In real-world applications-to minimize the impact of failures-machinery is often monitored by various sensors. Their role comes down to acquiring data and sending it to a more powerful entity, such as an embedded computer or cloud server. There have been attempts to reduce the computational effort related to data processing in order to use edge computing for predictive maintenance. The aim of this paper is to push the boundaries even further by proposing a novel architecture, in which processing is moved to the sensors themselves thanks to decrease of computational complexity given by the usage of compressed recurrent neural networks. A sensor processes data locally, and then wirelessly sends only a single packet with the probability that the machine is working incorrectly. We show that local processing of the data on ultra-low power wireless sensors gives comparable outcomes in terms of accuracy but much better results in terms of energy consumption that transferring of the raw data. The proposed ultra-low power hardware and firmware architecture makes it possible to use sensors powered by harvested energy while maintaining high confidentiality levels of the failure prediction previously offered by more powerful mains-powered computational platforms.
General hardware architecture of an energy-harvested wireless sensor network node (EH-WSN) can be divided into power, sensing, computing and communication subsystems. Interrelation between these subsystems in combination with constrained energy supply makes design and implementation of EH-WSN a complex and challenging task. Separation of these subsystems into distinct hardware modules simplifies the design process and makes the architecture and software more generic, leading to more flexible solutions. From the other hand, tightly coupling these subsystems gives more room for optimizations at the price of increased complexity of the hardware and software. Additional engineering effort could be justified by a smaller, cheaper hardware, and more energy-efficient a wireless sensor node. The aim of this paper is to push further technical and economical boundaries related to EH-WSN by proposing a novel architecture which-by tightly coupling software and hardware of power, computing, and communication subsystems-allows the wireless sensor node to be powered by a thermoelectric generator working with about 1.5 • C temperature difference while keeping the cost of all electronic components used to build such a node below 9 EUR (in volume).
Abstract:The aim of this research was to assess the content and composition of the pollutants emitted by domestic central heating boilers equipped with an automatic underfeed fuel delivery system for the combustion chamber. The comparative research was conducted. It concerned fuel properties, fl ue gas parameters, contents of dust (fl y ash) and gaseous substances polluting the air in the fl ue gases emitted from a domestic CH boiler burning bituminous coal, pellets from coniferous wood, cereal straw, miscanthus, and sunfl ower husks, coniferous tree bark, and oats and barley grain. The emission factors for dust and gaseous air pollutants were established as they are helpful to assess the contribution of such boilers in the atmospheric air pollution. When assessing the researched boiler, it was found out that despite the development in design and construction, fl ue gases contained fl y ash with a signifi cant EC content, which affected the air quality.
STRESZCZENIEWspółspalanie węgla i stałych odpadów komunalnych jest zjawiskiem społecznym, stanowiącym poważne źró-dło emisji substancji szkodliwych do powietrza. Przeprowadzono badania porównawcze współspalania węgla kamiennego z poszczególnymi frakcjami stałych odpadów komunalnych (m.in. makulatura, PE, PCV) w kotle CO o mocy 18 kW wyposażonym w automatyczny podajnik paliwa. Badania miały na celu porównanie parametrów spalin, zawartości pyłu (popiołu lotnego) i gazowych substancji zanieczyszczających powietrze w emitowanych spalinach z danego kotła CO. Podczas spalania pobierane były próbki emitowanego pyłu oraz analizowany był w sposób ciągły skład jakościowy i ilościowy spalin -analiza chemiczna spalin obejmowała: CO 2 , CO, H 2 O, SO 2 , NO x . Oznaczono ponadto stężenie pyłu w spalinach. Pobrany popiół lotny został poddany oznaczeniom zawartości węgla C -organicznego, elementarnego i całkowitego, PM10 i PM2,5 oraz zawartości 16 WWA. Wyniki zostały przeanalizowane pod względem efektywności spalania, emisji głównych zanieczyszczeń (NO x , CO, SO 2 ) i popiołu lotnego oraz zaadsorbowanych na jego powierzchni WWA. Średnie stężenie emitowanego pyłu wynosiło 764 mg m -3 , natomiast CO -1944, SO 2 -1256, NO x -555 mg m -3 (STP, 3% O 2 , gaz suchy). Spaliny zawierały popiół lotny, ze znaczną zawartością węgla EC (średnio 31%) oraz wysokim udziałem PM10 oraz PM2,5 -odpowiednio 100 i 75% obj.Słowa kluczowe: popiół lotny, odpady stałe, wskaźniki emisji EMISSIONS FROM CO-COMBUSTION OF COAL AND MUNICIPAL SOLID WASTE IN DOMESTIC CENTRAL HEATING BOILERABSTRACT Co-combustion of coal and solid municipal waste is a social phenomenon. It constitutes an important emission source of harmful air pollutants. The comparative research was conducted. It concerned co-combustion of coal and different types of municipal solid waste (including wastepaper, PE, PVC) in the domestic CH (central heating) boiler (18-kW power) equipped with an automatic fuel feeder. The aim of this research was to compare the parameters of flue gas, content of dust (fly ash) and gaseous air pollutants in the flue gases from the CH boiler. During the combustion were taken the fly ash samples and was continuously analyzed qualitative and quantitative composition of flue gas. Chemical analysis of flue gases included: CO 2 , CO, H 2 O, SO 2 and NO x . Concentration of fly ash in the flue gas was determined. The fly ash samples were analysed for the organic, elemental and total carbon, PM10 and PM2,5, and 16 PAHs content. The results were analyzed in terms of combustion efficiency, emissions of major pollutants (NO x , CO, SO 2 ) and fly ash with adsorbed of PAHs on its surface. The average concentration of emitted particulate matter was 764 mg m -3 , and CO -1944, SO 2 -1256 NO x -555 mg m -3 (STP, 3% O 2 , dry gas). The flue gases contain fly ash, with a significant carbon content EC (average 31%) and a high proportion of PM10 and PM2.5 -respectively 100 and 75% by volume.
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