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Abstract

Gasification technology is often seen as a synonym for the clean and efficient processing of solid fuels into combustible gas containing mainly carbon monoxide and hydrogen, the two basic components of synthesis gas. First and foremost, the facts that gas may be cleaned and that a mixture with any composition may be prepared in a relatively easy and inexpensive manner influence the possibility of using gas produced in the energy and chemical industries. In the energy industry, gas may be used directly to generate heat and electricity in the systems of a steam power plant or in combined cycle systems. It is also possible to effectively separate CO2 from the system. However, in chemistry, synthesis gas may be used to produce hydrogen, methanol, synthetic gasolines, and other chemical products. The raw material for gasification is full-quality pulverized coal, but a possibility of processing low-quality sludges, combustible fractions separated from municipal waste as well as industrial waste also exists. Despite such a wide application of technology and undoubted advantages thereof, making investment decisions is still subject to high uncertainty. The paper presents the main technological applications of gasification and analyzes the economic effectiveness thereof. In this context, significant challanges for the industrial implementation of this technology are discussed
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Abstract

The research was aimed at examining the impact of the petrographic composition of coal from the Janina mine on the gasification process and petrographic composition of the resulting char. The coal was subjected to fluidized bed gasification at a temperature below 1000°C in oxygen and CO2 atmosphere. The rank of coal is borderline subbituminous to bituminous coal. The petrographic composition is as follows: macerals from the vitrinite (61.0% vol.); liptinite (4.8% vol.) and inertinite groups (29.0% vol.). The petrofactor in coal from the Janina deposit is 6.9. The high content of macerals of the inertinite group, which can be considered inert during the gasification, naturally affects the process. The content of non-reactive macerals is around 27% vol. The petrographic analysis of char was carried out based on the classification of International Committee for Coal and Organic Petrology. Both inertoid (34.7% vol.) and crassinetwork (25.1% vol.) have a dominant share in chars resulting from the above-mentioned process. In addition, the examined char contained 3.1% vol. of mineroids and 4.3% vol. of fusinoids and solids. The calculated aromaticity factor increases from 0.75 in coal to 0.98 in char. The carbon conversion is 30.3%. Approximately 40% vol. of the low porosity components in the residues after the gasification process indicate a low degree of carbon conversion. The ash content in coal amounted to 13.8% and increased to 24.10% in char. Based on the petrographic composition of the starting coal and the degree of conversion of macerals in the char, it can be stated that the coal from the Janina deposit is moderately suitable for the gasification process.
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Abstract

Based on data collected during an UCG pilot-scale experiment that took place during 2014 at Wieczorek mine, an active mine located in Upper Silesia (Poland), this research focuses on developing a dynamic fire risk prevention strategy addressing underground coal gasification processes (UCG) within active mines, preventing economic and physical losses derived from fires. To achieve this goal, the forecasting performance of two different kinds of artificial neural network models (generalized regression and multi-layer feedforward) are studied, in order to forecast the syngas temperature at the georeactor outlet with one hour of anticipation, thus giving enough time to UCG operators to adjust the amount and characteristics of the gasifying agents if necessary. The same model could be used to avoid undesired drops in the syngas temperature, as low temperature increases precipitation of contaminants reducing the inner diameter of the return pipeline. As a consequence the whole process of UGC might be stopped. Moreover, it could allow maintaining a high temperature that will lead to an increased efficiency, as UCG is a very exothermic process. Results of this research were compared with the ones obtained by means of Multivariate Adaptative Regression Splines (MARS), a non-parametric regression technique able to model non-linearities that cannot be adequately modelled using other regression methods. Syngas temperature forecast with one hour of anticipation at the georeactor outlet was achieved successfully, and conclusions clearly state that generalized regression neural networks (GRNN) achieve better forecasts than multi-layer feedforward networks (MLFN) and MARS models.
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