Quantitative measurement of the net calorific value (NCV) of the bulk material
Still another example from industry shows the problems with reuse of secondary fuels.
Out of 8.3 billion tonnes from annual plastic production in 2015, only 600 million tonnes were recycled, 800 million tonnes being burnt (Ref. ). Therefore, reuse of plastic waste as secondary fuels is a contribution to a sustainable recycling policy in terms of environmental and economic policies. Thanks to the analysis of the materials to be burnt, it will be possible to determine in advance how much material needs to be fed to the incineration process in order to achieve a stable process.
This will enable good housekeeping with recoverable fuels and thus help to save the environment while increasing the economic efficiency of garbage incineration plants.
In concrete terms, this means that the use of a quantitative hyperspectral analysis directly in-line can help to improve economic efficiency of the line.
For this purpose, EVK has developed the analysis tool SQALAR and thus a solution that can be used for quantitative real-time monitoring of material streams based on data analysis. Thanks to so-called “Quantitative Chemical Imaging” (QCI), relevant measuring variables will be acquired right in the line so that it will, if necessary, be possible to intervene in the ongoing process and change it in case of need. The reflection spectrum of the material used in this process will change proportionately to the occurrence of functional groups within the material.
In this respect, the big advantage is that it will be possible to identify a representative measuring value of such important process variables as humidity and the net and gross calorific values throughout the material stream in real time.
Such an analysis of the overall stream will make it possible to make process control even more accurate and to assess different suppliers as well as the material qualities delivered by them.
In order to enable such a quantitative analysis using HSI (hyperspectral imaging), it will, first of all, be necessary to record several hyperspectral images of the product stream. Then specimens will be taken out of this material in a well-aimed manner and studied by means of methods based on reference analytics. Then the resulting reference values will be correlated to the fitting spectrums by means of multivariate data analysis.
This will help to create a model that can be used to predict measuring values by using spectral data.
Thus HSI (Hyperspectral Imaging) camera systems can, with the right know-how, be used to monitor the production of clinkers in cement plants. In this process, secondary fuels will be used to fire kilns. If the net calorific value of these secondary fuels is too low, there will be a critical drop in temperature: Therefore, the quality of the clinker will be reduced.
Up to now, specimens of these secondary fuels have only been studied by way of sampling and laboratory analysis. Yet as the material stream is so heterogeneous, and the amounts of specimens are too low, this does not lead to expressive results. Moreover, it should be borne in mind that such laboratory measurements cannot be done in real time and therefore highly depend on local fluctuations.
Thanks to “Quantitative Chemical Imaging” (QCI), the process variables relevant to the production of cement, such as humidity or the net and gross calorific values, can be acquired quantitatively. The product stream as a whole can be monitored in real time.
Therefore, the measurement won’t be subject to any fluctuation due to insufficient sampling and re-adjustment can be done without delay.
For such a quantitative analysis based on HSI (hyperspectral imaging), several specimens out of the product stream will first be recorded and studied by way of reference analytics.
Then the spectral image data will be evaluated by using SQALAR. The result of such a quantitative measurement of the net calorific value can be seen in Figure 2. The correlation function of the reference values with the spectral data, which is shown here, as well as the resulting feature curve will be used to determine the net calorific value. In the camera stream subjected to final classification, the initial image data will be scaled on the basis of the measuring variables and shown in false colours. This enables quantification of the process variables.