A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters

Effect of pH on the Economic Potential of Dark Fermentation Products from Used Disposable Nappies and Expired Food Products

Used disposable nappies constitute a waste stream that has no established treatment method. The purpose of this study was the assessment of the dark fermentation of used disposable nappies and expired food products under different pH values. The biodegradable part of the used disposable nappies was recovered and co-fermented with expired food products originating from supermarkets. The recoverable economic potential of the process was examined for different volatile fatty acids exploitation schemes and process pH values. The process pH strongly affected the products, with optimum hydrogen production at pH 6 (4.05 NLH2/Lreactor), while the amount of produced volatile fatty acids was maximized at pH 7 (13.44 g/L). Hydrogen production was observed at pH as low as pH 4.5 (2.66 NLH2/Lreactor). The recoverable economic potential was maximized at two different pH values, with the first being pH 4.5 with minimum NaOH addition requirements (181, 138, and 296 EUR/ton VS of substrate for valorization of volatile fatty acids through microbial fuel cell, biodiesel production, and anaerobic digestion, respectively) and the second being pH 6, where the hydrogen production was maximized with the simultaneous production of high amounts of volatile fatty acids (191, 142, and 339 EUR/ton VS of substrate respectively)