Abstract:Methanol and ethanol are among the most important biofuels and raw materials used to produce biorenewable fuels. These fuels are used with varying water contents. Nevertheless, the exact impact of the water content of these fuels on the energy potential and combustion characteristics is still unknown. Besides that, there are two noticeable risks (environmental impact of combustion and fire risk) associated with their production, processing, and utilization. Likewise, impact of the water content of these fuels … Show more
“…8 , 9 demonstrates that the combustion processes of ethanol and propanol are very similar, while the combustion process of pentane differs significantly from both ethanol and propanol. Further fire characteristics of the samples are described in detail in [ 22 – 24 ]. Fig.…”
This paper deals with the recognition of selected burning liquids by convolutional neural networks (CNNs). Three CNNs (AlexNet, GoogLeNet and ResNet-50) were trained, validated and tested (in the MATLAB 2020b software) for the recognition of selected liquids (ethanol, propanol and pentane) using photographs of the flames they produce. For training, validation and test photographs of the liquids under investigation burning in a 106-mm-diameter vessel were used. The accuracy of all the CNNs under investigation during the tests was above 99%. In addition the trained CNNs were tested using photographs of the flames generated by the liquids under investigation burning in a vessel with a diameter of 75 mm. The accuracy of the trained CNNs in this additional test ranged from 37 to 42% (GoogLeNet) through 62–73% (ResNet-50) up to 51–80% (AlexNet) – the results varied dependent upon the relative size of the flame in the photograph under analysis (in most cases an increase in the relative size caused an increase in accuracy). The accuracy of the AlexNet can be improved from 80% to almost 96% using an algorithm. The principle of the algorithm is the analysis of 10 photographs of the same liquid in the same vessel (taken over a few seconds) followed by the recognition based on an identical classification for at least 6 out of 10 photographs. An accuracy of 96% is sufficient for the rapid recognition of burning liquids in practical applications.
Supplementary Information
The online version contains supplementary material available at 10.1007/s10973-021-10903-2.
“…8 , 9 demonstrates that the combustion processes of ethanol and propanol are very similar, while the combustion process of pentane differs significantly from both ethanol and propanol. Further fire characteristics of the samples are described in detail in [ 22 – 24 ]. Fig.…”
This paper deals with the recognition of selected burning liquids by convolutional neural networks (CNNs). Three CNNs (AlexNet, GoogLeNet and ResNet-50) were trained, validated and tested (in the MATLAB 2020b software) for the recognition of selected liquids (ethanol, propanol and pentane) using photographs of the flames they produce. For training, validation and test photographs of the liquids under investigation burning in a 106-mm-diameter vessel were used. The accuracy of all the CNNs under investigation during the tests was above 99%. In addition the trained CNNs were tested using photographs of the flames generated by the liquids under investigation burning in a vessel with a diameter of 75 mm. The accuracy of the trained CNNs in this additional test ranged from 37 to 42% (GoogLeNet) through 62–73% (ResNet-50) up to 51–80% (AlexNet) – the results varied dependent upon the relative size of the flame in the photograph under analysis (in most cases an increase in the relative size caused an increase in accuracy). The accuracy of the AlexNet can be improved from 80% to almost 96% using an algorithm. The principle of the algorithm is the analysis of 10 photographs of the same liquid in the same vessel (taken over a few seconds) followed by the recognition based on an identical classification for at least 6 out of 10 photographs. An accuracy of 96% is sufficient for the rapid recognition of burning liquids in practical applications.
Supplementary Information
The online version contains supplementary material available at 10.1007/s10973-021-10903-2.
“…Other important parameters are CO and CO 2 yields, which describe the mass of particular gas per mass loss of fuel. They may be expressed in [g/kg] or [kg/kg] [11].…”
Smoke generated in the combustion process of plastics reduces visibility and contains toxic products causing severe health problems for and may be hazardous to both people and the environment. Therefore, the awareness of smoke specification is important. This paper presents two most common small-scale methods for measuring smoke parameters. The cone calorimeter method and single-chamber test, respectively, standardized by ISO 5660 and PN-EN ISO 5659-2 were reviewed. Fundamentals of these methods and measurement techniques were described. Di-verse smoke parameters obtained from these methods were collected and specified. Attention was also drawn to the relevance of these parameters to standard requirements for various industry fields such as rail transport or shipbuilding.
“…Input materials are usually connected with local production which significantly reduces dependence on imported products. The quality of the standardly sold fuels differs especially in the water content within the fuel, as was presented by Nozza et al [6], Martinka et al [13] and Ryšavý et al [5]. According to a study of Breaux [14], water/ethanol mixture is cheaper and much easier to manufacture since the distillation energy used for ethanol dehydration (for reaching more than 99% of the mass fraction of ethanol in the fuel) could cost approximately 1/3 of the overall energy consumed.…”
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