number of LNG Fuels

Liquid Natural Gas (LNG) is an energy source that is becoming more important in energy transition, as the world is facing lower the CO₂ emissions and backup sources for wind and solar energy are needed. LNG is becoming a major player not only as fuel for power plants, but also in transport and mobility. However, the composition of LNG varies significantly between the various production locations around the world, and the layering of hydrocarbons with different molecular weights takes place even in LNG containers. This is especially critical for LNG engines, in which the ignition properties of the gas depend heavily on the fuel quality or Methane Number (MN) of the gas. For optimized engine operation and motor management, this fuel quality should be measured regularly, preferably online and by a small and low-cost sensor. This paper presents two sensor solutions for the assessment of the full gas composition. For both sensors, the standard deviation in the composition of the relevant hydrocarbons was low enough to calculate the Methane Number with an accuracy of approximately 1 MN unit. It was demonstrated that the electronic capacitive sensor was better suited to assess the higher hydrocarbons, whereas the infrared sensor showed higher selectivity for the lower hydrocarbons.